I

No.

"WHO VOTED FOR HITLER?" REVISITED:

A CLUSTER ANALYSIS OF THE BASES OF INCREASED NAZI

SUPPORT IN THE 1930 REICHSTAG ELECTION

Mark H. Levine

A DIsser+ation

Submitted to the Graduate School of Bowling Green State University in partial fulfillment of the requirements for the degree of

DOCTOR OF PHILOSOPHY

June 1976

JEROME UBRA/f-BOOiG GREbft STATE Mii'fVEKSTJt < ì W, ho,UT c^pCL

ABSTRACT

In The 1930 Reichstag election the Nazi Party (NSDAP) was transformed from one of Germany’s many minor political groupings into the Reich's second leading party. Since that time the many studies aimed at explaining the sources of this tremendous surge in NSDAP support have fai led to reach a con­ sensus of opinion.

Although virtually all analyses have des i gnated the ruraI-Protestant areas as major NSDAP strongho Ids, attempts to identify the 1930 Nazi voters in terms of their prior political behavior have been inconclusive, Researchers have alternatively attributed the growth in Nazi vote to one, or some combination of, three sources : defections from the many moderate and liberal middle-class parties, and the sudden influx of "new voters".

The argument presented in this study is that this confusion can largely be attributed to the existence of two methodological problems associated with the use of aggregate data and multivariate statistical techni- ques: the "ecological fallacy" and rnulticol linearity.

After reviewing various "solutions" to these proh­ lems, this study advances c1u ster analysis as an appro- priate means for minimizing i heir effect. Specificaily, the variable-, comparative-, and object-cIustering rou- tines of the BC TRY system of cluster analysis were used in conjunction with multiple regression analysis and tabular analysis.

The results of these s I a i i st i ca I and methodological procedures confirm the v i ew f n a f while Nazi gains in 1930 were substantial throughout I he Reich, the most signifi­ cant increases occurred in the ruraI-Protestant areas. Within these regions, the greatest increase in Nazi sup­ port can be attributed to the previous supporters of the conservative DNVP. The extent of Nationalist defections to the NSDAP was somewhat greater than that of the combined centrist parties with these two groupings accounting for Ihe bulk of the Nazi increase. All com­ ponents of the analysis uniformly lead to the rejection of the increased voter turnout as a key factor in the 1930 growth of the Nazis. Ill

"For I must not measure the speech of a statesman to his people by the impression it leaves in a university professor, but by the effect it exerts on the people."

-- Adolph Hitler Pagination Error V

TABLE OF CONTENTS

Page LIST OF TABLES ...... v i

LIST OF FIGURES ...... i X

LIST OF MAPS...... X

INTRODUCTION ...... I

THE REICHSTAG ELECTIONS: 1928 TO 1933 ...... 8

"WHO VOTED FOR HITLER?" 1928 TO 1933 ...... I 9

"WHO VOTED FOR HITLER?" 1928 To 1930 ...... 5 I

METHODOLOGICAL ISSUES ...... 69

Units of analysis and the ecological fallacy...... 70

Statistical procedures and multicollinearity ...... 91

Cluster analysis ...... I 02

THE DATA ANALYS IS...... 108

V-ty p ing HO

Units of analysis: Wahlkrei s and Kre i s V-type comparison...... 124

V-types and Nazi support: multiple regression of Wahlkreis and Kre i s variable clusters...... , . , 128

O-ty p ing ...... I 42

Wahlkre i s and Kre i s O-types...... 147

0-types and 1930 Nazi support...... 171

"WHO VOTED FOR HITLER? REVISITED: SOME ISSUES AND ANSWERS...... 195

REFERENCES 2 I 3 v i

LIST OF TABLES

Page 2. I Reichstag Elections: 1928-1933 ...... 9

3. I The Electoral Districts (Wahlkreise) .... 23

4. I Reich Election Totals and Change: 1928 and 1930...... 57

4.2 Types and Frequency of Findings...... 59

4.3 Findings by Disciplinary Affiliation of Researcher...... 60

5. I Models, Findings, and Statistics Employed by Researcher in the Analysis of the 1930 Reichstag Election ...... 72

5.2 A Iternat i ve Models of Aggregation Employed in the Analysis of Weimar EIecto raI Data...... 73

5.3 Wahlkreis by Wahlkreis Per Cent of Total Vote Received by Nazis 1928, 1930, and 1928-1930 Change ...... 75

5.4 Areas Employed as "Universe" .by Researchers Using Aggregation Model II . . . 77

5.5 Areas Employed as "Un i verse" by Researchers Using Aggregation Model IV.. 8 I

5.6 Aggregation Model Employed by Discipline . 82

5.7 Findings by Aggregation Model Employed . . 85

5.8 Method of Statistical Analysi s by Aggregation Model Employed . 94

5.9 Method of Statistical Analysis by Disciplinary Affil ation of Researcher . 96

5.10 Findings by Method of Statistical Analysis EmpIoyed 98

6. I Variables in Final Cluster Analysis . . I 4 V i

6.2 Expanded Cluster Structure (Wahlkreise) .... 116

6.3 Expanded Cluster Structure (Kreise) ...... 121

6. 4 Exß^nded Cluster Structure (Wahlkreis a ng Kre i s Comparison)...... 126

6. 5 Correlations Between Oblique Cluster Domains ( W a h I k r e i s e )...... 129

6.6 Correlations Between Oblique Cluster Domains (Kreise)...... I 30

6.7 Compa r i son of Wahlkreis and Kreis Regression Coefficients (Nazi Vote in 1930) I 36

6.8 Comparison of Wahlkreis and Kreis Regression Coefficients (Nazi Gain in 1930)...... I 37

6.9 Estimates of I n d i v i d ua I - I e ve I Regression Coefficients Ranges (Kre i s/WahIkre i s Overlap)...... 138

6. 10 Homogeneity of Kre i s 0-types (7 0-types). . . . 149

6.11 Means of Kreis on Variable Clusters ( 7 0-types )...... 150

6. 12 Standard Deviations of Kre i s 0-types ( 7 0-types )...... 153

6.13 Euclidean Distances Between Kreis 0-types (7 0-types )...... 154

6.14 Homogeneity of Wahlkreis 0-types ( I I 0 - types ) ...... 156

6.15 Means of W a hIk r e i s 0-types on Variable Clusters (I I 0-types)...... 157

6.16 Standard Deviations of Wahl kreis 0-types ( I I 0-types )...... 159

6.17 Euclidean Distances Between Wahlkreis 0-types ( I I 0-types ) ...... 160

6.18 Homogeneity of Wahlkreis 0-types (6 0-types ) ...... 16 1

6.19 Means of Wahlkreis 0-types on Variable Clusters (6 0-types)...... 162 V i i i

6.20 Standard Deviations of Wahlkreis 0-types ( 6 O-ty pes )...... 164

6.2 I Euclidean Distances Between WahIkre i s 0-types (6 0-types)...... 165

6.22 Summary of Types Derived from Object Clustering Solutions ...... 166

6.23 1930 Nazi Vote by 0-type CKj...... 173

6.24 1930 Nazi Vote by 0-type EWK( II ) 3...... 175

6.25 1930 Nazi Vote by 0-type CWK(6)]...... 176

6.26 1930 Nazi Gain by 0-type CK]...... 184

6.27 Mean Nazi Vote (Percentage of Total Vote) By Kre i s 0-types...... 186

6.28 1930 Nazi Gain by 0-type EWK(il)]...... 188

6.29 1930 Nazi Gain by 0-type EWK(6)]...... 190

F I x

LIST OF FIGURES

Page 3. I Hypo+hetical Model of Maximum Voter Shifts Between 1928 and July 1932: AI I SPD Losses to KPD...... 35

3.2 Hypothetical Model of Maximum Voter Shifts Between 1928 and July 1932: SPD Losses Split Between KPD and NSDAP...... 36

3. 3 Hypothetical Model of Maximum Voter Shifts Between 1928 and July 1932: All SPD Losses to NSDAP...... 37

6. I Plotted Means -of Kreis 0-types (7 O-types) . . 151

6.2 Plotted Means of Wahlkreis O-types ( I I O-types)...... 158

6.3 Plotted Means of Wahlkreis O-types (6 O-types ) ...... 163 y

LIST OF MAPS

Page 3.1 The Birth of the "Volkstaat"...... 24

3.2 The Reichstag Electoral Districts (Wahlkreise)...... 25

3.3 Religious Divisions of the Reich...... 26

7. I Wahlkreis 0-types ( I I 0-types)...... 204

7.2 Wahlkreis 0-types (6 0-types) ...... 205 (

CHAPTER ONE

Infroducti on

Since The mid-1930's, social researchers have been

intrigued by the growth and ultimate accession to power

of the Nazi Party (NSDAP) in the final years of Weimar

Germany. In the 1928 Reichstag elections, the Nazis cap­

tured a mere 2.6 per cent of the total votes cast; however,

by the elections of March 1933 the NSDAP had become the

ranking party of the Republic with nearly 44 per cent of

the returns being cast for the NSDAP. The Nazis' "legal

revolution", carried out predominantly within the bounds of Weimar's electoral arena, has directed considerable attention to identifying characteristics of those segments of German society that provided the electoral strength needed for the Nazi victory. Of particular interest to analysts has been the issue of locating the bases of in­ creased electoral strength of the NSDAP between the 1928 parliamentary elections and subsequent ballotings. The approach which has been most commonly employed has been the attempt to account for the rise of Nazi support in terms of increased voter turnout and defections from other politica parties.

Previous analyses of the broad period from 1928 to

1933 have generally reaffirmed the view that appears quite obvious from a cursory examination of the gross election returns for the entire Reich. Nazi gains during this period 2

spanning five elections have been found to statistically

covary with and be theoretically accounted for by a com­

bination of I) increased voter turnout, 2) support lost

by the Nationalist Party (DNVP), and 3) defections from

numerous moderate or liberal national and regional parties

which had strong support among the urban and rural middle

class. Additionally, it is generally agreed that the Nazis

met with more success in rural than urban districts.

Similarly, analysts are unanimous in their contention that

the Nazis obtained more support from Protestant than Catholic

voters.

Sti I I unresolved, however, is the issue of the sources

of Nazi support in the 1930 Reichstag election when the

Nazis rose to become Germany’s second-ranking party. While

agreeing that, as in the elections over the longer period,

the Nazis were more successful in rural and Protestant areas,

researchers have differed considerably in their conclusions

regarding the political sources of NSDAP gains. Alternatively, the growth of Nazi support in the 1930 contest has been attributed to one (or some combination) of the groups

identified as providing new NSDAP votes over the larger time period. That the issue has not yet been resolved is evidenced by its continued discussion and re-evaluation.

In one of the most recent works investigating the sources of Nazi electoral strength, Waldman (1973) identified a number of models of mass movements which have been applied to the rise of the Nazis. Three competing models parallel the discrepant interpretations of the 1930 Nazi gains. 3

Waldman suggested that those researchers emphasizing the

Nazi gains accrued from losses suffered by the centrist

middle-class parties are advancing a "correspondence model".

Theorists positing this view of movements, in general,

and the Nazis in particular, have stressed the notion that

social classes differ in the kinds of movements they sup­

port. Furthermore, they hold as a starting assumption

that there is a correspondence between a movement's ideology

and its base of support. Fascism, those advancing this model have additionally asserted, is predominantly an

ideology of the middIe-cI ass.

While the correspondence model is basically a class or status explanation of movement support, those research­ ers stressing the role of new voter turnout or defections

from the Nationalists in NSDAP success have been more con­ cerned with the impact of political variables in attracting voters to the Nazis.

Analysts who have concluded that the Nazis garnered primary backing from previous non-voters have implicitly or explicitly made use of the "Party Identification Model" of political support. The basic premise on which this model rests is that individuals with a weaker attachment to or shorter time association with parties will be more likely to support radical or extremist parties or movements. Pro­ ponents of the party identification model assert that demo­ cratic political structures are characterized by the 4

. . . "binding in" of popular loyalties to one or another of the traditionally competing political Parties. Where these loyalites have not yet had time to develop, it seems likely that electoral support wi I I have capricious overtones, and that in times of severe distress non traditiona I and anti-democratic parties may find ready support (Converse, 1969:141).

The second predominantly political model which Waldman

identified involves the primary role of the "Nationalist Issue".

Those investigators who have emphasized the defections to the

NSDAP from the DNVP have stressed the attraction of the Nazis'

nationalist orientation to former DNVP voters. Border areas

of the Reich which had lost territory to several European neigh­

bors following World War I had been Nationalist Party strongholds.

The general passivity of the older party in the face of these

losses, however, is al leged to have led many of its supporters

into the camp of the more militant Nazis. Additionally, it is

argued that the fires of nationalism were further fanned by

the large influx of foreign workers while many in Germany were

hit by unemployment. As Ke I e noted,

. . . the German government had admitted at least 125,000 foreign workers in 1929 and 109,000 in 1930. Most of these workers were Polish, and many were employed in agriculture. There are no reliable statistics on the number of unemployed German farm workers but the figure was high (1972:140)

Thus, the question of identifying the possible sources

of Nazi gains in the 1930 Reichstag election has resulted in

a number of inconsistent answers. Efforts to overcome these

inconsistencies have generally focused on the methodological and statistical procedures employed in previous analyses and 5

their inadequacies. The lack of clarity surrounding this issue

is largely the result of the fact that records of individual

voting patterns do not exist. Consequently, since an examination

of shifts in support by individual voters is not possible,

researchers have undertaken the analysis of electoral statistics

reported by geo-political units at various levels of aggregation.

For the most part, investigators have followed one of a I imited

number of aggregation formulae.

These alternative aggregation formulae are important because each defines the parameters of a study employing it in terms of the kinds of variables to be observed and the degree to which generalizations can be made from these observations. It is also necessary to understand that the adoption of one or another of these schemes has implications and limitations for the kinds of statistical procedures that can be employed and the utility of their application.

The first (Model I) in attempting to identify the political base of increased support for the Nationalists, had addressed

Germany as the "universe of territory" for which an analysis is undertaken. The electoral districts are defined as the observations from which general izations are drawn to the whole of Germany.

Each of the other models can, similarly, be understood as being directed at different universes (areas about which generalizations are made) and/or employing different:"basic analytical Cor areal] units" (observed units).

The second major formula (Model II) has utilized geo­ political units smaller than the Reich, i.e., states, regions, b

and most frequently Wahlkreise as the universe. Units at a

still greater level of disaggregation, such as towns, cities

and most commonly the Kre i se (counties or county-like admini­

strative districts), are employed as the basic areal units.

A third formula (Model III) offered in one of the most

recent analyses of this issue (Waldman, 1973) was similar to

the first in that the universe it addressed was the whole of

the Reich. Recognizing that larger units of analysis might

be insensitive to the marked differences in Nazi gains from county to county within a given electoral district (Noakes,

1971:153; Pridham, 1973:139) this model utilized these smaller aggregations as the basic analytical units.

A fourth design (Model IV) offered in the literature has employed one or several territorial units at various levels of aggregation as both the universe of study and the basic areal or analytical units. A common application of this model has seen the researcher's attention directed to the study of Nazi gains throughout the Reich. Rather than basing conclusions on the examination of several observations (basic analytical units), Model IV calls for all generalizations to be based on only one observation, the Reich itself.

Whi le the last of these approaches has the obvious weakness of being limited to the comparison of gross election returns for any statistical analysis, the first three run into at least two major difficulties when more powerful statistical procedures are undertaken. The first of these difficulties, 7

frequently encountered in the analysis of aggregate data,

is associated with the problem generally identified as the

"ecological fallacy". This refers to the problematic nature

of the assumption that variables which are found to be

correlated at one level of aggregation, i.e., WahIkrei se,

Kre i se, will be similarly related at other levels, i.e.,

individuals. The second difficulty, which often charac­

terizes multivariate analyses, is the problem of multi­

collinearity. Multicollinearity refers to the situation in

which a number of possible independent or causal variables

are highly intercorreIated. This most frequently results

in confusing interpretations about the precise relationship

between individual independent variables, i.e., per cent

vote for DNVP, per cent Protestant, etc., and the dependent

variable, i.e., per cent vote for NSDAP, per cent increase

in NSDAP vote.

Prior analyses of the sources of increased Nazi support

have, in most instances, tended to ignore the implications of these particular problems. In recognition of the fact that the confusion surrounding this election may be at least par­ tially attributable to these problems, the current study

has employed a cluster analytic technique aimed at minimizing these methodological difficulties. Applying this procedure to data at a variety of levels of geo-political and multi­ variate aggregation, a clearer picture of the sources of

Nazi support in the 1930 Reichstag election is possible. 8

CHAPTER TWO

The Reichstag Elections: 1928 to I 933

In the 1928 election, approximately 30.75 million votes

were cast Of these, a little more than 810 thousand went A to the NSDAP. CorrespondingIy, this vote won the Nazis a

total of 12 seats in the Reichstag.' The Nazi vote, which

represented a drop from the 6.6 per cent of the total votes

cast, received in December 1924, was hardly an omen for what

was to occur in subsequent Weimar ballotings.

The 1930 election for the Reichstag, held in September

of that year, followed the March dissolution of the legislative

body. This was two years earlier than was necessary under the

Weimar constitution. In this contest, the NSDAP secured nearly 6.5 million votes. This was sufficient to earn the

Nazis places for 107 Reichstag deputies. This 664 per cent

increase in NSDAP parliamentary representation was the most substantial single-election gain i. n Weimar history. Most

importantly, this sudden flux of votes for the Nazis made them the second strongest party, behind the Social Democrats.

In addition to such surprising NSDAP gains, this elec­ tion witnessed a number of other rather sharp voting changes.

The highly conservative and nationalist DNVP suffered a considerable loss of backing. While the number of-votes received by the Nationalists declined to a lesser degree Table 2.I

Reichstag Elections: 1928-1933

20 May 1928 I 4 September I 930 3I July I932 Number of Number of Number of Party Names and Initials votes 1° RS* votes % RS votes % RS

National Social ist (NSDAP) 809771 2.6 I2 6380465 I8.2 I 07 I 377901 7 37. I 230 Nationa1i st (DNVP) 4380029 I4. I 73 2457572 7.0 4I 21 86051 5.9 40 Social Democrat (SPD) 9151059 29.4 I 53 8575699 24.3 I 53 79597 I 2 2I .4 I 33 Communist (KPD) 3263354 I0.5 54 4590453 I3.0 -7 5282636 I4.2 89 German Democrat(DDP) 1504721 4.8 25 I 322028 3.8 20 373800 I .0 4 People's (DVP) 2678532 8.6 45 I5774II 4.5 30 436002 I .2 7 Economic (WP) 1402203 4.5 23 I 373839 3.9 23 I 48648 0.4 2 Center (Z) 3710747 I I .9 62 4127005 I I .7 68 4589430 I2.4 75 Bavarian People 's (BVP) 945306 3.0 I6 I0587II 3.0 I9 I I 92684 3.2 22 Others 3322192 I5. I 5I 373701 5 I4.5 72 I 362749 3.7 I I

Tota 1 31160067 I 00.0 491 35226657 I00.0 577 37 I 62081 I 00.0 608

Elîg i b Ie Voters 4 I 244733 42957762 44211216

Non-voters 10084666 24.6 7731105 18.0 7049135 15.9

*Number of Reichstag Seats Table 2.I (Cont.)

6 November 1932 5 March 1933 Number of Number of Party Names and Initials votes 0p/ RS votes RS

National Social ist (NSDAP) 11737395 32.8 196 17277328 43.6 288 Nationa1i st (DNVP) 3019115 8.4 51 3136979 8.0 52 Social Democrat (SPD) 7247901 20.3 121 7181273 18. 1 120 Communist (KPD) 5980289 16.7 100 4847939 12.2 81 German Democrat (DDP) 3336447 1 .0 2 334315 0.8 6 People's (DVP) 660889 1 .8 1 1 432255 1 . 1 4 Economic (WP) 107274 0.3 1 —No Candìi dates- Center (Z) 4230545 1 1 .8 70 4425000 1 1 .2 73 Bavarian People ' s (BVP) 1095938 3. 1 20 1073551 2.7 19 Others 1449790 4. 1 12 949670 2.4 4

Tota 1 35758259 100.3 584 39658310 100.0 647

El ig i b1e Voters 44374085 44664825

Non-voters 8615826 19.4 5006515 1 1 .2

o I I

the percentage of the total votes cast won by the DNVP was cut

in half (14.3 per cent in 1928 to 7.0 per cent in 1930).

Moderate increases in the number of votes received were

achieved by both of the Catholic parties (Z and BVP). The only

other party to gain support was the Communist Party. Whi Ie KPD

gains were greater than those of Z and BVP, its increase of

2.5 per cent of the share of the total vote received was far overshadowed by the Nazis' success.

Despite its loss of approximately 600,000 votes, the SPD

remained the Reich's leading party. Given the Weimar system of basing the total number of Reichstag seats on the total vote

for the election the total number of seats increased by more than 80 over the preceding election. This allowed the Socialists to retain their 153 seats,a I though their popular support and and relative pariiamentary representation declined.

Other parties, particularly the’ left- and right-I ibera I DDP and DVP, respectively, were hit rather hard by the 1930 elections.

The German Democrats dropped slightly under 200,000 votes, bdt since they had received only 1.5 million votes in 1928, this 2 was quite costly. The largest liberal bourgeois party, the

DVP, lost nearly half of its 1928 support and had its Reichstag representation cut by a third.. The Economic Party (WP), which claimed the majority of its support from small business interests, suffered a similar fate. From its 1928 figure of just under 1.4 million (4.5 per cent) the Economic Party lost approximately

30,000 votes and received backing from only 3.9 per cent of the electorate. The remaining regional and national parties, pre­ dominantly republican middle-class groupings, received moderate increased in support. I 2

The last significant point regarding this election

concerns the total number of el igible voters casting

ballots. In 1930 there was an increase of over four million

in the total number of votes. Since a maximum of 1.7 mil­

lion of these electors comprised the newly eligible voters,

the remaining, at least 2.3 mi I I ion, votes were cast by

individuals who, while previously eligible to vote, had not

participated in the 1928 ba I loting.^ The 82 per cent voter turnout had been surpassed in only one prior election in

Weimar's short history. This was during the 1919 vote for the National Constitutional Assembly when 83 per cent of the electorate turned out (Pollock, 1930:990).

In 1932, two Reichstag elections were held within a period of three months. The first of the elections, held

in July of that year, witnessed further substantial gains by the Nazis vaulting them into the position of leading party. The total number of votes, the percentage of total votes, and the number of Reichstag seats captured by the

National Social ists alI increased by more than a hundred­ fold.

While once again suffering losses at the polls, the

DNVP deci ine in July 1932 was less marked than in the pre­ vious elections. Its percentage of the total vote dropped from seven per cent to 5.9 per cent and the DNVP Reichstag membership fell by one seat.

The Social Democrats lost approximately 600,000 votes, I 3

a figure comparable to the decline in the preceding balloting

Of equal importance for the SPD was its loss of Reichstag

strength which placed the Social Democrats a distant second

behind the Nazis.

The heaviest tolls, however, were paid by many of the

Reich's remaining parties. The DDP, the DVP, the WP, and

other centrist parties saw their total Reichstag represen­

tation drop to only 22 seats, a loss of nearly 100 seats.

Similarly, while the combined support for these parties

reached a minimum of seven mi I I ion votes in both 1928 and

1930, the July 1932 election reduced them to a total of only

I.I million.

Gains made previously by the Cathol ics and the Communist

Party were held and slightly improved upon. The combined

Catholic vote grew by nearly half a million and together,

BVP and Z increased their share of the tota I vote by approximately one percentage point. The Communist Party was only minimally more successful than the Catholic bloc.

In July 1932 KPD improved upon its earl ier showing by cap­ turing less than 700,000 additional votes. This gave the

Communists over 14 per cent of the total vote, an increase of slightly over one per cent.

During this election, the number of eligible voters not participating once again decreased. While I.^million new voters cast ba I lots, this increase was considerably less than that exhibited in the 1930 electoral contest. It was I 4

sufficient, however, to push the participation rate to over

84 per cent, surpassing the record turnout figure set in the preceding election.

Following another Reichstag dissolution, the next elec­ tion was held in November 1932. This election was charac­ terized by a moderate reversal of many of the trends established in the two prior elections. While maintaining its majority status, the NSDAP vote fell by slightly over two million votes. Similarly the percentage of the total vote which was carried by the Nazis was reduced by more than four po i nts.

The Nationalist party improved upon its July returns by nearly a third to achieve a total of approximately three million votes. Similarly, DNVP Reichstag representation increased to 51 deputies.

Next to the DNVP, the most substantial gains in this contest were made by the German Communist Party. The KPD was the only party to consistently attract new supporters in each election between 1928 and November 1932. The nearly six mill ion votes and 100 Reichstag seats for KPD represented the party's best electoral showing throughout the history of the Weimar Republic.

The Cathol ic parties were the victims of a decrease in support that was rather negligible. While attracting slightly less support in November 1932 than in July of the same year, the November returns for Z. and BVP closely approximated the

1930 figures for these parties. Simi lar gains were made by the combined forces of the

middle-class centrist parties. Jointly they captured 2.4

million votes in November as compared to 2.1 million in July

Next to the more than eight mi I I ion votes received in the

1930 election, however, the November 1932 returns do not

demonstrate a re-establishment of the strength once held

by the liberals and moderates. The successes of these

parties were at least partially depressed by the losses

suffered by several of the parties within this category.

Whi Ie the right-I ibera I German People's Party increased

their support by nearly a third, slight losses were suf­

fered by the left-liberal German Democrats and the repre­

sentative of small business interests, the Economic Party.

Thus, the downward pattern of support for these latter

parties continued.

A final point of note was the shift in voter parti­

cipation rates. Unlike the other elections of the early

I 930’s, the November 1932 ba I loting was characterized by a

decrease in voter turnout, with 19.4 per cent of the elec­ torate failing to cast ballots. This was the highest non­ voting rate since the 1928 electoral contest.

By the time of the next Reichstag election in March

1933, NSDAP leader Hitler had already served as German Chan­ cel lor for approximately two months. This fact has caused some researchers to argue that the specter of Nazism and

Nazi terror severely I imits any comparison between this and earlier elections (Hunt, 1964:117). A review of the 1933 balloting, however, is valuable if only to demonstrate the I 6

stark differences between this and prior elections.

The 1933 elections brought the Nazi party unprecedented

success. The more than 17.25 million votes and 288 Reich­

stag seats made the NSDAP the top vote-getter in the life of the Second Reich.

The DNVP essentially maintained its November 1932 strength, capturing another 52 seats for the rightist majority.

Between the Nazis and the DNVP, the national ist right had achieved a clear majority for the first time.

The Social Democrats, argued by the NSDAP to be respon­ sible for Germany's humiliation after World War I and the economic collapse of the Reich, achieved their lowest repre­ sentation since the May 1924 election, before they first became the nation's strongest party. The other major working class party, the KPD, was similarly hurt. The

Communist Party felI to 12.3 per cent of the total vote, its first decrease in nearly a decade. In estimating the sig­ nificance of this vote for the two labor parties, Hunt argued that the election "took place in such a reign of terror that C their combined total of] 30.6 per cent is more a victory than a defeat" (1964:117).

Taken together, the Cathol ic parties managed to make slight gains, with the Center Party gaining nearly 200,000 votes while BVP support dropped by slightly more than 20,000.

Between these two parties, they similarly managed to increase their Reichstag representation by two seats. I 7

The I iberal and moderate parties were among the hardest

hit in, this election. While only a few thousand votes were

lost by the DDP, the loss by the DVP of more than a third of

its previous total brought it below its low showing in July

1932. The WP had previously been the victim of such dramatic

losses that in 1933 it failed to run any candidates for the

Reichstag (Lipset, 1930:138). Collectively these parties were left with only 14 of a total of 647 seats in the new

parliament. Their combined total vote also reached a new

low of only 1.8 million.

The voter turnout in the March 1933 election reached an unprecedented high with 88.8 per cent of the eligible electorate participating. In 1933 there were only 300,000 newly eligible voters, but the total number of votes cast exceeded the Reich's previous high set in November 1932 by nearly four million votes. I 8

NOTES

'Regarding The process by which Reichstag seats were apportioned, Pollock, in an article describing the Weimar Party system wrote the following:

A party, however large its total vote in the Reich, cannot secure a seat unless it has been able to elect a member in one of the thirty-five districts EWa hI - k re i se]. All surpluses from these districts are taken to the unions of districts, where seats are again awarded on the basis of one seat for each 60,000 votes. Final ly, any surpluses from the union of districts, or if the party has not seen fit to make unions of districts, then all surpluses from the districts are carried over to the national list, where again one seat is awarded for every 60,000 votes . . . parties secure representation in exact proportion to the vote cast (I929:873n). See also Pollock and Thomas (1952:105).

2|n 1930 the German Democrats joined with some smaI I splinter parties and changed its name to the German States Party (Deutsche Staatspartei ) . Since the DDP composed the dominant faction of this coalition, its name will be used throughout (Pollock, 1930:992). Weimar Constitution extended suffrage to a I I adults over the age of twenty (Urwin, 1974:116). I 9

CHAPTER THREE

Who Voted for Hitler? 1928 to 1933

Over the five Reichstag elections between May 1928

and March 1933, German electoral politics underwent a drastic

transformation. Since the period of these elections, students of Weimar electoral politics have attempted the task of

identifying the source or sources of the dramatic balloting shift to the Nazis. Lacking individual voter returns, historians, sociologists, and political scientists have attempted to identify characteristics of Nazi supporters by drawing inferences about Nazi voters from characteristics of areal units and their covariation with Nazi support.

Most commonly, investigators have focused on factors such as industrial distribution (Loomis and Beegle, 1946;

Pollock, 1944; Pridham, 1973; Waldman, 1973), size

(Lipset, I960; Lipset, 1970; Pollock, 1944; Heberle,

1970; Pridham, 1973; Waldman, 1973), ' sex breakdown

(Lipset, I960; Schoenbaum, 1966; Shively, 1972; Tingsten,

1963), religious distribution (Loomis and Beegle, 1946;

Pridham, 1973; Waldman, 1973), and rural/urban breakdown

(Eyck, 1963; Kornhauser, 1959: 207-208; Loomis and Beegle,

1946; Heberle, 1970; Orlow, 1969; Pollock, 1944; Pridham,

1973; Shively, 1972; Waldman, 1973) for a variety of levels of territorial or geo-pol it ical units. 20

In addition to these features, researchers have also

directed attention to the 1928 voting distribution in trying

to account for Nazi support in subsequent electoral contests.

Increased Nazi support following the 1928 election has been

viewed as being associated with such factors as increased

voter turnout and/or losses from other political parties

(Allen, 1965; Bendix, 1956; Eyck, 1963; Heberle, 1951;

Heberle, 1970; Heiden, 1936; Holborn, 1969; Hunt, 1964; Ke I e,

1972; Loomis and Beegle, 1946; McKenzie, 1971; Mellen, 1943;

Noakes, 1971; O'Lessker, 1968; Orlow, 1969; Pollock, 1944;

Pridham, 1973; Scheele, 1945; Schnaiberg, 1969; Schoenbaum,

1966; Shively, 1972; Waldman, 1973).

political parties have been uniformly rejected A as significant contributors to overall Nazi success because of the relative stability of their support. From the data offered above it is clear that few political parties were able to maintain their 1928 backing. Other than the Nazis, only the Communist Party and the Catholic (Center and

Bavarian People’s) parties can be counted in this category.

Each of these organizations not only held on to prior sup-' port, but at times managed si ight to moderate gains. Due to the massive increases in voter participation, the percentage of the total vote going to the Catholic parties, however, deci in ed .

The gains in support for these parties over that obtained

in 1928 elections has been taken as p r i ma facie evidence in support of the hypothesis that substantial contributions to the electoral success of the Nazis did not come from pre- 2 I

vious supporters of the Communist Party or the. Catholic

bloc.

Further support for this contention is readily avail­

able throughout the literature investigating the bases of

Nazi support. Waldman noted that "Since the earliest research

on the rise of the Nazis, all investigators have concurred

in the judgment that Protestants had a strong affinity for

the Nazis and CathoI icsa strong antipathy" (1973:78).

Loomis and Beegle investigated the relative strength

of the Nazis in the German states' of Schleswig-Holstein

and Hannover (both over 70 per cent Protestant) and the state

of Bavaria (over 70 per cent Catholic). Of the 35 electoral

districts (WahIkre i se) throughout Germany, those included

in the Bavarian state, i.eOberbayern-Schwaben, Niederbayern,

Franken, and Pfalz, were among the areas showing the lowest

Nazi gains in 1928-1933 rankings, 32, 33.5, 27, and 15

respectively (1960:139). The predominantly Protestant

Wah I kre i se of the states of Schleswig-Holstein (Schleswig-

Holstein) and Hannover (Osthannover and Sudhannover-Braun-

schweig) all ranked among the top eight districts in Nazi

gains throughout this period. The differential support for

the Nazis wasseen by Loomis and Beegle as being directly

attributable to the religious differences of these areas.

In an in-depth investigation of Nazi efforts at the

seizure of power in Bavaria ,historian Geoffrey Pridham

similarly argued that the strongest opposition to the NSDAP came from the Bavarian People's Party (1973:3). He noted, 22 however, that the power of the Catholic Church over its constituency was not impenetrable. In his analysis of

Germany's largest and most Catholic state, Pridham suggested that

The combination of regional loyalty and confes­ sional attachment provided a strong barrier to the expansion of Nazi support, but even this had its limits. Bavarian society was not entirely homo­ geneous .... Although the Nazis did not finally break the stranglehold of the BVP until they had acquired power at the national level, their support among Catholic voters was far from insignificant . . . The NSDAP generally won Catholic voters from parties other than the BVP (1973:321).

Outside of Bavaria, the limited shifting support by

Cathol ic party voters to the Nazis has simi I arIy been noted. In his history of the Nazi Party in the state of

Lower Saxony (Wahlkreise Weser-Ems, Osthannover, and Sud- hannover-Braunschweig), Noakes observed that NSDAP support was lowest in "districts in which there was a predominance or high proportion of Roman Catholics" (1971:154).

In Kreis Vechta in Oldenburg, for example, the NSDAP won only 2. I per cent compared with a Centre party vote of 81.7 per cent, while in Kre is Aschendorf- Hümmling in Osnabrück the NSDAP was 1.9 per cent compared with 80.6 for the Centre . . . The figures for Oldenburg and Osnabrück are particularly important in view of the fact that in terms of social and economic structure these areas were more or less identical with the NSDAP strongholds to the CProtestant] North (1971:154).

In turning to the relationship between the relative strength of the Catholi c parties and the Nazis throughout the Reich, researchers have similarly concluded that while not non-existant,Catho I ic defections to the NSDAP were con- siderably less significant than for other parties. Schnaiberg 73

TABLE 3.1.

The Electora1 Districts (Wahlkreise)

Wahlkreis Rank Rank Number of 1928-1933 1 928- 1 930 Count i es Nazi Gain Nazi Gain (Kre i se )

1 . Ostp reussen 4 4.5 42 2. Be r1 in 33 . 30 6 3. Potsdam 1 1 24 23 1 2 4. Potsdam 1 1 8 13.5 26 5. Frankfurt/Oder 5 4.5 3 1 6 . Pommern 2 3 35 7. Breslau 9 1 27 8. L i egn i tz 6 7 24 9. Oppeln 1 0 33.5 20 10. Magdeburg 1 2 11.5 25 1 I . Merseb urg 1 6 11.5 23 12. Thüringen 1 5 19.5 39 1 3. Sch 1esw i g-Ho1ste i n 1 2 24 14. Weser-Ems 2 1 2 1 36 15. Osthannover 3 1 0 3 1 1 6. Südhannover- Braunschwe ig 8 6 37 J7. Westfa1en-Nord 32 3 1 48 41 9. Hessen-Nassau 1 1 13.5 43 20. Kö 1 n-Aachen 35 25 20 2 1 . Koblenz-Trier 28 26 24 22. Düsse 1dorf-Ost 25 22 1 1 23. Düsse 1 dort-West 30 19.5 1 9 24. Oberbayern-Schwaben 27 32 64 25. Niederbayern 34 33.5 5 1 26. Franken 1 7 27 76 27. Pfalz 1 0 1 5 22 28. Dresden-Bautzen 20 24 1 8 29. Leipzig 22 29 1 0 30. Chemn i tz-Zw i ckau 7 8 20 3 1 . Württemberg 29 35 64 32. Baden 1 9 1 8 40 33. Hessen-Darmstadt 1 4 16.5 1 8 34 . Hamburg 23 16.5 70 35 . Meck 1 en b u rg 1 3 9 30

Total Kre i se 1052 2 4 25

Map The Reichstag Electoral Districts (WahIkrei se)

^Numbers areelec+ion district (Wahlkreis) numbers and refer to +he Wahlkreis enumeration in Table 3.1 2 6

,3>

V 27

Kele further suggested that

Gregor Strasser employed a slogan which suggested that the party promised all Germans "participation in property, participation in profits." In March, 1930, Otto Strasser boasted that his brother's slogan illustrated how a I I Nazis "drew the practical con­ clusions from our theoretical socialism." (Kele, 1972:141 -- emphasis in original) '

It is generally agreed, however, that despite their

propaganda efforts, the Nazis made few, if any, inroads into

the partisan allegiance of the Communist Party supporters.

A clearer understanding of the role of previous supporters

of the KPD can only be achieved by expanding the investiga­

tion to include an examination of the other major working-

class party, the SPD.

Unlike the parties previously considered, the Socialists

were less successful in retaining their 1928 strength through­

out the elections of the 1930's. Between the Reichstag elec­

tions of 1928 and the final elections of the Weimar Republic,

the SPD lost approximately two million votes. This was

more than 22 per cent of the 1928 Social Democratic total.

It was during this period, however, that the Communist Party

picked up more than 2 million supporters. Invariable it is

argued that while some Socialist defections may have swelled the Nazi total, the bulk of these voters turned to the 'KPD.

Rudolf Heberle examined the political ecology of

Schleswig-Holstein, the German state ranking first in Nazi gains, during the period under consideration. In his regional case study, Heberle pointed to the different locations of

NSDAP and SPD-KPD power. 28

(1969) noted that the total correlation between Center Party

support and support for the Nazis was -.05 in 1930, suggesting

that previous Center Party backers provided less support for

the Nazis than did backers of any other party.

More recently this conclusion was echoed by Waldman.

He found that all correlations between Nazi vote and Center

Party strength, computed between WahIkrei se and between Krei se

within each electoral district were negative (Waldman, 1973:86).

Examining this question from another direction, Waldman noted

that the correlation between the percentage of Protestants in

the population and the National Socialist vote in 1930 across

alI counties in the Reich was .58. Furthermore, when per cent

Protestant was correlated with Nazi vote gains between 1928 and

1930 (t - +|), the resulting r (.63) was even greater. These

findings led him to conclude that the clienteles of the Nazi

Party and the Center Party were quite different (Waldman, 1973:86)

Turning next to the Communist Party, although the bulk

of the evidence suggests the contrary, a few researchers have

indicated that KPD voters may have migrated to the ranks of the

Nazis (Scheele, 1945:148; Hunt, 1964:117; Schoenbaum, 1966:35).

In his analysis of the Nazi appeals to the German working class,

KeIe noted that Nazi efforts at gaining labor support were quite strong.

Even though nationalism remained the prime ingredi­ ent in Nazi propaganda during the period 1928-1930, the Nazis were careful to remind the workers that the NSDAP also advocated "socialism" in its program.; The Cleft- Nazi Otto and GregorU Strasser press in 1929 pointed to the provisions for profit-sharing and redistribution of property as proof that the NSDAP was a genuine socialist party (KeIe, 1972: I 4 I ). 29

The Nazis, while in The urban communities reaching only 44.8 per cent even in 1932, gained almost a two-thirds majority -- 63.8 per cent -- in the rural communities. From a comparison of the combined SPD and KPD vote in rural and urban areas with the combined non-Social- ist vote it appears that one major factor in the change of the political attitude of the rural popu­ lation must have been the absence of as large a body of class-conscious voters among the rural labor­ ers as was present in the cities (1970:94-95).

The most complete investigations of the role of previous backers of the proletarian parties in the electoral success of the Nazis have been offered by KeIe (1972) and Hunt (1964).

Hunt began his analysis by noting that the Socialist losses when viewed against the backdrop of Nazi gains has led to the suggestion that there was a massive transfering of sup­ port from the SPD to the NSDAP.

This is seen as a part of a broader pattern of working class backing for Hitler. By adding nationalism to socialism, it is argued the Nazis were able to outbid the Marxist parties for the allegiance of the German proletariat. The attractive simplicity of this thesis is deceptive for a closer statistical analysis betrays no such mass migration for socialist voters to Hitler's camp (Hunt, 1964:117).

The "closer statistical analysis" to which Hunt was referring involved, initially, an examination of the changes in the combined vote for both Marxist parties throughout this period. For the Reich as a whole, the combined vote for the

Socialists and Communists remained relatively constant. In

1928 these parties obtained approximately 40 per cent of the total vote while in the July, 1932, election they witnessed this figure drop by only four per cent. Recognizing that 30

gross election returns for the entire nation may not be

sensitive enough to uncover "hidden transfers" he undertook

the analysis of possible labor support for the Nazis within

several smaller areas (Hunt, 1964:120). Selecting electoral

districts on the basis of a high degree of economic^ and

religious homogeneity he addressed his attention to four

Wahlkreise: Köln-Aachen (Urban-CathoI ic ), Hamburg (Urban-

Protestant), Niederbayern (RuraI-Catho I ic ) , and Ostpreussen

(RuraI-Protestant). Examining descriptive statistics (per

cent vote for the Nazis and per cent combined working-class

vote) within each of these regions, Hunt noted the dissimi­

larity of NSDAP and KPD-SPD strongholds. Whi le both were

more successful in predominantly Protestant districts, the

Nazis' strength was greatest in rural Ostpreussen, while the

working-class parties had their greatest success and, when

losses occurred, greatest losses in urban Hamburg. Conse­

quently he argued that Social Democratic losses were largely

compensated for by Communist gains.

There were, of course, workers in the Nazi movement, including some who were formerly Social Democrats or Communists. What is important is that no mass migration occurred; thousands’of workers may have transferred their allegiance to the NSDAP, but not millions. The Nazis got their millions from another source (Hunt, 1964:117-118).

Quoting the work of German historian Heinrich Striefler, Hunt suggested that"the two socialist parties contributed only three of every one hundred votes the Nazis won between 1928 and July I 932" (1964:117). 3 I

In a more recent analysis of this question, KeIe

undertook an examination of the pattern of vote fluctuations

for SPD and KPD for each of the Reich's electoral districts.

In twelve of the thirty-five Wa hIkre i se (0stpreussen, Potsdam I,

Frankfurt/Oder, Magdeburg, Merseburg, Weser-Ems, Osthannover,

Westfalen S'ud, Pfalz, Chemn i tz-Zw i c ka u , Hamburg, and Mecklen-

berg) the decline in the combined share of the vote received

by the two leftist parties fluctuated between * and 3.9 points. -A In Hessen-Nassau and S’udhannover-Braunschweig the decrease in combined support was over- four points and in Breslau this

figure exceeded five percentage points. In KeIe's estimation,

"In these 15 districts it would appear that the NSDAP . . . may have made some limited gains at the expense of the SPD"

(1972:165 ). He added, however, that given the comp I exity■of the elections which witnessed changes in the backing for virtually all parties, as well as fluctuations in voter participation, "there appears to be no way to analyze the scattered number of votes that the Nazis may have received from former SPD voters (Kele, 1972:165).

One contribution to the investigation of these com­ plexities was offered by Waldman in his examination of the relationship between union membership and Nazi strength.

Upon finding a positive correlation between these variables in Germany's 42 largest cities (all over 100,000 population), he suggested that"if the correlations are to be believed, we would have to conclude that members of the socialist trade 32

unions voted for the Nazis rather than against them" (Wald­

man, 1973:190). Recognizing the possibility that this

apparent relationship may be the result of a strong associa­

tion between each of these variables and some third variable,

Waldman .examined the effects of the introduction of numerous

control variables on this correlation. As was previously

suggested, the Communists, the Socialists, and the National

Socialists all found their greatest strength in predominantly

Protestant regions. When the effects of religion (per cent

Protestant) were controlled for, the correlation was reduced.

Contrary to his expectations, however, the correlation re­

mained positive. This curious finding was partially clarified

with the discovery that membership in socialist labor unions

was moderately negatively related to the percentage of workers

in the labor force. Waldman observed that socialist union

membership was high in cities in which the proportions of

self-employed (r =.4 9 5 ) and white-collar workers was high

(r=.5O6). Making reference to patterns of correlations between

several variables/ Waldman reported that

Not only was trade union membership related to Protestantism which itself was positively related to the Nazi vote, but it was positively related to occupational variables which correlated with occupational variables which were negatively correlated with the Nazi vote (Waldman, 1973:196).

At least in part, the difficulty in estimating the precise nature or size of the SPD contribution to Nazi gains

is due to the lack of understanding of the Socialist contributions 5 5

to the KPD. A clear picture of the role of previous Socialist

Party backers in aiding either of these parties is confounded

by discrepant views regarding Socialist support for the other.

It was noted above that in addition to numerous shifts

in the support for specific political parties, the final

elections of the Weimar period were characterized by a

tremendous increase in overaI I voter participation. The

1933 elections brought out nearly 8.5 mill ion more voters

than did those of 1928. This was more than a 27 per cent

increase in the total number of votes cast. Given the

fact that the total number of individuals eligible to parti­

cipate in the elections increased by less than 3.5 million

between 1928 and 1933, the final Weimar elections stimulated a minimum of five million previously eligible non-partici­ pants to take part in the parliamentary contests.

Given the hypothesis that the bulk of this increase was divided between the Nazis and the Communists, disagree­ ment regarding just how much of this new turnout was won by each of these parties has limited the understanding of gains accrued from other sources such as the prior supporters of the Socialists. Working with the gross changes in Socialist vote, Communist vote, Nazi vote, and voter turnout through the Reich, a number of hypothetical models of voter shift can serve to illustrate the complexity of interpretations.

If it is assumed, for example, that the KPD gained the 5 support of all previous backers of the SPD, the maximum 34

number of new voters that could have supported the Communists

is only .75 million (see Figure3.!). As the number of new voters who turned to the Communists is increased, the amount of potential SPD shift to the Nazis is similarly increased while the maximum possible new voter support for the Nazis declines (see Figures^.2 and3-3). If the patterns of support among the new voters during this period are unclear, any further understanding of the destinations of SPD defectors must remain similarly unclear.

This introduces one segment of the electorate which has generally been viewed as providing substantial support for the

Nazis: the new voters (Heiden, 1935; Mellen, 1943; Scheel,

1945; Loomis and Beegle, 1946; Bendix, 1956; Lipset, I960;

Eyck, 1963; Pridham, 1963; Allen, 1965; Schoenbaum, 1966;

O'Lessker, 1968; Holborn, 1969; Schnaiberg, 1969; McKenzie,

1971; Noakes, 1971; KeIe, 1972). Evidence has been found in virtually all portions of the Reich which supports this view. For the Reich as a whole it must be recognized that between 1928 and 1933 the combined losses of all parties suf­ fering attrition was approximately 10.5 million votes. During this same period, however, the Nazis gained nearly 16.5 million votes whi le the KPD and the Cathol ic parties picked up nearly

2.5 million votes. Thus various analysts have argued that even if it is assumed that all defectors from other parties shifted their allegiance to the Nazis, approximateIy 5.9 mi I I ion new Nazi backers are unaccounted for. 35

HYPOTHETICAL MODEL OF MAXIMUM VOTER SHIFTS BETWEEN 1928 AND JULY 1932: ALL SPD LOSSES TO KPD * (In Millions of Votes)

Figure 3.1

e F ig u res 3.1 through 3.3 are not to be seen as identifying the sources of the increased Nazi support in the July 1932 election, Rather, they are offered to demonstrate the complex interrelationship between voter shifts for these four groups in Weimar's multi-party system. 36

HYPOTHETICAL MODEL OF MAXIMUM VOTER SHIFTS BETWEEN 1928 AND JULY 1932: SPD LOSSES SPLIT BETWEEN KPD AND NSDAP (In Millions of Votes)

Figure 3.2 37

HYPOTHETICAL MODEL OF MAXIMUM VOTER SHIFTS BETWEEN 1928 AND JULY 1932: ALL SPD LOSSES TO NSDAP (In Millions of Votes)

Figure 3.3 58

unaccounted for. This, support has most frequently been

attributed to the new Reichstag voters.

Among the clearest statements advancing this perspective

was that by sociologist Reinhard Bendix. Bendix suggested

that the "radica I ization of the electorate" which accounted

for many gains of both the Communists and the National Soda I -

ists "originated among the previous nonparticipants in

party politics''.^

( I 963:605) .

In a more recent historical analysis of the source of

Nazi gains Schoenbaum arrived at the same conclusion. He

noted that the two groups comprising the new voters were

,v responsible for the NSDAP successes. Schoenbaum argued

the ranks of the Nazis were increased "by the growth of new

voters in a number of districts and of previous non-voters"

( I 966 : 35 ) .

The role of the two components of the new voters in

aiding Nazi victories has also been advanced in the work of

Godfrey Scheele. Scheele was also quite clear in presenting

his conception of the impact of new voters on the electoral outcome when he suggested that

The National Socialist Party achieved its electoral support firstly as a residuary legatee of new gener­ ations of voters, and finally as the principal bene­ ficiary of the increase ... in the proportion of the electorate which voted (Scheele, 1945:148).

In noting the voter shifts and non-voting decline between November 1932 and the 1933 elections, Lipset simi­ 39

larly argued that the Nazis acquired some added strength

from the prior non-participants.

• Non-voting dropped from 19 per cent in 1932 to I I per cent in 1933, a drop of 8 percentage points, whi le the Nazi vote increased from 33 per cent to 43 per cent. And if we again correlate the growth in the Nazi vote with the increase in the electorate, we find . . . that the two trends show a high posi­ tive relationship (.6) (Lipset, 1960:150).

Recognizing that further information regarding this rela­ tionship might be obtainable through a more careful examina­ tion of smaIler sections of the Reich, Lipset offered a

Wa hIk re i s by Wa hIk re i s comparison of the association between these variables. He noted that in twenty-eight of Germany’s thirty-five WahIkrei se, the Nazi gains and turnout increases followed a similar pattern of highs and lows. That is, the Nazi increase was above or below the national average gain in the same districts that exhibited turnout increases which were higher or lower than the mean, respectively. On the basis of his examination of electoral statistics for both the Reich and Germany's electoral districts, Lipset concluded that "As a mass authoritarian party whose leader was already chancellor, the Nazi party received additional support . . . from the ranks of the anti-po I iticaI apathetics"

( I 969 : I 5 I ) .

The same argument has been advanced by researchers focusing their attention on smaIler regions and communities.

In his analysis of the political activity within the state of Bavaria, Pridham cited the key role played by the new 40

German voters in the eventual Nazi victory. Noting that the July 1932 elections involved an increased voter turnout of nearly 300,000 over the previous election, Pridham sug­ gested that the majority of these new voters swelled the

Nazi vote count.

This was very evident in South Bavaria. A study of the seven towns and ten rural districts in Upper Bavaria and Swabia, where the NSDAP became the strongest party in this election, shows that new voters were the primary source of the increase in support for the NSDAP . . . (Pridham, 1973:283).

In addition to attracting large portions of the new voters in the predominantly Catholic sections of Niederbayern,

Pridham further concluded that the NS'DAP made significant. inroads into this section of the electorate in the more

Protestant Bavarian district of Franken.

Among the most notable investigations of the Nazi rise to power at the local level is the work of William Allen.

In hi$ analysis of the pseudonymc s town of "Thalburg" he found that across the elections of the early I 930's, in each election a minimum of three-fourths of the "new votes" went to the Nazis. Furthermore, he asserted that in the

July 1932 balloting all of Thalburg's increased vote was cast on behalf of the NSDAP (Allen, 1965:129).

While not necessarily indicative of the sources of electoral support for the Nazis, additional evidence often offered in support of this hypothesis has involved ¡'in«/-;

m hfre.\

Abel (1966:314) analyzed 600 autobiographies (biograms) obtained from followers of the Hitler movement during the

party's ascendancy. Of his sample, 407 (68 per cent) NSDAP members had not previously maintained membership in any political parties prior to joining the Nazis.

The second generally agreed-upon source of substantial increase. contributions to the g.nowt-h of Hitler's National Socialist was the previous supporters of the German NationaI People's

Party (DNVP). The DNVP was, as Lipset noted, "both the most conservative and the most nationalist pre-Nazi opponent of the Versailles Treaty" (1960:140). This common ideological bond coupled with heavy DNVP losses in the face of NSDAP gains has led to the argument that the rise in Nazi strength resulted, at least in part, from a radicaI ization of sup­ porters of the rightist Nationalist Party (Eyck, 1963;

Holborn, 1969; Hunt, 1964; Loomis and Beegle, 1946; McKenzie,

1967; Mellen, 1943; Noakes, 1971; O'Lessker, 1968; Pridham,

1973; Schnaiberg, 1969; Schoenbaum, 1966; Allen, 1965; Kele,

1972; Heiden, 1935; Bendix, 1956; Waldman, 1973).

Over the elections of the final years of the Second

Reich, the Nationalist Party's support declined by over 1.2 mi I I ion votes. This represented a loss of over one-quarter of its 1928 electoral strength. Across the period^of these elections, this factor in conjunction with.other groups has A been viewed as allowing for the maximization of NSDAP gains.

Offering figures on gross ba I loting changes throughout 42

the Reich, Mellen (1943:620) argued that the Nazis not only from the. *“ k ‘Yy gained support^but were simi larly aided by the losses suffered

by the DNVP. Mellen pointed to the November 1932 balloting

as further evidence in support of the hypothesis that the

pattern of association between Nazi gains and DNVP losses

was a complementary one. As was suggested above, the Novem­

ber 1932 Reichstag contest signaled a temporary reversal of

trends suggested in the preceding elections. Unlike the

other elections of this period, this voting witnessed a

decline in Nazi support and total voter turnout, as well

as a substantial resurgence by the Nationalists who increased

their showing from the July 1932 balloting by nearly one

m i I I i on votes .

Other researchers have, similarly, pointed to the

inverse patterns of Nazi gains/losses and DNVP gains/losses

in arguing that the Nazis benefited from Nationalist Party

setbacks. Hans Holborn pointed to the significance of the

November 1932 election in which, he argued, "The chief win­

ner of the deci ine of the Nazi vote was the German National ist

Party" (1969:701). Increased DNVP gains during this election

were viewed by Holborn as reflecting Nazi losses. These

voters were seen as comprising only a fraction of the’votes

that the DNVP had previously lost to the Nazis.

Unlike these historical accounts which have based their

statistical analyses on gross election returns for the whole of the Reich, other investigations have employed different 4 3

research strategies. These have included The use of more

powerful statistical procedures and/or the examination of

the relationship between Nazi gains and DNVP losses within

various regions or districts within Germany. The work of

Lipset, for example, involved each of these tactics. Re­

porting that for the 35 Wahlkreise the rank-order correla-

tion between the proportion ate Nationa Social i s t gains and

Nationalist losses was .25, he argued that in some areas the

Nazis were the beneficiaries of losses suffered by the

DNVP. He suggested that

The largest drop-off in the conservative vote lay mainly in the election districts on the eastern border of Germany. The proportion of the vote obtained by the German National People's party declined by 50 per cent or more between 1928 and 1932 in ten of the thirty-five election districts in Germany. Seven of these ten were border areas, including every region which fronted on the Polish corridor, and Schleswig-Holstein, fronting on the norther border . . . these data suggest that the Nazis most severly weakened the conservatives in those areas where nation­ alism was their greatest source of strength (Lipset, I 960: I 40).

As was stated above, the Nazis' advances were particularly slow within the areas of the Reich inhabited largely by

Catholics. Within Protestant regions, it was suggested that the Nazis' gains in rural districts exceeded that in urban areas. That increased backing which was acquired in Catholic regions, however, fol lowed the opposite pattern, with the

Nazis receiving greater increases in support in urban-

Catholic, rather than ruraI-CathoI ic areas (Waldman, 1973:

122).Pridham, pointing to differential Nazi support in 44

urban and rural areas within the largely Catholic state

of Bavaria, attributed the higher rates of Nazi successes

in towns and certain EProtestantU rural areas to the

greater support (and greater losses) of the DNVP in these

areas.

Hunt offered a similar conclusion in his previously-

cited comparative investigation of several Wa hIkre i se.

Making particular reference to the variation in Nazi success

between Catholic (Niederbayern) and Protestant (Ostpreussen )

rural districts, Hunt argued that unlike the Catholic

parties, the DNVP (stronger in Ostpreussen than in Nieder-

bayern) were unable to fight off "significant encroach­

ments by the Nazis" (1964:121-122).

The final segment of the German electorate which re­

searchers have uniformly identified as a major contributor to the Nazi successes throughout this period was that block of supporters previously allied with the Reich's many liberal and moderate bourgeoise parties. During this period, the combined support for these parties felI from nearly nine million votes to less than 2.5 million, retaining less than

25 per cent of their 1928 total. Nazi success, it is agreed, was closely tied to the thinning ranks of the German Demo­ cratic Party (DDP), the German People's Party (DVP), the

Economic Party (WP), the Landvolkspartei, and numerous smaller "splinter parties" (Eyck, I 963;Heber Ie, 1951; Heberle,

1970; Holborn, 1969; Hunt, 1964; Lipset, I960; Lipset, 1970;

Loomis and Beegle, 1946; Mellen, 1943; Noakes, 1971; O'Lessker, 4 5

1968; Orlow, 1969; Pridham, 1973; Scheele, 1945; Schnaiberg,

1969; Schoenbaum, 1966; Allen, 1965; KeIe, 1972; Heiden,

1935; Bendix, 1956).

In advancing this argument, Lipset suggested that

The idea I-typica I Nazi voter in 1932 was a middle- class self-employed Protestant who lived either on a farm or in a smaI I community, and who had pre­ viously voted for a centrist or regionalist political pa rty ( I 960: I 48 ) .

Similarly, Bracher saw the phenomenal Nazi growth as the

result of a "panic of the middle class" which occurred with the onset of the Reich's economic crisis (1970:157).

Lipset based his characterization on the fact that while the Nazis were undergoing their tremendous electoral surge, the moderate and liberal bourgeois parties, supported primarily by small business and white collar workers virtu­ al ly suffered total col lapse. Between 1928 and 1932 the vote received by these parties felI by nearly 80 per cent.

At the same time their proportion of the total vote de­ ci ined from a quarter to less than three per cent. Citing a rank-order correlation between the proportion of Nazi gain with the loss of the middle-class parties of .48,

Lipset contended that while over the whole of this period the NSDAP acquired the backing of various sources of the

German voters, they gained most from the "liberal middle- class parties, the former bulwarks of the Weimar Republ ic"

( I 960 : I 38) .

Loomis and Beegle offered a comparable description of 4 6

the new Nazi voters in the Protestant states of Hannover and

Schleswig-Holstein. Their investigation involving only communities with less than 10,000 inhabitants pointed to a strong association between the level of smalI business and

farm ownership and Nazi vote (Loomis and Beegle, 1946:730-

731).

In his in-depth analysis of Nazi vote in Schleswig-

Holstein, Heberle provided additional evidence in support of this hypothesis. Suggesting that ". . . E + he data] show clearly the association of Nazi strength with the middle classes", Heberle identified small farm proprietors, the rural lower middle class, as being particularly susceptible to the NSDAP appeals (1970:112-117).

Similar conclusions have been offered with respect to Nazi gains in smaller communities. Suggesting that the middle-class was willing to support the Nazis in an effort at defeating the Socialist Party, Allen noted that "Thalburg had an increasingly strong petite bourgeoisie: the raw material from which Hitler forged his movement" (1965:15).

In summary, a number of generalizations about the sources of Nazi backing between 1928 and 1933 can be gleaned from the relevant Iiterature. Throughout the elections of this period, researchers have been virtual ly unanimous in their view that considerable evidence exists in support of all of the following hypotheses:

In terms of religious identification, the Nazis were much less successful in attracting Catholic 4 /

voters than in gaining the support of Protestants. Similarly, the Nazis met with considerably more success in predominantly Protestant than in Catholic states, electoral districs, and counties.

2. In Protestant areas (regions with less Catholic party backing), the Nazis made their greatest gains in rural rather than urban regions. Those gains made in districts with high Catholic party affiliation were more extensive in urban rather than rural areas.

3 Few inroads were made into the ranks of the work- ing class parties. The Communist party wa s particularly effective in resisting the advance of the Nazis. The Socialist Party lost some support to the Nazis but the number of SPD defec- tions to the NSDAP was far overshadowed by support from other sources.

4. The Nazis benefited from the increased participa­ tion by newly eligible voters and previous non­ voters. Whi le some new voters were attracted to the KPD, the bulk of this group was won over by the NSDAP.

5. The Nazis' strong nationalist orientation provided an attractive alternative for many voters who had previously backed the DNVP. This was particularly true in border areas ■ which had been forced to cede land in the period following Germany's defeat in World War I.68 *

6. The Nazis' strength grew as a result of the massive defections from Germany's many national and regional parties which had been backed by the Reich's middle class. Previous supporters of the German Democratic Party, the German People's Party, the Economic Party and other moderate, liberal, or centrist parties who turned to the Nazis as their economic security worsened with the onset of the depression in the early I 9 30's.

Kele (19 72: I 6 3n ) recently noted that since World War

I I students of the Nazis' rise to power have argued that Ger­ man voting trends should be studied through the period between zAr vC.?

1928 and 1933. Given that the influx of new voters , which frequently made the understanding of the sources of Nazi support more confusing, was most significant during the

1930 election, it is frequently treated as "atypical".

KeIe further suggested that this type of approach casts little light on the 1930 election in which the Nazis be­ came the-Reich's second strongest political party. An examination of the literature addressed to this election confirms KeIe's observation that this crucial step on the

Nazis' road to power is little understood. 49

NOTES

The Weimar Constitution gave the German Lander (states ) the right to join any other Land to dismember itself or re­ arrange state boundaries. This yielded the 17 individual states of Preussen, Bavaria, Saxony, Wurtemburg, Baden, Hesse, Thüringen, Hamburg, Mecklenburg-Schwerin, Oldenburg, Braunschweig, Anhalt, Bremen, Lippe, Lubeck, Mecklenburg- Strelitz, Schaumburg-Lippe (Lowenstein, 1940:347). 2 Unlike Loomis and Beegle's inclusion of four electoral districts in their analysis of Bavaria, Pridham deleted Wahlkreis Pfalz from his investigation. He justified this alternative definition of Bavaria by suggesting that

Until she became a kingdom in 1806, Bavaria had consisted only of the areas of Upper COber] and Lower CNieder] and the Upper Palatinate later known as 'Old Bavaria' (Altbayern). These areas were almost totally Catholic. As a result of her alliance with Napoleon, Bavaria acquired much new terri tory including Franconia and Eastern Swabia. Middle and Upper Franconia were predominantly Protestant, while Lowe r Franconia and (Bavarian) Swabia were largely CathoI ic. Bavaria later received additional terri- tory on the left bank of the Rhine (the Bavarian Palatinate) which remained part of the state until the Th r d Reich, but for the purposes of this study 'Bavaria i refers to the state of Bavaria p rope r (Pr i dham, I 973:3).

Thus the Bavarian Palatinate (Wa hIk re i s Pfalz), which was not part of "the state of Bavaria proper" was excluded from Pridham's analysis.

^Rural/urban definitions were based on the percentage of the regions' population dependent (Berufszugehoerige) on agriculture and the percentage of the total population inhabiting small communities (less than 2,000) (Heberle, 1970 : I I 5n ) .

^This somewhat confusing circumstance is typical of the problems in interpretation often encountered in the use of aggregate, areal, or ecological data. This diffi­ culty is compounded by the associated problem of multi­ colinearity, that is, high correlations between numerous independent or explanatory variables. These problems are discussed more fully in Chapter Five.

In I ight of the aforementioned excess of SPD losses 3 0

to KPD gains when Wahlkreise or Kre i se rather than Reich returns are examined, it must be recognized that at least a minimum of SPD defectors were available for the Nazis to attract, KeIe set this minimum figure for leftist transfers to the Nazis in seven of the Reich's districts (Berlin, Thüringen, Breslau, Sudhannover-Braunschweig, Chemnitz-Zwickau, Frankfurt/Oder, and Magdeburg) at 189,000 votes. He further noted, however, that any further clari­ fication of this issue is clouded by the "new voter" factor (Kele, 1972:208).

Waldman (1973:122) identified a continuum of Nazi growth with the NSDAP making the greatest gains in rural- Protestant areas fol lowed by urban-Protestant, urban- Catholic, and ruraI-Catho I ic regions.

7 I n each of the elections throughout the period under consideration, the parties running candidates for Reichstag seats minimally numbered in the twenties. The majority of these parties, both national and regional, wou Id be i ne I uded in this category.

°0ne consequence of the cessation of land was the physical separation of Ostpreussen, one area in which the Nazis experienced tremendous growth, from the rest of the Re i ch (Urw in, 1974:110).

Land Ceded By To

Prussian Provinces of Posen New Polish state and West Prussia and part of Upper Silesia

North Schleswig Denmark

Districts of Eupen and Belgium Ma Imedy

Re-ceded Alsace-Lorraine France 5 I

CHAPTER FOUR

Who Voted for Hitler? 1928-1930

The 1930 Reichstag election is worthy of attention

for a number of reasons. Among the numerous circumstances

surrounding this election that have drawn the attention of

researchers are the Nazis' surge in local and

national politics, the accompanying restructuring of Nazi

organization and redirection of strategy, the onset of the

depression during this period, and the lack of conclusive

explanations regarding the sources of Nazi support.

First, as has previously been suggested, the Nazis'

increased support represented the largest increase in par­

liamentary representation ever experienced by any party

during the history of the Weimar Republic (Pollock, 1930:

993). With the establishment of the Nazis as the Reich's

second strongest party, "It underlined the transformation of a former fringe party into a mass movement in terms of voting support" (Pridham, 1973:145).

Second, it must be recognized that while the extent of

Nazi growth may have been greater than even the Nazis had anticipated, the increase in support for the NSDAP in the

September parliamentary contest was not an isolated occur- ance without precedent. Throughout 1929 and 1930 the Nazis 2

met with considerable success in attracting voters in numerous state and local elections. In state elections the

National Socialists registered impressive gains in Saxony,

Thüringen, Mecklenburg, Baden, Liibeck, and Braunschweig

(Bracher, 1970:167). In their first significant electoral victory the Nazis captured 11.3 per cent of the vote in the

December 8, 1929, provincial election in Thüringen (Abel,

1966:92). This marked a substantial increase from the 4.6 per cent of the vote which the NSDAP received in 1927 and provided the Nazis with sufficient strength to become part of a coalition government (Bracher, 1970:167).

Similar successes were also apparent in numerous municipal elections throughout this period. On June 23,

1929, the -Nazis obtained an absolute majority of votes for the first time in the city of Koburg (Abel, 1966:308). In local elections in Saxony, the NSDAP got five per cent of the vote on May 18, 1929. During June of the foI lowing year, however, the Nazis had increased their share of the vote to over 14 per cent of the total (Bracher, 1970:167; Pridham,

1973:134). Comparable gains were witnessed throughout

Ge rma n y. •

Third, 1930 was an important year in that it marked the complete arrival of the depression throughout the Reich.

In his analysis of German Bus i n e s s Cycles, economist Carl QTTs' Schmidt argued that by 1929 the agricultural regions of

Germany witnessed the onset of the depression. This preceded J J

by one year the collapse of the German economy in urban and

industrial areas* With regard to the rela­ tionship between these economic trends and Nazi Party growth,

Zeman noted that until 1931, "The sharp increase in Nazi membership followed the growth of the numbers of the unem­ ployed" (1973:28).

Fourth, 1930 brought forth the first substantial support tor the Nazis by portions of the big business community. As was noted by Kornhauser, in its early period the National

Socialist movement obtained relatively little support from big business (cf. Zeman, 1973:28). Following the depres­ sion, however, the Nazis were able to pick up considerable aid from this source during the NSDAP's period of rapid g rowth .

By 1930 . . . faced with great pressures on their economic position and confronted with a powerful Nazi movement, portions of big business did give considerable financial backing to Hitler. What is of specific note ... is the apparent fact that this support did not emanate from big business as a whole, but from its newer sections . . . those large contributions which were forthcoming by 1930, and which played a crucial role in the following years, came particularly from leaders of heavy industry, especially steel (Kornhauser, 1959:199).

Fifth, the 1930 electoral contest was the first major test of the newly reorganized National Socialist Party. Be- ginning in 1928, the Nazis undertook a major restructuring of the party and a redirecting of the party's propaganda effort (Shirer, 1960:171). The major Nazi Party administra­ tive level beneath the Reich was the Ga u (regional party unit). The Gau leadership maintained primary responsibility 54

for the propagandistic and organization penetration within a clearly defined geographical area. Following a September

15, 1928, announcement by Hitler, the boundaries of the

Ga ue were redrawn to correspond with the Wah I krei se. -

-+■??§- in ■S3 coordination of the Nazi organization with the 2 Reich's electoral system. According to Orlow, "This meant,

in effect, that the Ga u would become a year-round tactical campaign unit, and the Gauleiter Eregional party leader] a permanent regional campaign manager" (1969:140).

This was not the only level at which the Nazi Party was to undergo reorganization prior to the 1930 election.

One of the major strategies of the Nazis' reorganization involved the proliferation of party activities and organiza­ tions at local and intermediary levels. In describing the enactment of the plan of action within Lower Saxony, Noakes suggested that

. . . at the end of 1929 . . . a new network of Kreis Ecounty] organizations model led on the existing official administrative division began to be introduced between the local branches EOntsgruppe] and district LBez i rke] leaders with the aim of eventual ly replacing the latter. This encouragement of smaller organiza­ tions was an important aspect of the party's organi­ zation. The NSDAP believed in granting independence to its smallest units as soon as possible on the grounds that 'a smaller group which must work inde­ pendently and on its own responsibility inevitably develops much greater activity than if it is directly dependent on another branch' (1971:140).

The primary purpose of the massive reorganization effort by the NSDAP was to allow for a greater penetration of the

Nazis in rural areas. The localization of the party organi­ zations involved a considerably high degree of localized 55

campaigning and in the Reichstag campaign of 1930 the program of Nazi propaganda in the country was fully operational

(Zeman, 1973:26; Bullock, 1971:81). Pridham noted that the plan for this saturation campaign (G ros s kamp f e) was publicly declared by the party's national propaganda director Joseph Goebbels who "had urged that 'by 14 September there will be no town, no village, no spot, where we National

Socialists have not made our appearance through a large meet­ ing' ’* ( Pr i dham, 1973:137). Further indication of this effort was provided in the party's paper, the Völkischer Beobachter which only a month before election boasted "that from 18

August Cuntil the September I, 1930, election] there would

t be '34,000 National Socialist meetings'" (Pridham, 1973:137).

Zeman g-f-f r j (3 u-j-e(j fhe success of these programs to a coalescence of circumstances favorable to them.

Indeed, in the final stage of Hitler's way to power circumstances conspired to make the ascent easy for him. The technical means for propaganda had been developed, and they became available to Hitler at a time when he disposed of adequate financial resources and when a highly receptive audience existed. During the year 1930 microphones and loudspeakers became the standard equipment of Nazi meetings; without them, the monster meeting . . . would have been impossible to organize (Zeman, 1973:30).

In addition to calling for a change in organizational practices, the Nazis' "Agrarian Program" also redirected their message to attract the attention of their new target audience (Pridham, 1973:114-125; Bullock, 1971:79)? This rural message guaranteed to protect landed property against 5 6

taxation and the encroachment of banks and speculators by

lower interest on agricultural loans. It further called for higher prices for farm produce and lower prices for artificial fertilizers. Additionally, as was suggested by Wi Ikinson and Conze, the Nazis' new program led to the cI arification of its position on private property.

The Nazis began to realise the necessity of making a bid for countryside support only in 1928. Their original programme had only contained one point about the land:

',We demand land reform suitable to our national requirements, the passing of a law for confis­ cation without compensation of land for communal purposes; the abolition of interest on land loans, and the prevention of all speculation in land.'

The words 'confiscation without compensation' raised a g reat deal of suspicion in quarters that had begun to realise that the Nazi movement might be used to prevent just this sort of thing. Hitler, therefore, in a declaration issued in April, 1928, to "reply to the false interpretation on the part of.opponents" obligingly explained that "since the N.S.D.A.P. admits the principle of private property" the expression "confiscation without compensation" merely refers to possible legal powers to confiscate, if necessary land illegally acquired or not adminis­ tered in accordance with national welfare. It is directed in the first instance against the Jewish companies which speculate in land." This might mean anything, but it had the effect of opening the countryside to the Nazis whose p ro p a - gandists made it very clear they were not proposing any land nationalisation schemes (Wilkinson and Conze, 1973:119).

A final circumstance which has recently stimulated much interest in the 1930 Reichstag election is the considerable disagreement about the sources of Nazi gains which has emerged from previous analyses. As was suggested above, researchers have uniformly agreed on the political sources Table 4.1

Reich Election Totals and Change: 1928 and 1930

20 May 1928 I 4 September I 930 Change Number of Number of Number of Party Names and Initials votes 1° RS* votes % RS votes % RS

Conservative Parties National Socialist (NSDAP) 809771 2.6 I2 6380465 I8. I I 07 5570694 I 5.5 95 Nationa1i st (DNVP) 4380029 I4. I 73 2457572 7.0 4I -1922457 - 7. I -32 Total Conservative Vote 5189800 I6.7 85 8838037 25. I 48 3648237 8.4 63

Working Class Parties Social Democrat (SPD) 9151059 29.4 I 53 8575699 24.3 I 53 - 57360 - 5. I 0 Communist (KPD) 3263354 I0.5 54 4590453 I3.0 77 I 327099 2.5 23 Total Working Class Vote 12414413 39.9 207 I 3166 I 52 37.3 230 751 739 - 2.6 23

Catholie Parties Center (Z) 3710747 I I .9 62 41 27005 I I .7 68 4 I 6258 - 0.2 6 Bavarian People's (BVP) 945306 3.0 I6 I 05871 I 3.0 I9 I I 3405 0.0 3 Total Catholic Vote 4656445 I4.9 78 41 84716 I4.7 87 529271 - 0.2 9

Middle Class Parties German Democrat (DDP) 150^721 4.8 25 I 322028 3.8 20 182693 - I .0 - 5 People's (DVP) 2678532 8.6 45 I 5774 I I 4.5 30 II0II2I - 4. I - 5 Economic (WP) 1402203 4.5 23 I 373839 3.9 23 - 28364 - 0.6 0 Others 3322192 I0.7 28 37370 I 5 I0.6 49 4 I 4823 0. I 2I Total Middle Class Vote 8907648 28.6 I2I 80 I 0293 22.8 I 22 897355 - 5.8 I

Tota1 All Parties 31160067 100. I 491 35226657 99.9 577 4066590 86

El igible Voters 41244733 42957762 171 3029 Non-voters 10084666 24.5 7731 I 05 I8.0 -2353561 6.5

*Numbcr of Reichstag Seats 5 8

of increased NSDAP support over the last four Reichstag

elections of the Weimar Republic. However, when, in recog­

nition of its importance for the eventual success of the

Nazis, researchers have turned their attention to the 1930

election, significantly less consensus has resulted. The

four parties that were rejected as major contributors to

the Nazi gains during the larger period (KPD, SPD, Z, and

BVP) have similarly been dismissed as accounting for sig­

nificant gains in the single election. Researchers have

alternatively offered one or more of the identified sources

for the 1930-1933 period (DNVP, centrist middle-class parties,

and increased voter turnout) as the base of 1930 Nazi gains.

Table T?V provides some indication of the discrepant

arguments which have been offered regarding the relative

importance of these three hypothesized contributors to the

1930 Nazi upsurge. Half of the 22 studies reviewed in 4-3 Table A) have identified al I of these sources (the DNVP

losses, fhe centrist middle-class party losses, and the

new voters) as playing a significant role in the Nazi

success during this, election (Allen, 1965; Heiden, 1935;

Kele, 1972; Noakes, 1971; Holborn, 1969; Hunt, 1964; Loomis

and Beegle, 1946; Eyck, 1963; Schnaiberg, 1969; Schoenbaum,

1968; Pridham, 1973). Among those researchers offering

this view, however, considerable disagreement can be found with the relative importance of each of these sources ar­

ranged into one of three different permutations. 5 9

TABLE 4.2

Types and Frequency of Findings

Sources of 1950 Nazi Gains* ______Number of Times Found**

Turnout, DNVP, MC Combination A. Turnout, DNVP, MC 4 B. DNVP, MC, Turnout 3 C. DNVP, Turnout, MC 4

Total for Combination (II)

1 1 . Turnout, DNVP Combination A . Turnout, DNVP 2 B. DNVP, Turnout 2

Total for Combination (4)

1 1 . MC, T urnout 1

1 V. MC, DNVP 1

V. MC 3

V 1 . Not Turnout 2

22

*Findings listed under combinations represent alternative rank-orderings of the elements in those combinations.

* * S e e Table 5.1, page 72. for identification of researchers offering these particular findings. 60

TABLE '<3

Findings by Disciplinary Affiliation of Researcher

Findings D i s c i p I ine

History Sociology Political Totals Science

Turnout-DNVP-MC Comb¡nation 9 2 0 (75.0) (33.3) (0.0) (50.0)

Tu rnout-DNVP Comb i nation I 2 4 (8.3) (33.3) (25.0) (18.2)

MC-Turnout 0 0 (8.3) (0.0) (0.0) (4.5)

MC-DNVP 0 0 (0.0) (0.0) (25.0) (4.5)

MC 2 0 3 (8.3) (33.3) (0.0) (13.6)

Not Turnout 0 0 2 2 (0.0) (0.0) (50.0) (9.1)

Tota I s I 2 6 4 22 (99.9)

*TotaI percentage does not equal 100.0 due to rounding. 6 I

The general argument that NSDAP gains in 1930 were made possible because of the combination of DNVP and centrist party losses and the sudden influx of new voters has been advanced by researchers focusing their analyses on a variety of geographical levels. Historians McKenzie (1971), Eyck

(1963), Schoenbaum (1968), Holborn (1969), and Kele (1972) who al I based their investigations on analyses oyt gross election returns for the whole of the Reich each offered some permutation of this three-pronged base of Nazi gains.

Similar interpretations were provided by researchers, again primarily historians, who directed their attention to more localized regions within the Reich. Included within this category of investigations are Noakes' (1971) study of

Lower Saxony, Pridham's (1973) study of the Catholic state of Bavaria, Hunt's (1964) comparative study of four Wahl- k re i se^. K6 I n-Aachen , Hamburg, Ostpreussen, and Miederbayern], and Allen's (1965) study of the town of "Thalburg".

Whi le historians have represented the majority of those offering this response to the question of the bases of Nazi gains in the 1930 Reichstag election, they have not been alone in advancing this interpretation. Sociologists

Loomis and Beegle (1946) and Schnaiberg (1969) employed returns for each of the electoral districts to make a general statement of the sources of NSDAP gains throughout the Reich. Based on their bivariate and multivariate cor­ relational analyses, these researchers also pointed to the 62

significant contributions to the 1930 NSDAP success made

by defections from the Nationalist Party, the liberal and

moderate middle-class parties, and the large number of new

voters.

The next most popular thesis focused on the defections by prior Nationalist party supporters to the Nazis and gains by the NSDAP due to the increased participation by the elec­ torate. While not in agreement on the relative contributions to the Nazis made by these two sources, analysts have been unanimous in advancing the view that the moderate and

liberal middle-class parties (DDP, DVP, WP, etc.) suffered few losses to the National Socialists (Bendix, 1956; Mellen,

1943; McKenzie, 1971; O'Lessker, 1968).

Alternatively, other investigators have attributed the success of the Nazis in 1930 to the combined sources of middle-class party losses and the new voters (Scheele,

1945), middle-class party losses and DNVP defections (Wald­ man, 1973), or exclusively to the middle-class party defec- tions (Heberle, 1951; Heberle, 1970; Lipset, I960; Orlow,

1969). Similarly, some political scientists addressing only the question of the role played by the new voters in the 1930 electoral gains by the Nazis have suggested that this portion of the electorate made only negligible con­ tributions to the NSDAP (Pollock, 1944; Shively, 1972).

The basic issues in the disagreement can best be under­ stood by reviewing a number of key studies. After noting 6 3

the significant number of new participants and the drastic

reduction in DNVP support, Bendix offered an interpretation which is stiII favored by many, in spite of its later

rejection by Bendix (Bendix and Lipset, 1959:12):

Ethe] most plausible interpretation of this evidence is to suggest that the increase in Nazi votes resulted from a radicaI ization of members in the nationalist parties of the Right and from the sudden participation of about 4,200,000 non-voters and young people (Bendix, 1953:605).

Countering this contention, Lipset argued that while new voters may have figured importantly in the Nazi increase in later elections, their contribution to 1930 Nazi gains was minimal. Specifically, Lipset suggested that previous analyses which have advanced the view that Nazi gains were strongest among the new voters, inappropriately based their conclusions 4 on over-al I election returns for the Reich. He further points out that "when changes in the rates of non-voting and Nazi vote are broken down by districts (WahIkrei s e), the rank-order correlation between the Nazi per cent increase and the increase in the proportion of eligible voters actually casting ballots is -.2.

In only five of the electoral districts where the Nazi gain between 1928 and 1930 exceeded their average gain for all of Germany was the increase in the size of the electorate also disproportion­ ately high. In twenty-two of the thirty-five national districts, there is a negative relationship; either the voting gain is low and the Nazi gain is high, or vice versa (Lipset, 1960:150).

In addition to rejecting the hypothesis that expanded

Nazi vote in 1930 was influenced by the increased participation 64

by the electorate, Lipset also cast doubt on the other possible source noted by Bendix: the DNVP. While failing to specifically address the 1930 DNVP defections, he noted that the rank-order correlation between proportionate Nazi gains and conservative losses over the period covering al I of the elections of the early 1930's is +.25. When this is compared to the +.45 correlation between Nazi gains and middle-class losses, Lipset concluded that "the Nazis gained disproportion ate I y from the ranks of the center and liberal parties rather than from the conservatives" (I960 :

I 44-I 45.

In directing his attention to Lipset's findings, O'Less­ ker suggested that a closer analysis using different, more powerful statistical tools could provide firmer ground for making an evaluation on the role of various groups in the

1930 NSDAP triumph. Basing his conclusions on a multiple regression analysis, O'Lessker offered an interpretation reminiscent of that previously advanced by Bendix.

The most important single source of Nazi strength in 1930 came from voters who had formerly sup­ ported the conservative, uItra-nationaIist DNVP: in statistical terms, fully 38 per cent of the variance in the Nazi vote can be attributed to this source. But a close second in importance, accounting for some 32 per cent of the variance, were the previous non-voters: that group, which in Lipset's analysis, appeared to be wholly unrelated to the group of new Nazi voters (1968: 66 ) .

He further noted that the liberal middle-class parties made contributions of considerably less importance than the pre- ced ing two groups. 6 5

The original objections raised by O'Lessker against

Lipset's analysis, which led to the contradictory findings,

revolved around his use of specific statistical procedures.

Objections of this same type have simi larly been advanced

against the work of O'Lessker.

After noting the possibility that joint or indirect

effects could result in confusing multiple correlation

coefficients, Schnaiberg (1969) advanced as an alternative

procedure, the partitioning of total correlations between

"sources" and Nazi vote into direct and indirect effects.

Employing this technique, Schnaiberg posited that although

the increased turnout had a substantial direct effect on

NSDAP increases, it also had a large joint effect (-.76)

which derived from the large positive relationships between

increased votes given to other parties. He further sug-,

gested that "a substantial proportion of the increased

turnout went to non-Nazi parties, particularly the Com­ munist and Catholic center groups" (1960:734). Thus the total correlation between f he 1930 Nazi vote and voter turnout is +.32, indicating that the Nazis gained consider­ ably less from increased participation than had been asserted by O'Lessker.

Addressing the other "sources" Schnaiberg noted that the 1928 supporters of the DNVP appear to have shifted en m a s se to the Nazis. They were not alone in their voting shifts, however, being joined by the previous backe rs of 66

the liberal middle-class parties. Schnaiberg concluded his

analysis by suggesting that

Lipset appears to have been on sound ground in changing Bendix's view that the Nazi increase was due principally to new voters. But he does not appear tc have been correct in assuming that only the non-Catholic middle-class parties' sup­ porters were defecting to Nazism; the DNVP also appears to have been a rather large contributer fo the Nazi success (1969:734).

More recently, political scientists have deemed the

thesis regarding the role of new voters in the Nazi success offered by Schnaiberg and other^ worl^l of renewed attention.

Recent investigations by his disciplinary colleagues have

generally been consistent with the view initially advanced

by political scientist James Pollock (1944) that the in­

creased vote was not a significant factor in the 1930 Nazi

success. Employing a variety of techniques, Shively (1972)

ultimately suggested that based on his use of geographic

correlations, sex-aggregated correlations, and time-series

analysis, it is most unlikely that the Nazis gained any

unusual degree of support from previous non-voters.

This same conclusion was drawn by Waldman (1973) in

one of the most complete statistical analyses of the sources of Nazi support. Computing correlation coefficients across the counties o r the Re i ch , Waldman found that voter partici­

pation was only weakly related to Nazi vote in 1930 (r=.004) and the change in Nazi vote b.etween 1928 and 19 30 (r=.O38).

Similarly, the relationship between the change in voter turnout and the change in Nazi vote between these two elections was also reported as negligible (r=.O35). 6 7

In light of these rather contradictory and inconclusive

findings and given certain methodological difficulties en­ countered in the preceding analyses, the question of the sources of the 1930 Nazi vote remains without a satisfactory answer and, thus, is in need of further research. The current study has focused on a careful re-evaluation of the seven hypotheses offered 'Ihroughout the relevant literature and provided at the conclusion of Chapter Three.

Prior to turning to the actual analysis undertaken in the current study, however, it is necessary to address a number of methodological issues that have been at least partially responsible for the confusion surrounding the question under investigation. While these issues have been alluded to throughout much of the preceding discussion, their more direct presentation, as we I I as a discussion of solutions offered in this study, will be fruitful. 68

NO'

In the 1930 Reichstag election, the Nazis were able to increase their previous electoral strength in every county in every district of the Reich. 2 While this was the general policy of reorganization, there were some exceptions to the one-to-one correspondence between WahIkrei s e and Ga ue. The state of Bavaria, consisting of Wahlkreise Oberbayern-Schwaben, Niederba yern, and Franken, remained one Gau and the national leadership "took direct charge of two districts in the old Ruhr Ga u QDusseIdcrf-Ost and DusseIdorf-West]" (Orlow, 1969:140).

^The importance of the twin elements of political strategy, organization and propaganda-, was recognized very early in Nazi h:story. In the init:a, major statement of his plan of attack, Mein Kamp f, Adolph Hitler suggested that "The function of propaganda is to attract supporters, the function of organization to win members . . . The victory of an idea wi I I be possible the sconer, the more comprehen­ sively p r o p a g a n c a has prepared people as a whole and the more exclusive, rigid, and firm the organization which carries out the fight in practice" (Hitler, 1973:236).

^While this observation is generally correct, the previously discussed studies by Loomis and Beegle (1946) and Schnaiberg (1969) are examples of analyses which have employed district-by-district returns. 69

CHAPTER FIVE

Methodological I ss ues

Disagreements in the literature addressed to the 1930

Reichstag election have primarily, but not exclusively re­

volved around the alternative conclusions which have been offered regarding the sources of Nazi support in this bal­

loting. Whi l-e only infrequently discussed by those research­

ing this issue, divergent viewpoints regarding certain methodological questions are quite apparent'within this body of I iterature. Paramount are two issues on which, either

implicitly or explicitly, all analysts of the Nazi's electoral success have been forced to take positions: the units of analysis and the statistical procedures to be employed in attempting to arrive at the clearest understanding of the sources of this increased vote. Given the potential imp I i- cations of alternative stances regarding these methodological concerns for the resultant conclusions, a complete examina­ tion of them is crucial. These are addressed in the first two sections of this chapter.

Those .researchers who have explicitly addressed these questions have generally done so in an effort to demonstrate weaknesses in prior approaches and to put forth alternative research schemes as advances over what had come before. In 70

spite of this, however, these "solutions" have often intro­ duced new problems or at least failed to solve the old ones.

In response to the problems of the "ecological fal lacy" and mu 11ico I I inearity, which are associated with the selection of units of analysis and the use of specific multivariate statistical procedures, respectively, the final section of the present chapter then discusses the technique of cluster analysis as a means of minimizing these problems.

Units of Analysis and the Ecological Fallacy

In the absence of information regarding the behavior of individuals in the 1928 and 1930 elections, researchers investigating the 1930 Nazi electoral surge have had to depend on territoria I Iy-aggregated election returns as their data base. Since "spacia I-territoria I" (Dogan and Rokkan,

1969:4) or areal units are "modifiable units" (Yule and

Kendall, 1950; Valkonen, 1969:61), that is, a single set of aggregations is not the only possible one, it is not sur­ prising that a review of /fhe literature addressed to the question at hand uncovers considerable variation in the types of units for which data have been analyzed. Based on the definitions offered by Duncan, et ■ a I . , (1961:32),

where some "universe of territory" constitutes the field of study and this universe is sub­ divided into a "basic set of areal units" on the condition that the subdivision exhausts the universe of territory and no areal unit overlaps another, 7 I

a I imited number of general models of aggregation are para­

mount.

In the first design (Model I, Table 5.2), the nation of

Germany is projected as the universe of territory. Con­

clusions drawn regarding the 1928 electoral behavior of

1930 Nazi backers are global and said to be valid for the

entire Reich (Pollock, 1944; Loomis and Beegle, 1946; Lipset,

I960; Eyck, 1963; Schoenbaum, 1966; O'Lessker, 1968; Orlow,

1969; Schnaiberg, 1969; Waldman, 1973). In the computation

of numerous correI ationa I measures, Germany's WahIkre i se,

the Reichstag election districts, are employed as the basic

areal units. In 1928 there were 35 WahIkreise with consti­

tuencies ranging from one and a quarter to two and a half

million people (Pollock, 1929:874).

The major benefit of Model I is its concern with the

identification of the sources of increased Nazi support for

the nation of Germany-. Although the magnitude of the in­

crease in support for the Nazis varied from district to

district, the NSDAP was a party with some backing through­ out the Reich (Pollock, 1944; Pridham, 1973:139-140),

making the question of the source of Nazi gains throughout

Germany a significant one. The percentage of the total vote

received by the NSDAP increased in al I 35 W a hIk r e i s e, ranging

from a low of 7.5 in Wurtemberg to a high of 23.2 in the district of Breslau (see Table 5.3).

On the other hand, a design which addresses such a 72

TABLE 5.I

Models, Findings, and Statistics Employed by Researchers in the Analysis of the 1930 Reichstag Election

Researchers (Year, Discipline) Model* Findings** Statistics

1. Allen (1965, Hi story ) 1 V 1 A Descriptive 2. Heiden (1935, History) 1 V 1 A Descriptive 3. Kele (1972, Hi story) 1 V 1 A Descriptive 4. Noakes (1971, History) 1 V 1 A Descriptive 5. Holborn (1969, History) 1 V 1 B Descriptive 6. Hunt (1964, H i s t o iry ) 1 V 1 B Descr i pt i ve 7. Loomis & Beegle (1946, Sociology) 1 & 1 1 1 B Co r re 1 a t i on 8. Eyck (1963, H i story ) 1 1 C 9. Schnaiberg (¡969, Sociology) 1 IC Corre 1 at i on 10. Schoenbaum (1968, History) 1 IC Descr i pt i ve 1 I-. Pridham (1973, History) 1 1 IC Descriptive 12. Bendix (1956, Sociology) 1 V 1 1 A Descr i pt i ve 13. Mellen (1943, Political Science) 1 V 1 1 A Descr i p t i ve 14. McKenzie (1971, History) 1 V 1 1 B Descr i pt i ve 1 5 . O'Lessker (1968, Sociology) 1 1 1 B Corre 1 at ion 1 6. Scheele (1945, History) - 1 V 1 1 1 Descr ipt i ve 17. Waldman (1973, Political 1 , 11 Science) & 111 1 V Co rre 1 a t i on 18. Heberle (1951 & 1970, Sociology) 1 1 V Correlation 19. Lipset (I960, Sociology) 1 & 1 1 V Correlation 20. Orlow (1969, Hi story ) 1 V V Descriptive 2 1 . Po1 1ock ( 1944, Pol i t i ca1 Science) 1 V 1 Descr i pt ive 22. Shively (1972, Political Science) 1 1 V 1 Correi at i on

*TabIe 5.2, page 73 **Table 4.2, page 59 7 3

TABLE 5.2

Alternative Models of Aggregation Employed i n the Analysis of Weimare Electoral Data

Aggregate Model Model Model Model 1 1 1 1 1 I 1 V

The Reich UT* UT UT and BAU**

Large-level Geopolitical Aggregates--i .e. , Wahl- kreise, provinces, states, reg ions BAU UT UT and BAU

OR

Proximal Geopolitical Aggregates--i.e., Kreise, towns, cities, communities BAU BAU UT and BAU

Examples of Research Applications of the Basic Models Pol lock Loomis & W a 1dman Mel len '( 1 944 ) Beeg le (1973) (1943) Loom i s & (1946) Scheele Bée g 1 e Heberle ( 1945 ) (1946) (1951, Noakes Lipset 1970 ) (1961) (1960) Li pset Hunt Eyc k ( I960) ( 1 964 ) ( 1 963) Shively Allen Schoenbaum (1972) ( 1 966 ) ( 1966) P r i dham Holborn 0 ' Les s ke r (1973) ( 1 969) ( 1 968) Wa1dman Orlow Schnaiberg (1973) ( 1 969 ) ( 1969 ) McKenz i e Wa1dman (1971) (1973)

^Universe of Territory (Duncan, et. a I . , 1961:32) **Basic Areal Unit (Duncan, et. al., 1961:32) / 4

large unit, and employs aggregations as populous as the

electoral districts may be flawed in its tendency to over­

look the regional variations that did exist (Allardt, 1966:

339; Blalock, 1964:99; Capecchi and GaI I i, 1969; Janson,

1969:331; Scheuch, 1966:149). This criticism of Model I

was offered by Heberle in his discussion of Pollock's

early analysis. According to Heberle, by using the Reich's

large electoral districts, Pollock (1944) employed units

which are much too heterogeneous to reveal any factors

influencing political behavior. Heberle further suggested

that his own investigation of Schleswig-Holstein

shows once more the necessity of studying small, relatively homogeneous areas whenever one is concerned with a causal explanation of political behavior. The use of too large areas tends to conceal those differentials which give one the clue to an inquiry into possible factors ( 195 1 :236n).

The alternative offered by Heberle is the second major

aggregation design (Model II, Table 5.2). This model limits the universe of study to some portion of the WeimarRepubI ic.

While a number of divisions, including states and regions, have served as the universe, the most frequently employed aggregate is the Wahlkreis.(see Table 5.4). While all researchers working within this framework have utilized fairly small or proxima I - I eve I subdivisions as the basic analytical units, a number of different units have served this function.

Most commonly, the Kre i se have been employed in this capacity.

Alternatively, however, investigators have employed units 7 5

Table <>..3

WahIkrei s by Wahlkreis Per Cent of Total Vote Received by Nazis 1928, 1930, and 1928-1930 Change

Wahlkreis 1 928 1930 1 928-1930 Change*

1 . Ostpreussen 0.8$ 22.5$ 2 1.7$ 2 . Be r1 in 1 .4 12.8 11.4 3. Potsdam 1 1 1 . 8 22.5 14.9 4. Potsdam 1 1 . 6 18.8 17.2 5. Fran kf u rt/Ode r 1 . 0 22.7 2 1.7 6. Pommern 1 . 5 24.3 22.8 7. Breslau 1 . 0 24.2 23.2 8. L i egn i tz 1 . 2 20.9 19.7 9. Oppeln 1 . 0 9.5 8'. 5 10. Magdeburg 1 . 7 19.5 17.8 1 1 . Merseburg 2.7 20.5 17.8 12. T h u r i n g e n 3.7 19.3 15.6 13. Schleswig-Holstein 4.0 27.0 23.0 14. Weser-Ems 5.2 20.5 15.3 15. Osthannover 2.6 20.6 1 8.0 16. Sudhannover-Braunschweig 4.4 24.3 19.9 17. Westfalen Nord 1 . 0 12.0 1 1 . 0 18. Westfa 1en S ud 1 . 6 13.9 12.3 19. Hessen-Nassau 3.6 20.8 17.2 20. Koin-Aachen 1 . 1 14.5 13.4 2 1 . Koblenz-Trier 2. 1 14.9 12.8 22 . Düsseldorf Ost 1 . 9 17.0 15.1 24 . Oberbayern-Schwaben 6.2 16.3 1 0. 1 25 . N i ederbayern 3. 5 12.0 8 . 5 26 . Fran ken 8. 1 20.5 12.4 27. Pfalz 5.7 22.8 17.1 28. Dresden-Ba uzen 1 . 8 16.1 - 14.3 29 . Leipzig 1 . 9 14.0 12.1 30. Chemn i tz-Zw i cka u 4.4 23.8 19.4 3 1 . W u r 11 e m b e r g 1 . 9 9.4 7 . 5 32. Baden 2.9 19.2 16.3 33. Hessen-Darmstadt 1 . 9 18.5 16.6 34. Hamburg 2.6 19.2 16.6 35. Mecklenburg 2.0 20 . 1 18.1

*Change = per cent 1930 vote - per cent 1928 vote /b

such as cities and towns for the same purpose (Loomis and

Beegle, 1946; Heberle, 1970; Shively, 1972; Pridham, 1973;

Lipset, I960; Waldman, 1973).

Thus, the major limitation of Model I outlined by

Heberle is at least partially avoided by Model II with its- smaller universe. The latter design also benefits from the inclusion of smaIIer aggregates as the basic areal units,

in that they may be more sensitive to the fluctuations in the sources of Nazi support found even between neighboring

Kreise (Noakes, 1971:153).

The principle limitation of Model II, taken by itself, is undoubtedly lV-V. narrowness of scope. Consequently, this design is most frequently employed in conjunction with alternative aggregation schemes (Loomis and Beegle, 1946;

Waldman, 1973; Lipset, I960; Shively, 1972).

In a recent study of the rise of the Nazis, Waldman

(1973) offered a third aggregation scheme (Model I.II).

With respect to the scope of design, Model III is similar to the often-encountered Model I, in that both address the nation of Germany as the universe of study. Unlike Model

I, however, the alternative proposed by Waldman employs the nation's Kreise as the basic analytical units rather than the 35 larger electoral districts. Waldman was thus able to incorporate Heberle's emphasis on the value of the use of small units into a model addressing the bases of

1930 support throughout the Reich. 7 7

Table {.X

Areas Employed as "Universe" by Researchers Using Aggregation Model II

Resea rcher Un i verse (including Wahlkreise)

Heberle Schleswi g-Ho 1 stei n Schleswig-Holstein

Lipset Schleswig-Holstein Sch 1 esw i g-Ho 1 ste i n

Loomis & Beegle Schleswig-Holstein Schleswig-Holstein Hannover ' Weser-Ems Osthannover Sudhannover-Braunschweig Bavaria Oderbayern-Schwaben N i ederbayern Franken Pta 1 z

Noakes Lower Saxony Weser-Ems Osthannover

Pridham Bavaria Oderbayern-Schwaben N i ederbayern Franken

Shively* Pomme rn Thüringen N i ederbayern

Waldman Most Wahlkreise

*Employed these geo-poI iticaI aggregates in conjunction with sex-aggregation 78

The f irst step in the analysis of election statistics is the choice of territorial units . . . Small areas a re I i keIy to contain a voting population of greater socio-economic homogeneity than large areas . . . therefore, the smaller the areas for which election results can be obtained the better (Heberle, 1951: 206) .

Since party support in Weimar Germany was closely tied to

social class (Heberle, 1951:317), the use of units which I tend to maximize socio-economic homogeneity should similarly

increase the differences in voting patterns between units.

In addition to maximizing the between-unit variation

in party support, employment of the Kreise, the smaI lest aggregates which can exhaust the universe of. territory and

for which complete returns from across the Reich are readily accessible, will also tend to increase sensitivity to varia­ tions in the sources of increased Nazi support even between neighboring areas.

An analysis of the figures for the Kre i se Ein the region of Lower Saxony] show that the correlation between the rise in NSDAP vote and the deci ine of One or more parties varied markedly from Kre i s to Krei s. In the north of . . . Hanover, for example, where the NSDAP made very big gains, the DNVP suf­ fered very heavily in Kre i s Diepholz losing over 1000 votes, whereas in the neighboring Kreise Syka and Hoya it actually increased its vote slightly (Noakes, 1971:153).

The use of the Krei se as the basi c unit of analysis offers one final benefit. In their di scussions of the selec tion of appropriate units on which to base one's analysis, most researchers have emphasized the i mportance of choosing units which have some sociological rei evance to the problem under investigation. Pointing to some problems in the use Z9

of areal or ecological data which are addressed below

Valkonen suggested that

To solve problems associated with the use of eco­ logical data it seems necessary to examine the sociological meaning of areal units and the kind of effects their properties may have on the behavior of individuals (1969:53).

While directing their attention specifically to the use of

aggregate data in electoral analysis, Dogan and Rokkan offered a similar warning in asserting that "the behavior of citizens cannot be understood without knowledge of the ecological alternatives set for them by parties, electoral managements, and so on . . ." (1969:8).

In light of the key role played by the NSDAP's Kre i se organizations following the party's reorganization prior to the 1930 balloting, the inclusion of units at this level carries with it an added significance. The crucial role of the Nazi party's county-level organizations in propaganda activities make these units particularly sensitive to the understanding of the 1930 NSDAP success.

The last common I y encountered aggregation design has been employed in half of the investigations of the 1930

Reichstag elections. This design (Model IV) has most fre­ quently been offered by historians and has been used in the majority of analyses undertaken by historians. In Model IV researchers have based their analyses solely on the gross election returns for a given aggregate. This unit is defined as both the "universe of territory" and the "basic areal unit" 80

Social scientists adopting this scheme for an analysis of the Reich have included Mellen (1943), Heiden (¡935),

Scheele (1945), Holborn (1969), McKenzie (1971), and Kele

(1972). In his analysis of four WahIkrei se (Ko In-Aachen,

Hamburg, Ostpreussen, and Niederbayern) Hunt (1964) adopted this scheme, as did Allen (1965) in his study of the Nazi takeover in the town of "Thalburg". The major weakness of this model, which is discussed further in the next section of this chapter, involves the recurrent dependence of inves­ tigators on the comparison of total party votes and per­ centages. The limited potential for any further data reduction makes the interpretation of data both more difficult and more tenuous than with the previously-discussed aggregation models. In spite of this weakness, however, studies employing this design are invaluable in that they frequently offer a wealth of qualitative and historical data.

While an absence of available data limits their utility, two alternative models are noteworthy. They have been sug­ gested by a relatively recent entrant into the debate on the s'ources of Nazi electoral support, W. Phillips Shively.

One design outlined by Shively involves his technique of

"sex-aggregation analysis".* Noting the possibility of the repeated employment of solely geo-political aggregations producing "a result which is replicable but spurious", he introduced an alternative design which is based on

the aggregation of all men living in a particular 8 I

Table . S

Areas Employed as "Universe" by Researchers Using Aggregation Model IV

Resea rcher Un i verse

Allen "Thalburg" (pseudonym -for town in Wahlkreis Osthannover)

Bendix The Reich

H e i den The Reich

Holborn The Reich

Hunt Wahlkreise Ko 1n-Aachen Hamburg Ostpreussen N iederbayern

Ke 1 e The Reich

McKenzie The Reich

Mel len The Reich

Orlow Wahlkreise Schleswig-Holstein Weser-Ems • Sudhannover-Bi aunschweig Franken Oberbayern-Schwaben

Scheele The Reich 82

Table 5.. 6

Aggregat i on Model Employed, by Discipline

Mode I History Sociology Political Total Science

2 2 I 5 ( 16.7) * (33.3) (25.0 ) (22.7)

I 3 (8.3) (16.7) (25.0) (13.6)

I V 9 I I (75.0) (16.7) (25.0) (50.0)

I and II 0 2 0 2 (00.0) (33.3) (00.0) (9.1)

I, II, and 0 0 I (00.0) (00.0) (25.0) (4.5)

Totals I 2 6 4 22 ( 100.0)

^Figure in parentheses represent percentages of the column totals, i;i>n this and all subsequent tables in this chapter G 5

city, and the aggregation of alI women I iving there. For these two aggregations, the correlation between change-in-participation and change-in- percentage-of-vote-Nazi can be calculated, just as it can be calculated for two or more geographic districts (Shively, 1972:1214).

It might be tempting to adopt this alternative scheme

in light of Shively's conclusion that the NSDAP won dis­

proportionate support from previous nonvoters only in the

March 1933 election, if at all. However, this scheme is

severely hampered by a paucity of usable data. As Shively

pointed out, the required data on per cent of participation

and party votes were only tabulated in nine cities and towns

throughout the Reich. Furthermore, some of these districts

only kept the necessary records for a limited number of

elections between 1928 and 1933. Thus, Shively's conclusion

regarding the limited support from new voters was based on

only fourteen aggregations, or one composed of males and

one of females for each of seven cities.

As a second alternative to the most commonly encountered

geo-political aggregations, Shively further offered an

analysis of a series of changes over time in the proportion

of votes going to the NSDAP and the voter turnout, for any

particular geo-political units. Unlike other attempts at

the "time-series" analysis of the shifts in Weimar party

support, Shively enhances his analysis with the inclusion of local and state (Land) elections, as well as the 1932

balloting for the president of the Reich While this mode 84

of analysis appeared to provide some justification for

Shively’s skepticism regarding the importances of new votes

in Nazi electoral success, it too is restricted by a

limited base of available data.

Given the availability of all requisite data, a primary

goal of these analyses of the sources of Nazi electoral

gains in 1930 would be a precise understanding of the

relationship between support for the NSDAP in that year and the 1928 voting behavior of individuals. Unfortunately,

however, as in other historical studies where there exists no possibility of gathering additional data, the "missing- data problem" is characteristic of research into past elections (Linz, 1969:98; Duncan, et. a I . , 1961:26-27).

Hannan observed that

Electoral analysts are presently able to overcome many of the difficulties posed for individual level analysts through the use of opinion po I I data and survey research. However, for past elections this is no longer a viable option (1971a: I I).

Furthermore, as was noted by political scientist V.O. Key, existing aggregate level data may be somewhat limited in what they can tell the researcher.

Students of politics become concerned about the magnitude of the movements of voters from party to party. Election figures do not tell us much about this phenomenon because they show only the net shift of strength. One party has a larger proportion of the vote than it did at the preceding polling (Key, 1966:44). 85

Table 3 3

Findings by Aggregation Model Employed

Findings .Model

1 II IV 1 & 11 1, II Totals & 11 1

T urnout-DNVP- MC Comb¡nation 3 1 6 1 0 1 1

(60.0) (33.3) (54.5) (50.0) (0.0) (50.0)

Turnout-DNVP Comb¡nation 1 0 3 0 0 4 (20.0) (0.0) (27.3) (0.0) (0.0) (18.2)

MC-Tu rnout 0 0 1 0 0 1 (0.0) (0.0) (9.1) (0.0) (0.0) (4.5)

MC-DNVP 0 0 0 0 1 1 (0.0) (0.0) (0.0) (0.0) (100.0) (4.5)

MC 0 1 1 1 0 3 (0.0) (33.3) (9.1) (50.0) (0.0) (13.6)

Not Turnout 1 1 0 0 0 2 (20.0) (33.3) (0.0) (0.0) (0.0) (9.1)

Totals 5 3 1 1 2 1 22 (100.0) 86

In the above passage, Key was offering an indirect

reference to what is undoubtedly the dominant issue surround­

ing the interpretation of aggregate data, of which electoral

data are but one type. He was al luding to the problem of

the ecological fal lacy or what Galtung termed the "fal lacy

of the wrong level" (1969:45). This fallacy refers to the

problematic nature of the assumption that variables found

to be associated for units at a particular level of aggre­

gation will be similarly related for units at another level.

Most frequently, the problem of the ecological fal lacy has

been seen to be particularly acute when aggregate level data

are utilized in correlational analyses.

One of the earliest statements addressed to the

ecological fallacy in sociological analysis is the now

classic article by W.S. Robinson (1950). In this work,

Robinson cautioned users of ecological correlations (cor­

relations computed from aggregate data) not to employ such

measures as substitutes for individual correlations. In

support for his admonition, he pointed to the apparent

absence of a clear comprehension of the relationship between

correlation measures employed at different aggregation

levels.

After analyzing the effects of different sized groupings of census tracts on the size of correlations coefficients,

GehIke and Biehl, in an even earlier work, concluded that

fluctuations in the magnitude of correlation coefficients

are in some sense "conditioned upon changes in the size of 8 7

the unit used, with a smaIIer value of r associated with the

smallest unit rather than with the largest" (1934:197).

Similar conclusions were drawn by statisticians Yule and

Kendal I (1950) who made note of the "attenuation effect" whereby an increase in the size of the aggregate units yields a larger correlation coefficient than that derived from the use of data from smaIIer units.

Since these early contributions, this problem of the

interpretation of the relationship between variables measured in ecological, grouped, aggregate, or areal form has received considerable attention. A review of some attempts to re­ solve this Issue (Menzel, 1950; Goodman, 1953; Goodman, 1959;

Duncan and Davis, 1953; Duncan, et. a I . , 1961; Blalock, 1964;

Cartwright, 1969; Janson, 1969; Shively, 1969; Hannan, 1971a;

Hannan, 1971b; Lazarsfeld and Menzel, 1972; Allardt, 1966;

Allardt, 1969; Capecchi and GaI I i , 1969 ; Jones, 1974;

Linz, 1969; Ranney, 1962; Scheuch, 1966; Valkonen, 1969;

Jones, 1972; Kousser, 1973; Lichtman, 1974) lends support to Galtung's assertion that the

most important conclusion to be drawn seems to be as fol lows: sometimes translation from one level to another may be tenabIe in all cases, sometimes it may be untenabIe in all cases, but very often it is likely to be a mixture (1969: 48--emphasis in original).

Out of. these many discussions of the use of ecological data has emerged a limited number of suggestions for the minimization of problems associated with its use. Three general types of so Iutions^differing in the degree to which they involve a statistical or interpretational focus^have 8 8

predomi nated.

The most commonly discussed procedure has been The use

of regression analysis on ecological data (Shively, 1969;

Goodman, 1953; Goodman, 1959; Blalock, 1964; Jones, 1972;

Kousser, 1973; Lich+man, 1974; Jones, 1974; Valkonen, 1969;

Hannan, 1971a). While a number of al+erna+ive ecological

regression techniques have been offered, all are concerned

with the estimation of individua I - Ieve I relationships from A aggregate-I eve I statistics. Researchers advocating this

technique have suggested that the use of ecoIogicaI regres­

sion coefficients, rather than ecological correlations

provides a solution to the attenuation problem noted by

Robinson and others.

A second procedure for dealing with aggregate data was advanced by Hadden and Borgatta. Their offering in­ volved the introduction of specific statistical procedures as we I I as a reconceptual ization of the data. In a project concerned with the development of a system of classification of U.S. cities, Hadden and Borgatta attempted to reduce a

large list of aggregate variables to a limited number of underlying dimensions through the use of factor analysis.

Noting the sizeable body of literature which has suggested

"that the results obtained in ecological research are, to a considerable extent, dependent on the way the ecological unit is defined", they concluded that the effects of changing units on the analysis of a factor structure was worthy of 89

closer examination (Hadden,and Borgatta, 1965:185). While

not dealing with shifts from aggregate- to individuaI - I eve I

associations they compared the factor structures derived

from various definitions of aggregates.

Inspection of the factors indicated that a con­ siderable parallelism in structure existed for the different sets of data: ten of the fifteen factors occurred in parai lei for al I three sets of data ... To further test the parai lei ism of factorial structure, the variable loadings of the parallel factors were intercorreIated . . . Remarkable stability of structure occurs for the first five factors, where the correla­ tions between the parallel factors are all .92 or greater, which means that at a minimum 85 per cent of the variance is in common for the parai lei factors (Hadden and Borgatta, 1965: 188).

Thus by addressing the dimensions or factors underlying a

set of variables rather than the variables themselves, a degree of consistency can be achieved which is not dependent on the units employed.

A third method for overcoming the problems associated with the use of aggregate data is not concerned with the

issue of the appropriate statistical measures to be employed so that cross-level inferences can be made but is based on the premise that areal units can be viewed as wholes in their own right (Valkonen, 1969:53). Shortly after W.S.

Robinson's word of caution to sociologists about assuming • that correlations between variables at the aggregate level are valid indicators oF relations at the individual level,

Menzel (1950) argued that Robinson's findings do not deny the validity of aggregate measures. Basing his argument on Q0

Robinson's own examples, Menzel suggested that while ecologica and individual correlations may indeed differ, the former are useful in that they provide information about the ter­ ritorial units themselves. These aggregated variables can, therefore, be used as contextual properties which aid in the explanation of variations in the correlated variables

(Allardt, 1969:41-2; Ranney, 1962; Lazarsfeld and Menzel,

1965).

When applied to the analysis of electoral statistics, this approach involves the recognition that it is impossible to discover how each individual voters cast their ballots.

It is possible, however, to discover how the electorate within each district, county, state, precinct, etc., voted as a body. Since voters within a given unit, particularly within small units, hold many characteristics in common, some degree of generalization "regarding the relationship between a certain way of voting and a certain kind of people" may be possible (Olds and Salmon, l948:A-3). Working from this perspective, Ranney summed up the strength of aggre­ gate electoral analyses by suggesting that

. . . they are most likely to make valuable contri­ butions if they bypass questions about particular individuals and adopt as their sole object of“ in­ quiry the behavior of eIectorates ( I 962:99--emphasis .inoriginal). ^From the preceding discussion, it should be clear that in the current investigation, inferences regarding the individuaI - IeveI relationship between 1928 voting practices and later support for the Nazis can only be drawn with the utmost caution. While still subject to much criticism, 9 I

the existence and application of the above-discussed tech­

niques for minimizing aggregation bias makes this I eve I -

shifting less suspect than some observers might feel. As

a precautionary measure, however, the current study made

use of two alternative aggregation schemes (Models I and

III) so that the effects of changing units may be observed.

Additionally, the value of aggregate data for the under­

standing of contextual properties associated with gains in

1930 is wel I recognized and wi I I serve as the foundation

for the construction of several typologies of areal units

based on these contextual characteristics.

Prior to turning to the specific aggregation or data

reduction procedures employed in this investigation, it is

necessary to address the second major methodological issue

encountered in the literature addressed to the identifica­

tion of the sources of 1930 Nazi support: the selection of

the appropriate statistical techniques for a successful

analysis and the problems associated with them. Whi Ie

some discussion of statistical procedures was offered in

this section, this has dealt with rather general concerns

encountered in the social scientific literature addressed

to the use of aggregate data rather than specific applica­

tions to the analysis of the rise of the Nazis.

Statistical P roce du res and Mu It i co I I i n ea r i ty

Throughout the literature dealing with the analysis of the 1930 Reichstag elections, one encounters considerable '! 2.

disagreement about which statistical techniques are most

appropriate for such an analysis. Researchers concerned

with specifying the degree to which the 1930 Nazi vote

or the increased Nazi vote between 1928 and 1930 is associated

with some other variable (i.e., DNVP losses, increased

turnout, defections from the centrist middle-class parties,

etc.) have tended- to emphasize one of two major statistical strategies. In a monograph directed at introducing students of politics to the methods of political research, Shively noted that

. . . there are two ways in which we can measure the "strength" of a relationship between two variables. (I) We can measure how great a difference the inde­ pendent variable makes in the dependent variable; that is, we can see how greatly values of the dependent variable differ, given varying scores on the independent variable. Or, (2) we can measure how completely the dependent variable is determined by the independent variable; that is, we can measure the degree of accu­ racy with which scores on the dependent variable may be predicted from scores on the independent variable (1974:105).

The first of these techniques, Shively termed the "effect- descriptive" method, while the second he labeled the "cor­ relational" method. These two procedures can be seen as corresponding to the classes of statistical methods known, respectively, as descriptive and correlational statistics.

Each of these is presented as a class of measures, internally and externally, distinguished by the amount of information about the variables under consideration that they can offer the researcher.

In the preceding discussion of alternative models of j 3

aggregation, it was noted that 50 per cent of the studies

under consideration employed Model IV. The primary limi­

tation of the model which utilized gross electoral returns

for a given aggregate as both the universe and the basic areal

unit involves its restricting the researcher to the compari­ son of descriptive statistics. Researchers employing this model are forced to draw their conclusions solely from compari­ sons of total votes, the percentage of the total vote received

in 1928 and 1930, and changes in each of these figures for various parties within a given area.

Given that Model IV has been the aggregation design most commonly adopted by historians, it is not surprising to discover that researchers from this discipline have most frequently em­ ployed descriptive procedures. Similarly, in light of the com­ paratively recent introduction of statistical analysis into works by historians and the relatively low demand for statistical sophistication among the discipline's students, the finding that those involved in studying the success of the Nazis in the 1930 election have uniformly limited their analyses to the use of descriptive statistics is not a startling one. This latter point is true even of those historians employing aggregation des designs other than Model IV (Table 5.8). it is quite likely that as higher-level statistical analysis takes on a greater role i n the research strategies of historians, this will carry over into their investigations of the 1930 Reichstag election.

Analysts employing the a Iternative I y-offered aggregation schemes which make use of basic areal units, which are 9 4

Table 5.8

Method of Statistical Analysis by Aggregation Model Employed

Statistical Procedures Model

I II IV I & I I 1,11 TotaI & I i I

Descriptive Statistics 31 II 0 0 15 (60.0) (33.3) (100.0) (0.0) (0.0) (68.2)

Cor re I at i ona Statistics 2 2 0 2 1 7 (40.0) (66.7) (0.0) (100.0) (100.0) (31.8)

Totals 5 3 I I 2 I 22 (100.0) 9 5

subdivisions of the larger universe of study^have not been

limited to this class of statistical techniques. Although

researchers working with Models I, II, III or some combina­

tion of these can, and frequently do base alI or part of their statistical analyses on descriptive measures, this has been a matter of choice rather than methodological necessity (Table &■$•) .

While political scientists have been evenly divided in their adoption of particular types of statistical techniques, the most recent studies offered by researchers in this discipline have employed correlation-based procedures (Shively,

1972; Waldman, 1973). The two electoral analyses of the rise of the Nazis undertaken by political scientists which made use exclusively of descriptive measures were both pub- lished thirty years ago (Mellen, 1943; Pollock, 1944 ). A Unlike their counterparts in other disciplines, socio­ logical researchers studying the Nazis' electoral surge in the 1930 Reichstag election have overwhelmingly favored the use of various types of correlation measures. The strong attraction to this category of data-reduction techniques is apparent in the observation that only one of the sociological analyses of the question under consideration failed to adopt some form of correlational analysis (Bendix, 1956).

Although the most recent presentation by political scientists and the majority of those by sociologists have emphasized the greater precision that could be achieved Table > •

Method of Statistical Analysis by Disciplinary Affiliation of Researcher

Statistical Procedu res D i s c i p I ine

History Sociology Political Totals Science

Descr i pt i ve Statistics I 2 I 2 I 5 (100.0) (16.7) (50.0) (68.2 )

Correlational Statistics 0 5 2 7 (0.0) (83.3) (50.0) (31.8)

Totals I 2 6 4 22 (100.0) 9 7

through the utilization of correlation analysis, no consensus

exists regarding the specific procedures within this class

which are most appropriate for the available electoral data.

Heberle (1951:207, 225; 1970:115) argued that the "most

adequate measure" for understanding the relationship between

party support and one or more contextual properties (in­

cluding party support in an earlier election) of areal units

is a rank-order correlation coefficient. Lipset (I960),

whose conclusion that the losses suffered by the centrist

middle-class parties comprised the single source of Nazi

gains in 1930 echoed that of Heberle, similarly employed

rank-order coefficients of correlation in his investigation

of the rise of the NSDAP.

In his critique of Lipset and reanalysis of the same

data, O'Lessker suggested that

if we are to get any picture of where the Nazi gains came from, surely it would be more prudent ___ to use some more powerful, i n c I u s i v e^o p h i s t i c a t e d statistical technique than simple rank-order cor­ relation for two variables only (1968:65-66).

As an alternative, O'Lessker advanced the utility of multiple

correlation. Multiple correI ation/regression, the multi­

variate extension of simple bivariate correlation, is a

technique for the analysis of "the collective and separate

contributions of two or more independent variables ... to l: the variation of a dependent variable (Kerlinger and

Pedhazur, 1973:3). In his attempt to account for variation

in the dependent variable (fhe change in NSDAP support between 1928 and 1930), O'Lessker was thus able to address

several independent variable's (changes in support for the 9 8

Table • i

Findings by Method of Statistical Analysis EmpIoyed

Findings Descriptive Cor re I ationaI Totals Statistics Statistics

Turnout-DNVP-MC Comb¡nation 9 2 I I (60.0) (28.6) (50.0)

Tu rno ut-DNVP Comb¡nation 3 • I 4 (20.0) (14.3) (18.2)

MC-Turnout I 0 I (6.7) (0.0) (4.5)

MC-DNVP 0 I 1 (0.0) (14.3) (4.5)

MC I 2 3 (6.7) (28.6) (13.6)

Not Turnout 2 (6.7) (14.3) (9.1)

Tota I s I 5 7 22 (100.0) 9 9

SPD, the KPD, the Catholic parties, the DNVP, the non-

Catholic middle-class parties, and the voter turnout).

Similar procedures were employed in the more recent

and more in-depth investigation.of this issue by Waldman

(1973). In addition to offering a considerable body of

descriptive statistics and bivariate correlations, Waldman

made use of numerous R's in drawing his conclusions about

the sources of the increased Nazi support in 1930. Through

the use of multiple regression, Waldman, like O'Lessker,

was able to examine the joint and individual effects that each of the independent variables had on the Nazi party's growth in this election.

In spite of some apparent advantages of multiple cor­

relation over descriptive statistics or bivariate correla­ tions in clarifying the relationship between Nazi gains in

1930 and numerous other variables, the use of this technique

is not withoutits critics. Following O'Lessker, Schnaiberg,

1969:732-735) pointed to various difficulties in the inter­ pretation of multiple correlation which had been overlooked

in the former's investigation and which were not addressed

in the later study by Waldman. The main problem with this work centers on the use and interpretation of multiple regression/corre I ation analysis. I n 'particuI ar, there are considerable restrictions on interpretations of stepwise regression analyses such as O'Lessker used. This is especially true when the predictor variables are highly inter- correlated. In such case, part of the explained variance (often the largest part) cannot be parti­ tioned to a single predictor but can only be termed the variance explained jointly by the several predictors. Hence each of the intercorreIated I jO

predictors contributes two ways to the variance explained by the regression: an incremental effect and a joint effect (Schnaiberg, I 969 : 7 32

The incremental effect refers to the unique contribution

made by a given predictor or.independent variable to the

variation in the dependent variable. The joint effect, on

the other hand, serves as a measure of the contribution

made by a single variable in conjunction with the other

independent carriers. Schnaiberg went on to note that "This

latter portion of the explained variance Cthe joint effect]

cannot be assigned to any one of the variables acting

jointly" (1969:732).

The situation, cited by Schnaiberg, in which some or all

of the independent variables included in a multiple regres­

sion solution are highly intercorrelated, is frequently en­

countered in multivariate analysis. This circumstance is

referred to as the problem of muItico I I inearity (Blalock,

1963; Guilford, 1965; Farrar and Glauber, 1967; Gordon,

1968). As suggested by Kim and Kohout, mu I tico I I inearity can create problems with respect to a number of inter­

related aspects of regression analysis.

1. If at least one of the independent variables is a perfect linear function of one or more other independent variables in the equation, the coefficients may not be uniquely determined. Perfect col I inearity would lead to the prob­ lems of a zero divisor. If extreme collinearity exists (intercorre I ations in the .8 to 1.0 range), it may not be possible to invert the correlation matrix of the independent variables. 2. Estimates of the regression coefficients from sample to sample fluctuate markedly. I 0 I

3. One of the uses of multiple regression as an interpretive tool is to evaluate the relative importance of the independent vari­ ables. The situation is somewhat paradoxical, however. The more strongly correlated the independent variables are (excluding, of course, extreme mu 11ico I I inearity which pre­ vents the coefficients from being calculated at all), the greater the intercorreI ation of independent variables, the less the reliability of the relative importance indicated by the partial regression coefficients (Kim and Kohout, 1975:340).

Given the most common generalization of studies d i s-

cussed in the previous section, that correlations between

aggregate or ecological variables frequently are inflated

over the correlation coefficients between variables measured

at the individuaI - I eve I, it can be seen that the problems

of areal analysis and mu Itico I I inearity are not unrelated.

On the contrary, the higher correlation coefficients result­

ing from the use of aggregate data may be at least partial ly

responsible for the occurance of mu Itico I I inearity. Addi­

tional ly, if the researcher merely makes use of regression

analysis in an effort to counter the problem of aggregation

bias, the high intercorre I ations existing between the

independent variable will lead to interpretation problems

that are equally severe.

A number of potential solutions to the problem of multicollinearity can be found in the literature addressed to this problem. Following his critique of O'Lessker,

Schnaiberg suggested that an alternative statistical procedure was necessary for a clear analysis of the joint and direct effects of the independent variables on the dependent variable. Specifically, he undertook the par- I 02

titioning of the total correlation of each party, group of

parties, and turnout with Nazi gains into their direct and

i nd i rect ef fects (Schna i berg, 19 69).

Other responses to the problem of multicollinearity can be seen as focusing on the reduction of the number or

form of collinear variables employed in the analysis. Examples of this type of procedure can be seen in the solutions suggested by Kim and Kohout (1975:341). One approach to dealing with multicollinearity identified by these authors

involves the use of only one of the variables in the highly correlated set to represent the common underlying dimensions.

Alternatively, they posited that col linearity can be reduced through the creation of a new variable consisting of a composite scale of those independent variables which are highly intercorre I a ted. In either instance the resulting or remaining variables can then be used in a regression equation in place of the original variable set.

The technique of cluster analysis can similarly be viewed as a valuable procedure in dealing with the problems I of multicollinearity as well as those associated with the analysis of aggregate data. A further discussion of this technique, which was employed in the current analysis, is presented in the concluding section of this chapter.

Cluster Analysis

Originating in psychology and anthropology in the

|930’s, cluster a ni?ia)y s i s is a class of multivariate tech- I 6 3

niques which has received considerable attention in economics, geography, political science, psychiatry, criminology, mathematics, management and most notably in the taxonomic work in biology. For the most part, however, this procedure has been the subject of relatively little discussion among sociologists. In a recent review of clustering methods aimed at sociologists, Kenneth Bailey offered the following summary of the goals of the various cluster-analytic tech­ niques:

Cluster analysis seeks to divide a set of objects into a small number of relatively homogeneous groups on the basis of their similarity over N variables. Conversely variables can be grouped according to their similarity across all objects. Cluster analysis can be viewed as either a means of summarizing a data set or as a means of constructing a typology (1975:59).

Cluster analysis was employed in the current study as a means of minimizing the data analysis problems outlined in the preceding sections of this chapter. Extending the logic underlying the ■ suggestions of Kim and Kohout (1975:

340) identified previously, cluster analysis can be viewed as a useful technique for overcoming the problem of mu 11ico I I inearity. Its utility in negating the problems associated with mu Itico I I inearity is apparent in Bailey’s description of this technique. Cluster analysis groups variables into clusters or dimensions maximizing within- cluster homogeneity and between-cIuster heterogeneity. The clusters can themselves be treated as variables for further analysis, the effects of the high intercorre I ations between I 04

the a Pr i„ ° r~ i variables being reduced. The confounding Vre.r\\e. erV/iV-. influence of'^mu 11 i co I I i nea r i ty can Thus be avoided.

In addition to aiding in the analysis and interpre­

tation of highly intercorre I a ted variables, cluster analysis

can. also offer some assistance in overcoming the problems

associated with aggregate or ecological data. In the first

section of this chapter, three strategies that have been

offered for dealing with aggregate data were discussed.

Each of these approaches or procedures is quite compatible

with the goals and capabilities of cluster analysis.

The first technique advanced as a solution to the

difficulties encountered in the use of aggregate data in­

volved the use of ecological regression. It has been argued

that, unlike correlation coefficients, regression computed

from aggregate data were not hampered by aggregation bias.

However, as was pointed out above, the interpretation of

regression coefficients may suffer from the existence of

mu Itico I I inearity. When used in conjunction with cluster

analysis the potential power of regression analysis may be

realized. Like factor scores, cluster scores (derived

from the BC TRY (Tryon and Bailey, 1970) system of cluster

analysis employed in this investigation) can be used in

regression equations as independent variables which do not

suffer from the problems encountered in the use of the a.

priori variables.

The second scheme for the use of aggregate data

focused on the utility of factor analysis for overcoming commonly encountered difficulties. As was noted by Hadden 105

and Borgatta ( I 965: 188), factor analysis, by reducing the

original set of variables to a limited number of underlying

factors, can be used to get at relationships which cut

across various definitions of the units of analysis. The

same may be argued for the data reduction techniques associ­

ated with BC TRY. Given Anderberg's (1973:7) assertion

that the Tryon and Bailey technique is essentially an

extension of classical factor analysis, this key benefit

of the latter methodology may be . A ' f' or t'h' e former,

For the present study, however, cluster analysis was

opted for over factor analysis largely because of its ad­

vantages in dealing with the problem of multicollinearity.

The added strengths of cluster analysis are clearly identi­

fied by Bailey in his discussion of the distinction between

the two related procedures.

In cluster analysis we draw boundaries so that each x object Cease or variable] is in one (but only one) group. Thus we meet the typological requirements of exhaustiveness and mutual exclusiveness. In factor analysis we place a factor through a cluster vector; each object is represented by a vector and each factor represents a condensation of vectors. The set of factors is not mutually exclusive and exhaustive. An object can belong to _N oad positively on) more than one factor because the object's variance is divided between factors (Bailey, I 975 : 62--emphasis added).

Thus, while factor analysis and cluster analysis may be equally valuable in reducing a set of (aggregate- or indivi dual-level) variables to underlying or representative dimensions the former is less valuable in overcoming the multicollinearity problem. I 06

The preceding passages from Bailey also point to the

compatibility of cluster analysis with the final strategy

suggested for the analysis of aggregate data. Following

Wenzel’s (1950) lead, ecological data have been viewed as

useful, not for what they can or cannot tell us about the

behavior of individuals, but for the understanding they

can provide about the area, units themselves. A popular

procedure in this type of analysis has involved the con­

struction of typologies of units so that the relationships

between unit characteristics can be examined. . Likening the process to the drawing of maps by cartographers, Bailey noted that clustering can allow the researcher to draw

unit boundaries not merely ,on the basis of location, but on characteristics across a multiplicity of variables.

Following the discussion of the theoretical and methodological issues involved in the investigation of the 1930 Reichstag election offered in this and preceding chapters, it is now possible to turn to the data, analysis, and findings of the current study. I 0 7

NOTES

For a further discussion of non-geographic bases of aggregation, see Duncan, et. al., "Areal Unit as a Collec- t i on of i terns" (1951 :4 f —46) and Dogan and Rokkan, eds. (1961), pa rt i cuIarIy' the editors' "Introduction" and the articles by Allardt and Valkonen I 0 8

CHAPTER SIX

The Da+a Analysis

Given that the BC TRY system of cluster analysis is

a cor re I ation-based multivariate technique, the analysis

process begins with the construction of a correlation matrix of all variables deemed theoretically relevant to the

investigation. The current inquiry began with a matrix of

37 electoral and demographic variables encountered in the prior studies of the 1930 Reichstag election. Included at this stage of the analysis were numerous variables from the 1925 German census covering areal statistics on the degree of ruraIity/urbanity and population dependency on various industria I/occupation a I categories.

The remaining variables considered at this point were all electoral statistics from the 1928 and 1930 Reichstag elections. These included figures on voter eligibility, total turnout, and the precentage of the total vote captured by each of the major political parties, i.e. DDP, DVP, WP,

DNVP, SPD, KPD, and the Catholic parties/ Add i't i ona I I y , in light of the attention focused on the centrist "non-

Ca+holic middle-class" parties in prior research efforts, a summated variable (MC) including al! but the last four groups in the above listing of parties was also incorporated into the analysis I 0 9

A central concern of the present, as well as all prior

studies of this subject, has been the understanding of the

dynamics of the Nazis' rise to power. Since change cannot

be explained^by reference to a constant, the static variables mentioned above are, by themselves, inadequate for a complete

investigation. It was thus necessary to incorporate several change variables into the analysis.This procedure involved creating variables representing the net difference between the 1930 and 1928 values for each of the electoral variables.

In an effort to verify the contention that cluster analysis identifies relationships which are not dependent on particular definitions of the analytical units, variables at two levels of aggregation were addressed in the analysis.

Two distinct data sets, representing values of each of the aforementioned variables for both the WahIkrei s e and Kre i se, were employed to allow for a comparison of results across

I eve Is of analysis.

With this brief overview of the data, it is now possible to turn to the appl¡cation of BC TRY to the analysis of these data. This chapter continues with a description of the technique of variable clustering (V-typing) and a discussion of the specific V-types emerging from this application.

Also included in this discussion will be a comparison of the V-types derived from the data at the two levels of aggre­ gation.

This is followed by the further analysis of V-types through their inclusion as independent variables in stepwise I I 0

regression solutions. Employing the Nazi vote in 1930 and

the change in Nazi vote between 1928 and 1930 as dependent

variables in the regression equations allows for an under­

standing of the relative contributions made by each of the

variable clusters to the Nazis' success.

For each of the Kre i se and WahIkre i se employed in the

analysis, it was possible to compute cluster scores on each

of the dimensions or V—types. These scores are employed

in the identification of object clusters (0-types) for

each aggregation level. 0-typing is essentially a process

of constructing a typology of (areal) units on the basis of

scores on each of the variable clusters.

The chapter concludes with a discussion of the 0-types

derived in this analysis and a specification of the rela­

tionship between the defining characteristics of the object

clusters and the success of the Nazis in the 1930 Reichstag

election. Through the use of tabular analysis^

-it is possible not only to evaluate the various arguments

advanced regarding the Nazis' electoral showing in 1930

but, additionally, to specify the different areas within

the Reich for which alternative explanations may be appro­

priate.

V-typ i ng

Unlike multiple regression analysis, the BC TRY system I I

of cluster analysis is not hampered by the existence of

mu 11ico I I inearity. On the contrary, the presence of high

intercorrelations between variables is a necessary condi­

tion for the successful application of BC TRY (Hindelang

and Weis, 1972:274). Multicollinearity is of particular

importance for the V-typing procedure of BC TRY, cumulative

communality key-cluster analysis, which was employed to

identify clusters of mutually collinear variables among

the original set of 37 electoral and demographic variables.

The extraction of variable clusters through the method

of key cluster analysis operates so as to meet three

specific sets of criteria (Dunn, 1974).. First, all vari­

ables defining a given cluster must be mutually collinear;

that is, they should be highly intercorreIated with one

another and exhibit similar patterns of intercorre I ations

with other variables in the total matrix. Additionally,

they should be relatively independent of variables in other

clusters. Second, a minimal number of clusters must account

for a sufficient proportion (usually 92 per cent) of the

shared variation in the original correlation matrix. Third,

there should be maximum independence of clusters allowing

each to "represent a relatively different proportion of

variation in the total correlation matrix than the other

dimensions" (Dunn, 1974:143).

Key cluster analysis begins with the selection of a

"pivot" variable for the first cluster. In keeping with the above requirements, the pivot variable must have high I 2

correlations with some of the remaining variables and low

correlations with others. The variable selected is the

variable with the greatest variance of squared correlation

coefficients with the remaining (N - I) variables (index of

p i votness).

To build a cluster, the variable with the highest

degree of collinearity with the pivot variable is selected

as the second variable in the cluster. The degree of col­

linearity is measured by the index of proportionality or

index of collinearity (P?), which indicates "the degree to

which the correlations of two variables are consistently

proportional across all the other variables of the study"

(Tryon and Bailey, 1970:48). Tryon and Bailey outlined

the role of this correlation of correlations in the forma­

tion of variable clusters by suggesting that 2 The index P is 1.00 when all their correlations with the other variables are the same; it is .00 when their correlations vary from each other in an un­ systematic way; and the sq'are root P or -J.00 if their correlation p ro f i I e s' a re mirror images. Defin­ ing variables of empirical clusters reveaj, within- group similarity from the fact that the P values between them approach 1.00, but they show between- ■ group differences in collinearity because their Pz values with the defining variables of other clusters are considerably less than unity (1970:48).

Following the selection of the cluster's 'second variable, others that have the highest mean index of proportionality with those variables already in the cluster are added one at a time. Additionally, the P? value of these additional variables with the cluster's deflners must be greater than

.40 for them to become part of the cluster.

To maximize the "tightness" of the cluster, the criteria I : .5

Ko <'<■ for the addition of a variable to a cluster become strict

once the cluster includes four definers. At this point,

Additional definers are included only if their mean index of proportionality is within twice the range of the indexes of proportionality among the four first-selected variables and if all of the indexes of proport i ona I i ty of the variable and the- pre­ viously selected variables are greater than .81 (Tryon and Bailey, 1970;290).

The first cluster continues to grow unti I these requirements cannot be met by any of the remaining variables in the original matrix. The BC TRY version of key cluster analysis then proceeds to extract additional clusters until the three general clustering criteria can no longer adequately be met.

As can be seen from Table 6.1, not all variables are able to meet the rigid requirements for inclusion in a vari able cluster. Those variables whose variation is for the most part unique and fail to contribute substantially to the understanding of the variation of other variables are deleted at this stage of the analysis. Consequently, the cluster structures derived in the current analysis, at both the W a hIk r e i s and Kre i s levels, incorporated only 27 of the original 37 variables. The three change variables deemed to be most important in prior studies of the 1930

Reichstag election, that is, the changes in support for the Nationalist Party, the combined centrist "non-Catholic middle-class parties", and the change in voter participation, were all counted among the definers of a cluster.

Looking first at the analysis of (V a h I k r e i s variables, the cluster structure is made up of six independent V-types Table 6.1

Variables in Final Cluster Analysis

Acronym Variable

AGP* Per cent o f pop u 1 at i on dependent on ,agricultural employment CATH2P Pe r cent o f tota 1 1 928 vote rece i ved by the Catholic parties CATH3P Pe r cent o f tota 1 1 930 vote rece i ved by the Catholic parties DDP2P Per cent o f tota 1 1 928 vote rece ived by the Democratic Party DDP3P Pe r cent o f tota 1 1930 vote received by the Democratic Party DNVP2P Per cent o t tota 1 1 928 vote received by the Nationalist Party DNVPCH Change iin per cent vote tor Nationa 1 ist Party, 1928 to 1930 DVP2P Pe r cent o f tota 1 1 928 vote received by the People's Party DVP3P Pe r cent o f tota 1 1930 vote received by the People's Party DVPCH Change ii-l per cent vote for Peop1e's Party, 1928 to 1930 ELG2P Pe r cent o f population eligible to vote in 1928 election ELG3P Pe r rent o t po p u 1 at i on eligible to vote in 1930 election 1 NDP* Pe r cent o f population dependent on industrial employment KPD2P Pe r cent o f tota 1 1 928 vote rece i ved by the Communist Party KPE3P Per cent o f tota 1 1930 vote rece ived by the Communist Party MC2P Pe r cent o f tota 1 1 928 vote rece ived by +he centrist parties MCCH Change i n per cent votei for centrist parties, 1928 to 1930 MEDP* Pe r cent o f pop u 1 at i on depen dent on employment in medicine SPD2P Pe r cent o t tota 1 1 928 vote rece ived by the Socialist Party SPD3P Pe r cent o f tota 1 1 930 vote received by the Social ist Party TOWNP* Per cent o f pop u 1 at i on living in towns of 5,000 TRADER* Pe r cent o f pop u 1 at i on dependent on employment in trade VOTE2P Pe r cent o f e 1 i g i b I e vote rs casting ballots: in ; 9 2 S VOTEEH Change i n per cent of eligible voters casting ballots, 1928 to 1930 W1RT2P Per cent of tota 1 1 928 vote received by the Economic Party W1RT3P Pe r cent o f tota 1 1 930 vote received by the Economic Party W1RTCH Change i n per cent vote for Economic Party, 1928 to 1930

*Figures from 1925 national census I I 5

(Table 6.2). The first cluster (INDUST) includes nine

collinear variables. The "rational conceptualization" of

this cluster is rather straightforward. An electoral dis­

trict with a high score on the composite of these definers

is characterized by a number of logically as well as empiri­

cal ly related factors. These areas can be defined by a low^

dependency on agricultural employment, a high dependency on trade, medical, and industrial employment, and a high per­ centage of their population in towns.

An examination of the electoral variables included in this cluster is equally informative. Due to the smaller percentage of chi Idren in urban settings at this time, these districts counted among their inhabitants a larger percentage of eligible voters. In terms of party support,

Wah I krei se scoring high on INDUST provided the strongest bases of support for the Communist Party in both 1928 and

1930. This finding is one that would be expected in light of the high degree of industrial employment which charac­ terized these districts.

Conversely, WahIkrei se with a low score on this cluster were primarily rural districts with a high population- dependency on agriculture and low popuI ation-depen dency on other forms of employment. Similarly, agricultural regions tended to provide minimal support for the Communists.

The sec'ond Wa h I k re i s variable cluster (DVPDDP) is defined by the strong support provided to two of the Reich's largest centrist, middle-class parties: the German People's Party Table 6.2

Expanded Cluster Structure (Wa h 1k re i se )

Variables in Oblique Cluster 1 / re1 îa bî 1 ity=.9657a "INDUST"

Oblique F actor Average r Variables Coef f i c i ent^ Communa1 i tyc Within Definers

AGP -.9579 . 9488 . 7892 TOWNP .9368 .9015 .7718 ELG2P . 8809 . 8837 . 7257 ELG3P . 8526 . 8032 . 7024 KPD3P . 8028 . 7039 .6614 KPD2P . 8026 . 6836 .6612 TRADEP . 8009 . 7865 . 6598 MEDP . 7834 . 7094 . 6454 1 NDP . 5966 . 6269 .4915

Va r i ab les in Oblique Cluster 2 / reliability =.9144 "DVPDDP1

Oblique Factor Average r Variables Coe f f i c î en t Communa1 i ty Within Definers

DVP3P 1.0195 1 . 1 5 1 5 . 760 1 DVP2P . 8824 . 8707 . 6578 DDP2P . 6677 . 6628 . 4978 DDP3P . 6055 . 5768 .4514 DVPCH - .5526 .437 1 .4120

Variables in Oblique Cluster 3 / r e 1 i a b î 1 i t y = .9713 "SPDCAT

Obi i que Factor Average r V a r i ables Coe f f i c i en t Communa1 i ty Within Definers

CATH2P -.9456 . 9032 . 8880 SPD3P .9382 . 8988 .8810 CATH3P -.9372 . 8966 .8801 SPD2P .9353 . 8842 . 8783 I I 7

Table 6.2 (conf.)

Variables in Oblique Cluster 4 / re 1 i ab i 1 Îty=.9203 "MCDNVP"

Obi i q ue Factor Average r Variables Coe f f i c i en t Communal ity Within Definers

DNVPCH .9171 1 .0273 .7180 DNVP2P -.7694 . 7400 . 6024 MC2P . 7624 .8170 . 5969 MCCH - .6827 . 5660 . 5345

Variables in Oblique Cluster 5 / re 1 i ab i 1 i ty=. 9250 "WRTSFT"

Obi i que Factor Average r Variables Coefficient Communal ity Within Definers

W1RT2P 1.2127 1.2306 .973 1 WIRT3P . 8245 . 7376 .7153 W1RTCH - .6562 . 4826 . 5693 Variables in Oblique Cluster 6 / re 1 iab i 1 ity =. 9076 "TRN0UT"

Oblique Factor Average r Variables Coe f f i c ien t Communa1ity Within Definers

V0TE2P -.9351 . 9467 .8193 VOTECH .-8 174 . 7406 .7 163

aThe reliability of a cluster is defined as its correlation with a second (unobserved) composite consisting of definers "strictly comparable" to the existing first set. When the comp arab Ie' set is defined by variables collinear with the ob­ served definers, the correlation is called an "interaI-con sis- tency" reliability coefficient (Tryon and Bailey, 1970:58).

bThe oblique factor coefficient is an estimate of the correla- t i on of a cluster domain with the individual variables.

cThe communality of a variable (h2 ) indicates ts shared varia­ t i on with the general domain, it is a number between .00 and 1.00 that measures the geenneeraral lities of individual differences in the variables. The "gap" between the communality and 1.00 represents the uniqueness of the variable, that is, that vari­ ation not shared with other variables (Tryon and Bai ley, 1970: 6 1-62 ) . Since a variable's communality is equal to the estimate of its squared correlation with its domain, some communality estimates, as we I I as some factor coefficients, may occa- sionally exceed 1.00 (Rummel, 1970:318) i I 8

and the German Democratic Party. Areas scoring high on this

V-type provided the greatest support for both the DVP and DDP

in the 1928 parIiamentary election. Additionally, it was

in the districts of greatest DVP strength that the People's

Party witnessed the largest portion of its more than I.I mi I I ion-vote decrease in voter support between 1928 and 1930.

In spite of these losses, however, it was in these same areas that the DVP and the DDP found their greatest 1930 strength.

V-type 3 (SPDCAT) represents a religious, as well as political, dimension. Units with strong support for the

Reich's two Catholic parties, the BVP and Z, were relatively weak in the backing provided to the Socialist Party. On the other hand, areas of Socialist strength were largely Protest­ ant areas in which the Catholic parties found rather small constituencies. While the combined vote for the Catholic parties increased by over 500,000 and the vote for the SPD declined by a like amount in 1930, the changes in the sup­ port for these parties were apparently no more significant in these than in other areas of the Reich.

The fourth variable cluster (MCDNVP) sheds some light on the relationship between the DNVP and the combined centrist vote. High scores for a district on this cluster would indicate a strong degree of 1928 support for the combined middle class parties. Since this variable did not cluster with specific centrist parties included in the analysis

(DDP, DVP, and WP) it can be concluded that these areas ex­ hibited the greatest cross-section of support for each of I i 9

these parties, in addition to the numerous other "non-

Catholic middle-class" parties. Districts with strong centrist-

party support in 1928 were weak districts for the Nationalist

Party during the same year. It was in these areas, however,

that the DNVP, which in 1930 gained support only in Wa hIkre i s

Oppeln, increased its strength, or at least experienced its

smallest losses.

Wahlkreise in which the Nationalist Party was strongest,

however, were those electoral districts supplying the least

backing for the centrist parties. Similarly, these seats of

Nationalist power in 1928 were the districts in which the

DNVP suffered its greatest losses. The centrist middle-

class parties, which found added support in II WahIkrei se

(Frankfurt/Oder, Liegnitz, Oppeln, Merseburg, Schleswig-

Holstein, West f a I en-Sud , Ko I n-Aach.en , Dusse I dorf-West,

Franken, Wurtemburg, Baden), experienced their greatest growth

in these very same areas.

It is unlikely that the DNVP gained many prior sup­

porters of the moderate and liberal middle-class parties since

the only district in which the former improved upon its

1928 showing was also the scene of increased backing for the

latter. On the other hand, it is quite likely that many of the defectors from the National ist Party turned their sup­ port to one of the Reich's many centrist parties.

The fifth variable cluster (WRTSFT) represents the sup­ port, and the change in support, for the Economic Party, the party claiming to represent the Reich's smaI I business interests I 20

and artisans. Electoral districts scoring high on this V-

type would be characterized by the high degree of support

offered to theEconomic Party in both the 1928 and 1930

ballotings. These areas of greatest WP strength were the

same districts in which the bulk of the party's nearly

30,000-vote decline was experienced.

The final- V-type extracted through key cluster analysis

(TRNOUT) includes as its definers variables related to voter

turnout in 1928 and the change in participation by the elec­

torate occurring between 1928 and 1930. A high score for an

areal unit on TRNOUT can be seen as an indication of a high

increase in the percentage of eligible voters casting ballots

in the 1930 election. it is interesting to note that in the

previous election, these areas were characterized by the

lowest rates of voter participation. Conversely, areas with

higher turnout rates in 1928 tended to experience less sig­

nificant increases in these rates in the subsequent electoral

contest. Thus, given that the growth in turnout was most

substantial where it had previously been weak, the frequently- offered contention that the Nazis, or something else, stirred the previously apathetic to action appears, at this stage of the analysis, to be a potentially fruitful hypothesis.

Turning next to the cluster structure derived from data at the Kre i s level, one again encounters six variable clusters

(Table 6.3). Not only were six V-types extracted but their definers were exactly the same variables as the defining variables of the Wahlkreis V-types. I 2 I

Tab I e 6.3

Expanded Cluster Structure (Kreise)

Variables in Oblique Cluster I / re I iabi I ity=.9 I 92 "INDUST"

Obi i q ue Facto r Average r Variables Coe ff i c i ent Communa1 i ty Within Definers

AGP -.9659 . 9397 . 7055 ELG2P .7810 .6397 .5704 TRADEP . 7786 .6 196 . 5687 TOWNP . 7475 . 6087 . 5459 ELG3P . 7448 .5815 . 5440 1 NDP . 7027 .5373 .5132 KPD3P . 626 1 .427 1 . 4573 MEDP .6142 . 4498 . 4486 KPD2P .6125 .4011 . 4474

Variables in Oblique Cluster 2 / re 1 i a b i 1 i ty = .8805 "DVPDDP"

Oblique Factor Average r Variables Coefficient Communa 1 i ty Within Definers

DVP2P . 8530 .7415 .6504 DVP3P .8418 .7116 .6419 DVPCH -.7832 . 625 1 .5972 DDP3P . 7063 .5316 .5386 DDP2P . 6280 .4169 . 4789

Variables in Oblique Cluster 3 / reliability^ .9328 "SPDCAT"

Obi î que Factor Average r Variables Coefficient Communa 1 i ty Within Definers

CATH3P -.9027 .8312 . 7885 CATH2P - . 8990 . 8232 . 7853 SPD3P . 8853 . 8036 . 7733 SPD2P . 8070 . 6632 . 7049 I 2 2

Table 6.3 (con t.)

Va r i a b les in Oblique Cluster 4 / re 1 ia bi 1 ity=.8909 "MCDNVP"

Oblique Factor Average r Variables Coefficient ■ Commun a 1 i ty Within Definers

DNVPCH .9128 . 9235 . 6980 MCCH -.7692 .6425 .5882 DNVP2P -.7597 . 6927 . 5809 MC2P .6167 . 5956 .4715

Variables in Oblique Cluster 5 / reliability^ .8876 "WRTSFT"

Obi i que Factor Average r Variables Coefficient Communal i ty Within Definers

W1RT2P 1 .3543 1 .6898 1.0610 W1RT3P . 5438 . 3735 . 426 1 W1RTCH - .4523 .2368 . 3544

Variables in Oblique Cluster 6 / reliability^,.9766 "TRNOUT"

Oblique Factor Average r Va r i ables Coe f f i c i en t Communa1 i ty Within Definers

VOTE2P -.9995 1 . 0079 .9717 .’OTECH . 9449 .9017 .9187 I 23

In spite of this general similarity, however, some dif­

ferences did exist between the two levels of aggregation.

First, on all but variable cluster TRNOUT, the reliability coefficients for each of the county-level V-types were lower than those for the larger units. But, in each case the coefficient was at least .88, a figure considerably higher than most reported by Tryon and Bailey (1970:57-58) and all cited by Hindelang and Weis (1972:10).

Second, with the sole exception again being TRNOUT, lower values characterize the other statistics describing the

Kr e i s V-types. In general, most of the factor coefficients, communal¡ties, and average correlations- within definers on the five remaining variable clusters are moderately lower for the county-level clusters. These values were sti I I suf­ ficiently high to pass the stringent BC TRY requirements for inclusion in the cluster.

Third, whi le the same variables were included in each matched cluster, the relative contributions of many of these variables to the empirical definitions of all but two V- types (WRTSFT and TRNOUT) are different for the two types of a reaI units.

To enable investigators to make a more systematic comparison of dimensions discovered in different groups than is possible from the mere "face-value" comparison offered above, BC TRY includes component programs to be utilized in this capacity. The methodologies of these components and the conclusions regarding the stability of cluster structure 124

across units of analysis are presented in the following section

Units of Analysis: WahIkre i s and Kre i s V-type Comparison

A primary objective of the "comparative dimensional

analysis of variables" is to describe the similarity of clusters

extracted in the key cluster analysis of different groups. In

BC TRY, comparisons of dimensions across different groups are

made with the aid of the "index of similarity" or the cosine®

^jthetajbetween the two dimensions. Tryon and Bailey explained

the evaluation of this comparison process by suggesting that

the reasoning by which we designate two dimensions as identicaj is based on the universal logic by which we conceive any two entities as being the same, namely, that they show the same pattern of "observations" in relation to a common set of other "referent entities." . . . The index of pattern similarity of any two entities on a common set of referent entities is P Qthe index of pro­ portionality] . . . The value of the index of similarity cos Q of any two dimensions in different groups is a simple quadratic function of P ( 1970 : I 90- 19 1).

Thus, the basic process of comparative dimensional analysis

is analogous to the method of key cluster analysis employed

in the discovery of V-types for which a comparison is sought.

The former process, however, differs from key cluster analysis

in two respects. First, rather than employing the inter-

correlation matrix of original variables, comparative analysis

is based on the intercorrelation matrix between the original

variables and the V-types derived from each of the two groups.

Second, the correlations between clusters are defined

somewhat differently than those between the original variables. 125

To identify the similarity between the V-types, comparative

analysis compares the factor coefficients of dimensions within

each group rather than the correlation coefficients which were

compared in key cluster analysis.

The cluster structure in the Wa hIk re i s and Kre i s com­

parative analysis yielded five variable clusters which can

be interpreted in much the same way as the V-types. In each

instance, the dimensions consisted of matching pairs of V-

types from each of the levels of aggregation.

The first cluster was made up of not one, but two pairs

of V-types. The WahIkre i s and Kre i s clusters INDUST and

DVPDDP were included in this cluster indicating a continuity

of cluster structure across levels of analysis. The inclusion

of the two pairs of clusters suggests some association between

urban/industria I characteristics and support for the People's

and Democratic parties. Cumulatively, the two clusters at

each level of aggregation represent 14 of the original vari­

ables. This feature and the comparatively low average cor­

relations with other cluster definers for each of the dimen­ sions in this cluster would seem to suggest that the degree of homogeneity of this dimension is somewhat less than that of the other four clusters.

Each of the remaining clusters consists of a single V- type from both the electoral district and county levels. The

SPDCAT, TRNOUT, MCDNVP, and WRTSFT V-types defined in the key cluster analysis were repeated in the comparison structure.

From a cursory comparison of cluster structures and a I 26

Table 6.4

Expanded Cluster Structure (Wah Ikreis and Kreis Comparison)

Variables in Oblique Cluster / re I i ab i I i ty=.897 1

Obi i que Factor Average r Va r i ables Coefficient CommunaI ity Within Definers

(K) 1 NDUST . 8680 .8194 .6936 (K) DVPDDP . 7908 .6731 .6320 ( W) DVPDDP . 7779 .6943 .6217 (W) INDUST . 7597 .6411 .607 I

Variables in Oblique Cluster 2 / rei i ab i I i ty=.9884

Obi i que Factor Average r Variables Coefficient Communality Within Definers

(W) SPDCAT .9932 .9956 .9771 (K) SPDCAT .9745 .9589 .9587

Variables in Oblique Cluster 3 / re I iab i I i ty=1 .02 I 3

Obi i que Factor Average r Variables Coefficient Communality Within Definers

(W) TRNOUT I . I 735 I.3874 1.1746 (K) TRNOUT .8283 . 6963 . 8290

Variables in Oblique Cluster 4 / re I i ab i I i ty=.9545

Oblique F actor Average r Variables Coefficient CommunaIity Within Definers

(W) MCDN-VP . 9976 I .0008 .9495 (K) MCDNVP .9060 . 8265 . 8624

Variables in Oblique Cl uste r 5 / re I i ab i I i ty=.8778

Obi ¡que Factor Average r Variables Coe f f i c i en t Commun a I i ty Within Definers

(K) WRTSFT .9517 .9137 . 8360 (W) WRTSFT .8053 .6565 . 7074 127

more systemat i c comparative dimensional analysis of variables,-

it is possible to conclude that a great degree of uniformity

exists between the results of the WahIkre i s and Kreis V-1 y p i n g

processes. Whi le some differences do exist, they seem insig­

nificant when compared with the large number of parallelisms

exhibited between the two sets of variable clusters. Thus,

the previously-stated premise that the BC TRY system of cluster

ana lysis, like factor analysi s , cou Id be a useful methodology

for uncovering relationships which cut across levels of

ana lysis appears, at least in th i s case, to have been borne

out

While the derived V-types provide some information about

the associations between variables at both the county and

electoral district levels, the impact of these dimensions on the rise of the Nazis has yet to be considered. The fol­

lowing section addresses the contributions made by each of these

variable clusters to the 1930 electoral success of the NSDAP. 128

V-types and Nazi Support: Multiple Regression of Wahlkreis and Kre i s Variable Clusters

Since one of the primary reasons for the employment of

BC TRY in the current study was to minimize the problem of

rnu I t i co I I i n ea r i ty between independent variables, some atten­ tion should be paid tp the intercorre I ations between the clusters derived through key cluster analysis. Beginning again with the Wahlkreis V-types, Table 6.5 indicates that whi le each of the V-types exhibits some degree of inter­ correlation with each of the other variable clusters, most of the coefficients are quite small. In no case does the 2 r~ between any two clusters exceed .4, with the r exceeding this value in only three instances.

A comparison of these values with the "average r within definers" column of Table 6.2 suggests that the "maximum within-cIuster, minimum between-cIuster" collinearity cri­ terion of BC TRY has been met. The condition of high col­ linearity, originally necessary for the clusters to be derived, does not exist between the Wahlkreis V-types.

Intercorrelations between the Kre i s V-types are 2 similarly low. The highest r , slightly above .3, is once again below the level of high or extreme mu Itico I I inearity identified as making the use and interpretation of multiple regression a hazardous undertaking.

Noting that in multiple regression concern is with explaining a single dependent variable with respect to a Table

CORRELATIONS BETWEEN OBLIQUE CLUSTER DOMAINS (Wahlkreise)

1 NDUST DVPDDP SPDCAT MCDNVP • WRTSFT TRNOUT

1 NDUST 1 .0000 . 4444 .2922 -.0067 . 1 230 -.1131

DVPDDP . 4444 1.0000 . 6006 . 3427 . 1223 . 0339

SPDCAT . 2922 . 6006 1 .0000 - . 0343 .3018 - . 5295

MCDNVP • - . 0067 . 3427 -.0343 1.0000 -.0023 . 3024

WRTSFT .1230 . 1 223 .3018 -.0023 1 . 0000 -.2155

TRNOUT -.1131 .0339 -.5295 . 3024 -.2155 1.0000

DOMA 1 N ..9827 .9562 . 9855 .9593 .9618 .9527 VAL 1D1 T 1ES OF CLUSTER SCORES3

An area's observed clus+èr score inevitably suffers from limitations of domain sampling.- The domain validity coefficient is a correlation coefficient of the observed scores with domain scores that would be earned by the areas with the inclusion of an indefinitely large number of variable-characteristics, all I

equally representative of (collinear with the definers of) the domain 2 (Tryon and Bailey, 1970:56). 9 Table

CORRELATIONS BETWEEN OBLIQUE CLUSTER DOMAINS (Kreise)

1 NDUST DVPDDP SPDCAT MCDNVP WRTSFT TRNOUT

1 NDUST 1.0000 . 5542 . 4005 . 1 434 . 3447 . 0008

DVPDDP . 5542 1.0000 . 5254 .3019 . 1 784 . 0892

SPDCAT . 4005 . 5254 1.0000 - .0544 .3512 -.01 19

MCDNVP . 1 434 .3019 -.5044 1 . 0000 .1165 .0914

WRTSFT . 3447 . 1 784 .3512 .1165 1.0000 -.0195

TRNOUT . 0008 . 0892 -.01 19 .0914 -.0195 1.0000

DOMA 1 N . 9588 .9383 .9658 .9439 . 942 1 . 9883 VALIDITIES OF CLUSTER SCORES I

3 0 I

number of independent variables, the six collinear variable

clusters derived from key cluster analysis were introduced

into stepwise regression equations as the independent vari­

ables. With a relatively successful reduction of the prob­

lem of mu 11ico I I inearity, it should be possible to identify

the contributions to the variation in the dependent variable

attributable to the six dimensions.

Two related but conceptually distinct variables were

addressed. These represent, in the first case, the per­

centage of the total vote received by the Nazis in 1930,

end in the second case, the net change in the percentage of

the total vote received by the Nazis between 1928 and 1930.

The r’s between these variables at the Wahlkreis and Kre i s

levels were .92 and .89, respectively. These high correla­ tions between the dependent variables provide some indica­ tion that the regression solutions for each of these variables will most likely be quite similar.

One is reminded that ecological regression has been advanced as a valuable means of overcoming the aggregation bias associated with the use of correlation coefficients.

Specifically, attention must be paid to the values of the regression coefficients (b ) . ^ Regression coefficients

indicate how much change in the dependent variable results from a given change in a single independent variable (V-type) when alI other variables are held constant.

As was pointed out by Shively (1969), grouping by geographical location or any other means will yield I 52

aggregations approximating one (or a combination) of three

situations. First, individual units may be grouped in a

manner that is related to neither their X- nor Y-values.

In this aggregation process,- knowledge of a individual- or areal-unit's score on either the dependent or independent variable would not assist in the prediction of the aggrega­ tion in which it is found.

Second, the grouping process could revolve around the unit's score on the independent variable (X), independent of dependent-variabIe scores. In this situation, the knowl­ edge of a unit's X-score would aid in the prediction of the aggregation into which it falls. Knowledge regarding the unit's score on the dependent variable, however, is not

likely to assist in this task.

Third, grouping can derive from the unit's score on the dependent variable (Y) , independent of its score on X.

In this case, a unit's score on Y would contribute to a prediction of the unit's aggregate, while knowledge of its

X-score would not.

Shively further posited that the ecological regression techniques of Blalock (1965) and Goodman (1959) were both based on the abi I ity to meet one specific assumption about the nature of the aggregation process.

Blalock ¡^explicitly and Goodman, more implicitly, showed] that for two "ecological" variables, X and Y, if we use as our measure of relationship the slope of the regression line rather tian the cor­ relation coefficient (which measures the spread of the data around the regression line), then we need make fewer assumptions to infer the individual- I 3 3

level relationship from the aggregate-I eve I relationship. . . . b (the regression coefficient for X and Y among the aggregates) tends to equa I bj (the regression coefficient for X and Y among individuals), when the individuals are "grouped" into aggregates in a way which is related to their scores on X, that is, when they are grouped by the independent variable . . . But if the individuals have been grouped by the dependent variable, b^ will not tend to equal b| (Shively, 1969:1186).

Thus, the only assumption which must be met in order safely to make cross-level inferences is that the aggrega­ tion process itself does not create a direct relationship between the value on the dependent variable and the specific aggregation into which a smaller unit or individual falls

(one must assume situations one or two above). Any effect resulting from the aggregation process must be indirect through the scores on the independent variable. Any dif­ ferences between cross-level b's may be seen as functions of the degree to which this assumption has been violated.

The use of comparable data sets at two levels of aggregation should offer greater security in guaranteeing the adequacy of this assumption. If the coefficients at the two aggregation levels are similar, the evidence in support of the view that the aggregation process had no direct effect on the dependent variable, is stronger.than if it were necessary to work merely from one set of aggregate data . I 34

Given that the value of b, as an estimated coefficient,

is subject to some degree of error, it is uni ike I y that the

b's will be exactly the same for each level of aggregation.

With the aid of the "standard error b", it is possible to compute the confidence I imits into which the true b's would

fall. Through a comparison of these ranges across levels,

it should then be possible to locate the best estimate of the range of the individua I - IeveI coefficient. Depending on the similarity of the regression coefficients across aggre­ gates, this estimate of the upper and lower limits for

individuaI - IeveI values would be derived in one of two ways.

First, if the limits at each of the aggregate levels exhibit some degree of overlap, this shared range can be taken as the best estimate of the limits of the individual- g level coefficients. Second, if the aggregate-I eve I con­ fidence limits fail to overlap, the coefficient with the smallest absolute value will be the best estimate of the individuaI - I eve I b. This latter point was illuminated by

Shively (1969:1192-1196) in his demonstration that negative ecological regression coefficients wi I I tend to be either unbiased or negatively biased and positive coefficients will yield either an unbiased or a positively biased estimate of the individua I - IeveI b.

With the detailing of these procedures, it is possible to return to the data under consideration. Tables 6.7 and

6.8 present the values of b, standard error b, and the upper and lower limits for each of the variable clusters I .3 5

wi+h Nazi vo+e in 1930 and Nazi gains in 1930, respectively.

Table 6.9 represents a more compact summary table providing

the limits for the individuaI - IeveI estimates of b for

both of the dependent variables. As was noted previously,

the correlations between the two dependent variables were

quite high at each aggregation level. Similarly, after

comparing the ranges of the estimates derived for each of

the dependent variables, it is clear that these values are

also comparable. That is, the contribution made by each

of the variable clusters to the variation in the dependent

variable does not differ significantly when our attention

shifts from one dependent variable to the other. This leads

to the conclusion that those factors which accounted for a

strong Nazi showing in 1930 were the very same factors that

contributed to major increases in the Nazi vo+e between the

1928 and 1930 ballotings.

From Table 6.9 it can be seen that the most substantial

contribution to the 1930 Nazi vo+e and vote-gains was made

by variable cluster SPDCAT. V-type SPDCAT, it should be

recalled, included variables reflecting the extent of 1928

and 1930 support for the Socialists and the Catholic parties.

Areas providing substantial support for the SPD were low on

Catholic strength and, conversely, Catholic party strongholds offered little assistance to the Socialists in their s+ruggIe to retain power. Whi le this association may indicate that

some of the nearly 600,000-vo+e decline for the Socialist Table ,■

Comparison of 'Wahlkreis and Kreis Regression Coefficients (Nazi Vote in i 930)

V -ty pe Kreis Wah i krei s

b standa rd 1ower-upper b standard 1 o ’a o r - u p p e r error b i i m i t ' b error b 1 i m i t ' b

SPDCAT . 349 . 029 E.320,.378] .220 .088 E • 1 32 , . 30 o

DVPDDP .19 1 .030 L . 1 61 ,.221] . 123 .084 E.039,.20’]

MCDNVP -.173 . 025 L-. 198,-. 148] -.161 .06 1 E-.222,-. I 0'E

WRTSFT . 1 33 . 026 E. 1 07, . 133] .082 . 058 E . 0 2 4, . ,40]

1 NDUST -.215 . 029 E-.244 ,-. i 86] -.181 .06 1 E- . 242 , - . 1 20.,

TRNOUT .046 . 024 E.022,”.070] .059 .074 E-.0 1 5, . 133] Table fc/.S

Comparison of Wahlkreis and Kre i s Regression Coefficients (Nazi Gain in 1930)

V-type Kre i s Wahlkreis

b standard 1ower-upper‘ b standard lower-upper error b 1 i m 11 ' b error b limit b

SPDCAT .317 . 023 E.294,.340] 2 1 5 .080 E. 1 35 , .295]

DVPDDP . 202 . 025 E. 177, .222] 1 23 .076 E. 047, . I99j

MCDNVP -.164 . 020 E- . 1 84 , - . 1 44] 1 90 .056 E-.246,-. . ;

WRTSFT . I 30 . 02 1 E. 109,. 151] 076 .052 E. 024,. i 2 8]

INDU ST - . 224 .024 E-.248,-.200] 1 46 .055 E-.20 1 ,-.09 ■ ]

TRNOUT . 02 I .019 E. 002,.040] 058 . 067 E-.009 , . 125] Table

Estimates of Individua I - IeveI Regression Coefficient Ranges (Kre i s/Wahlkreis Overlap)

V-type Nazi Vo+e Nazi Gain in 1930 in 1930

SPDCAT C.132,.308]* E.294,.295]

DVPDDP E . 1 6 1 , .207] E.177,.199]

MCDNVP E-. I 98,-. 148] E-. 184,.-! 44]

WRTSFT E. 107, . 1 33] E.109,.128]

i NDUST E-.242,-.186] E-.20 1 ,-.200]

TRNOUT E.022,.070] E.002, .040]

* Vimits for Kreis and Wahlkreis did not overlap. The estimate is based on Wah1kreis-1 eve 1 variables' which yielded the smallest b-value. Party between 1928 and 1930 may have been picked up by

the Nazis, the total number of such defections could not

have been that large. It should be recaI led that whi le the

variable "change in the percentage of total vote for the

Socialists between 1928 and 1930" was introduced as a vari­ able at the initial stage of key cluster analysis, the relationship between this variable and general SPD strength was sufficiently weak for it to be excluded from the cluster.

Thus, at least with respect to 1928 and 1930 Socialist Party strength, the change in SPD vote was essentially random.

The religious implications of this dimension undoubtedly provide a more fertile ground for the cultivation of an understanding of the association between V-type SPDCAT and the 1930 Nazi vote and the 1928-1930 vote gains. Most notably, as was pointed out in prior research efforts into this question, the locus of the apparent association between support for the Socialists and support for the Nazis -was their common appeal to Protestant voters or at least to non-Catholics in predominantIy Protestant locales.

The negative b's between the dependent variables and

V-type INDUST similarly reaffirm the oft-noted view that the 1930 election demonstrated the success of the NSDAP's campaign to capture the rural vote. Thus, not only did the 1930 balloting strength of the Nazis represent a

Protestant vote, but most frequently it signal led a vote of rural Protestants. these observations tend to be quite I 40

consistent with the conclusions drawn in virtually all prior

studies of the rise of the Nazis.

Only one other V-type (MCDNVP) resulted in negative

regression coefficients with the two dependent variables.

The cluster score on this dimension was high for areas with

high centrist party support in 1930. Additionally, these

areas registered the highest losses for the centrist parties

between 1928 and 1930. The DNVP support in these areas was

low and they generally were the scenes of more I imited

Nationalist Party losses, and in one instance, of an increased

support for the DNVP. The negative b on this variable sug­

gests that this complex of characteristics tended to signal

weak support for the Nazi Party in the 1930 Reichstag

election. This means that while the combined middle-class

centrist party loss was not a significant factor in the 1930

showing of the NSDAP, the deci ine in support for the

Nationalist Party in previous DNVP strongholds was important

in the success of the Nazis.

The combination of the Nationalist Party and the centrist

middle class parties within this single variable cluster,

however, may lead to an understatement of the contribution

that each of the factors made to the success of the Nazis.

This can be seen through an examination of two additional

V-types. Variable clusters DVPDDP and WRTSFT yield regres­ sion coefficients strong enough to challenge the view that these parties made no contribution to the success of the i 4 I

Nazis. The first of these represented the 1928 and 1930 seats of power for the German People's and the German

Democratic parties. The second of these V-types represented the 1928 and 1930 power bases of the Economic Party which also witnessed the most severe 1928-to 1930 losses for the party. It is possible that the complementary nature of

Nationalist and middle-class party support minimized the relative impact of each of these sources exhibited through the values of b. A more in-depth examination of the relative contributions made by these sources is provided in the next section, fol lowing the consideration of one final V-type.

The last variable cluster to be examined is V-type

TRNOUT. It will be recalled that areal units scoring high on this V-type witnessed strong increases in voter parti­ cipation in the 1930 Reichstag election. Similarly, these units tended to be areas which brought out a relatively weak showing in the 1928 contest. The TRNOUT coefficients for each of the dependent variables were clearly the weakest to be found, showing values only slightly above zero. Thus, whi le at this stage of the analysis, it appears that the

Nazis may have succeeded in 1930 at the expense of the

Nationalists and the centrist middle-class parties, the frequently-encountered view that the NSDAP was also the recipient of support from the large number of new voters in I 9 30 is somewhat doubffu I .

Further clarification of each of these arguments can I &?

be provided by once again turning Io ¡he BC TRY cluster

analysis system. Following this discussion of variable

clustering, attention should now be paid to fhe other major

technique of this methodological system, fhe cl uster i ng

of objects.

O-typ i ng

|n the introduction to cluster analysis offered in the

last chapter, two alternative modes of clustering were

identified. While one procedure is concerned with the grouping of variables according to their similarity across all objects, the other focuses on the grouping of objects on the basis of their similarity across all variables. The former technique, in the BC TRY system referred to as V- typing, was discussed in the preceding sections of this chapter. The latter technique, known in BC TRY as O-typing is fhe subject of the remainder of this chapter.

Object cluster analysis is a technique for the con­ struction of typologies of individuals, objects, or areal units on the -basis of their similarities and/or differences across the various dimensions or V-types derived from key cluster analysis. The benefits of typology construction, and hence O-typing, were clearly stated by Tryon and Bailey

There are a number of reasons why typologies are desirable: (I) Since a particular type in­ cludes many Eunits], a considerable amount of information erived from . . . observations on the Eunits] accumulates to I he type. Any Ecounty or electoral district that] fits the type can be better understood than if no such cumulative information were available. (2) A given type I 4 3

possesses special characteristics that differen­ tiate it from other types; hence, the strengths and weaknesses of its members can be conceptualized in terms of the distinctive high and low elevations of their profile of characteristics. (3) Among members of a given profile type the absolute level of Ea county's or electoral district's] score on any particular characteristic can be properly assessed in the light of high and low elevations on other attributes of Ei+s] profile . . . (4) Since a given type describes a collec- tion of Eunits] that have the same standing on multiple attributes, other behavior characteristics of these Eunits] are better predicted than would be the case if prediction were based only upon standing in one general attribute (Tryon and Bailey, 1970:135-136 -- emphasis in original).

The initial step in the 0-type or typology construction

procedure involves the computation of scores for each unit on each of the six dimensions defined through key cluster analysis. These scores, known as cluster scores, reflect a simple additive composite and are computed by summing the standard scores for a particular areal unit on each of the variables making up the dimension under considera­ tion. To facilitate the making of comparisons of profiles across dimensions, BC TRY transforms the cluster scores into standard scores with a mean value of 50 and a standard devi­ ation of 10. Following the calculation and standardization of cluster

scores, individual units are temporarily assigned to core object

clusters based on an arbitrary partitioning of the cluster

score space. The number of sectors (S) is a function of the

number of dimensions (D) and the number of categories (C) that

the researcher wishes to create for each of the dimensions.

The formula S=C^ represents the relationship between these variables. Given this formula, the division of each of the six dimensions under consideration into two, three, or four cate­ gories, for example, would result in the creation of 64,729 or

4096 logical possibilities of 0-types. While it may be desirable to subdivide a dimension into a greater number of categories to maximize discrimination, the large number of sectors derived from six dimensions with even a limited number of cutting points makes clear the unwieldiness of such an under­ taking.

The next step involves the sorting of individual units into the sectors in cluster score space. At this point al I of the arbitrary 0-type sectors for which there are no empirical counterparts are eliminated from the list of core

0-types. Types which do not include a minimum number of cases are similarly deleted with an attempt being made to reassign the units that they had included to one of the 0-types in the reduced list. The general procedure for doing this involves computing for. each of These cases the distance from the remaining core 0-types. Each case is then assigned to the object cluster from which it is least distant. I 45

The determination of The distance between a unit and

an O-type begins with the computation of the euclidean

distances (D) between the unit and each of the current

members of the O-type. The measure D is simply the square

root of the sum of the squared deviations on the dimen­

sions. Given that the magnitude of D is a function of the

number of dimensions involved, the square root of the mean

square distance in cluster scores (RMS) is a more useful

index of the distance.

The O-typing routine is an iterative procedure and as

was noted by Dunn,

Often, assigning objects to types changes the structure of a type slightly so that some other objects are deleted. This process is referred to as "wandering". In some cases, changes in membership promote convergences, mergers, or deletions of whole types, especially as the number of objects in a type approaches the specified 2 per cent lower bound. After successive iterations, this wandering settles down, and in fact, the procedure is terminated when no changes, in O-type membership occur from an iteration to the next (1974:172-3).

in some situations, certain cases are characterized by

such unique profiles that their assignment to any O-type

will destroy the multidimensional homogeneity of the cluster.

This would have the further effect of negating the primary

benefits of the technique of typology construction. In

preparation for this circumstance, BC TRY sets a minimum

criterion on which to decide whether a unit or individual

should be excluded from any O-type. If the RMS of a unit's cluster scores from those of the object cluster exceeds a I 4 6

single standard deviation, it will not be included in the 0-

type. Thus, alI cases are assigned to one of the core O-types

or are defined as unique'cases ("rejects") which cannot be

identified with an object cluster.

As with the key cluster analysis which determined the

initial variable clusters, the object cluster analysis

routine facilitates the refinement of solutions by providing

a number of statistics describing the O-types. These

measures, inc.luding the mean and standard deviation of each

0-type on each cluster dimension, the homogeneity (H2) of

each 0-type on each dimension, the homogeneity of each 0-

type across all dimensions, the euclidean distances between

O-types, and an index of correlation (eta) across alI of the

O-types for each dimension, are also necessary tools in the substantive interpretation of the object clusters.

A brief discussion of some of these statistics may be of some assistance in evaluating the core O-types derived in the current investigation. While the mean and standard deviation of each 0-type on each dimension should be self- explanatory, the other statistics require some explanation.

The homogeneity of an 0-type is a measure of the tightness of the profiles of individuals that compose a given type.

The homogeneity is defined as a function of variation in an 0-type's membership in relation to the total variation 2 of alI objects or units. The value of H varies from 0.00 to 1.00, approaching unity when the variation within an

0-type is non-existanr. In this instance, the members of an 0-type will be almost exactly like one another with low 147

standard deviations and high euclidean distances between 2 0-types. In the opposite instance, with the value of H

approaching 0.00, the members tend to be more like all other

units, regardless of 0-type (Dunn, 1974:176).

The measures of overa I I homogeneity and eta both

involve the mean homogeneity across dimensions and 0-types.

The overall homogeneity is defined as the mean homogeneity of

each 0-type across all dimensions. Conversely, eta is

defined as the mean homogeneity of each dimension across

all 0-types (Hindelang and Weis, 1972:293). Additionally,

the square of eta can be taken as an average of the squared

H values with each H being weighted by the proportion of

cases in the particular 0-type.

The preceding discussion provides The rudiments of the

BC TRY O-typing procedure. Following this introduction to the methodology and the criteria for its evaluation, attention can be paid to the application of this technique in the current analysis.

Wahlkreis and Kre i s 0-types

In the present study, the cluster scores for each unit at the Wahlkreis- and Krei s-levels were employed to derive core

0-types. Data for each level were analyzed with two, three, and four cutting points. The resulting solutions wer e then rein- troduced into the BC TRY 0-type procedure for an ove ra I I estimate of the best solution regardless of the specific i 4 8

number of cutting points.-While the minimum number of cases

required to make a core O-type can be defined by the researcher,

the BC TRY default on this parameter -- the unrounded integer

corresponding to two per cent of the total, or two, whichever

is greater -- -was selected.

In the case of the Kre i s data, this resulted in the Identi­

fication of seven core 0-types. The membership figure for

these object clusters ranged from a low of 76 to a high of

228. Similarly, the N of 1062 Kre ise produced only ten rejects.

Table 6.10 provides the complete membership figures, as well as

figures on the unidimensional and overall homogeneities and

the index of correlation, eta. Al I 0-types contain a minimum

of just under eight per cent of the total number of county-

level units. At the same time the homogeneities were all

quite substantial. The lowest within O-type single dimension 2 value of H is a rather high .85. Furthermore, none of the

overall homogeneities and only one eta coefficient fails to

exceed .90. From these figures it is clear that the results

obtained from the O-typing procedure are more than satisfactory

for a meaningful analysis.

Table 6.11 provides the means for each of the Kreis-

level 0-types. The apparent continuities and discontinuities

hinted at in these figures are somewhat clarified by an examination of figure 6.1. This graphic representation of the seven county-level 0-types plots the mean values of each Table \ f0

HOMOGENEITY OF KREI S O-TYPES (7 O-Types)

INDUST Type DVPDDP SPDCAT MCDNVP WRTSFT TRNOUT OVERALL NUMBER OF HOMOGENE 1TY MEMBERS 1 . 9587 . 9047 . 8587 . 9277 .9346 .9738 . 9289 1 5 1 2 . 9444 .9138 . 9243 . 8686 . 8870 . 9843 .9217 204 3 . 8572 . 8836 . 9298 . 9.396 . 8280 . 9800 . 9087 1 44 4 .9555 . 9570 . 9298 . 8950 . 9239 . 9834 .9411 228 5 .8719 . 8952 . 9438 . 967 1 . 9058 .9719 . 9299 1 33 6 .9213 .9374 . 9295 . 9607 . 8569 . 9862 .9359 1 1 6 7 . 8696 . 8857 . 8845 .9553 .8618 .9615 . 908 1 76 1052 Eta . 9207 .9163 .9174 . 922 1 .8917 . 9790 10 Rejects Table vj i \ \

MEANS OF KRE1S O-TYPES ON VARIABLE CLUSTERS (7 O-Types )

O-Types INDUST DVPDDP SPDCAT MCDNVP WRTSFT TRNOUT

44.928 54.560 52.553 59.029 44.250

c 52.385 \ l 43.778 46.452 55.775 37.732 48.524 r 48.872 e

54.248 52.168 57.338 54.833 67.1 44 49.806 x r 40.819 40.050 35.991 52.129 43.689 50.583 5 65.050 55.832 56.839 50.033 50.331 49.087 6 57.375 45.979 44.044 49.210 51 . 727 50.689 7 57.301 71.866 54.962 54.820 46.509 51.787

o ÌCUC: Vv?f>ù? . vBcòMv? <ù£.TSer >uR^cui I 5 2

O-type on each dimension. While a more substantive dis­

cussion of the characteristics of these 0-types is offered

below, evidence of the criterion of within-cluster homo­

geneity and between-cluster heterogeneity for the construc­

tion of typologies can be observed from this plotting.

it was suggested previously that the high homogeneities

for this O-type solution attested to the "tight-fit" of

the members of a given type on a single dimension. This

conclusion is echoed by the data in Tables 6.12 and 6.13

describing the standard deviation of each O-type on each dimension and the euclidean distances between each object cluster. All of the standard deviations, for example, were considerably below ten, the standard deviation for the entire pool of counties. The distances between 0- types ranged from a low of about 18 to a high of nearly 41.

With the minimum number of cases per O-type set at the BC TRY default of two, a Wah Ikre i s-I eve I solution yielded

I I core 0-types with no rejects. The smaI I number of electoral districts appears to have somewhat hampered the ability of a large number of units to cluster together.

Correspondingly, the values on the O-type homogeneities

(see Table 6.14), while still sufficiently high, are generally lower than they were at.the county level.

Table 6.15 and Figure 6.2 present and plot the means of the II 0-types on each of the variable dimensions.

These data demonstrate that when addressing Wahlkreis Table —

STANDARD DEVIATIONS OF KREIS O-TYPES (7 O-Types)

Variable Cluster O I — > Q < I D ~ . INDUST DVPDDP SPDCAT MCDNVP WRTSFT TRNOUT —

3.8783 5.8106 6.9879 5.6648 4.8504 3.4498 4.4825 5.5376 5.2060 7.5166 6.2972 2.6797 7.0238 6.3857 5.0204 5.1941 7.6465 3.0169 4.0216 3.956 1 5.0184 m 6.7666 5.2169 2.7505

6.6784 6.0773 4.5064 3.8592 e 5.7774 3.5730 5.3022 4.7484 5.0302 4.2105 7.0292 2.5138 6.7327 6.3317 6.3629 4.4827 6.9168 4.1700

v_T! v

0 4 2 5 4 6 7 0 9 1 6 7 1 8

...... 7 1 9 3 0 9 8

1 3 4 2 2 2 S E

P 0 3 3 6 5 5 Y 0 0 6 3 1 2

. . . . T . . 6 - 8 1 1 1 2 0

1 2 2 2 2

S I E R K

0 3 5 9 9

0 N 3 5 8 3

. . . . . 5 E 6 3 0 6 E 3 2 2 2 W T E B )

s S e 1 l 0 e

8 2 E b 0 p 5

9 6

C . . . . a 4 y N 6 5 3 T T A - 3 2 2 T O S I s D 7

( e

p

N 1 0 y

7 A 0 T 0 7 E

- . . . 3 D 8 5 I O L 2 2 C U E 0 0 0 7

. . 2 3 2 0 0

I . s

e

p

7 5 6

y CM fO T - O 9 9

data, while the small number of cases may limit the genera I izabi I ity, the core O-types clearly exhibit distinc­ tive multidimensional profiles..

In an effort to increase the generaIizabiIity of the

Wahlkreis-level 0-type solution, these data were reintro­ duced into the BC TRY object cluster routine. With the minimum number of cases per type raised to three, the resulting typology consisted of six O-types with only one of the 35 districts being classified as a reject.

While the size of the O-types generally increased, the declining values on the homogeneity statistics appear to signal a decrease in the uniqueness of each 0-type. In general, however, the bulk of the cluster statistics presented in Tables 6. 18 through 6.21 and in Figure 6.3 offer a fairly consistent image of a system of statistically and substantively distinct types of electoral districts.

Rather than discussing the types of units derived in each of the three classification systems by typology, the similarities across typologies can best be seen by addressing all Types** derived in ail analyses. Specifying with regard to each Type the O-types associated with it, to demonstrate the parallel patterns of 0-type identifica­ tion (see Table 6.22), the eleven O-types derived in the several analyses can be summed up as fol lows:

Type I: HIGHEST RURAL/HIGHEST CATHOLIC [K4,WK(6)6, WK(I I ) I 0]I 2 Kre i se and Wa hIk re i s e making up this type scored the lowest on the variable dimension INDUST. Simi­ larly, these rural areas exhibited the highest support for the Catholic parties and, conversely, the lowest support for the Socialists (low SPDCAT). Table jo J U

HOMOGENEITY OF WAHLKREI S O-TYPES ( I I O-Types)

Variable Cluster

0 - T y p e INDUST DVPDDP SPDCAT MCDNVP WRTSFT TRNOUT OVERALL NUMBER OF HOMOGENE 1TY MEMBERS 1 1.0000 . 9837 . 9997 . 8425 . 9436 .959 1 .9563 9 2 . 7079 . 9926 . 949 1 . 9808 . 9404 . 3673 . 8533 4 3 . 9828 . 635 1 .9816 -.9 9 1 3 . 9862 . 9902 . 8623 3 4 . 96-05 .8145 .9380 . 7999 . 8707 . 9895 . 8984 5 5 . 7888 . 7996 .958 1 . 9580 . 9273 . 977 1 . 9048 4 Ô . 9790 . 8877 . 8525 . 8437 .8141 .8422 .8715 3 7 . 9802 . 8828 . 8663 .9618 . 9773 .9307 .9343 3 . 8 . 8280 .9743 . 9656 . 9474 .8586 . 8699 . 909 1 3 9 . 9769 . 9988 . 9999 . 9400 . 9633 . 9927 .9788 2 i 0 . 865 1 . 9250 . 8959 .8619 . 4203 . 907 1 .8315 4 11 . 9640 . 9938 . 9640 . 9930 . 9763 . 8707 .9612 2 35 Eta . 9036 . 8533 .9390 .9165 . 8783 .8914 (Index of Correlation) Table ‘

MEANS OF WAHLKREIS O-TYPES ON VARIABLE CLUSTERS ( I I O-TYPES)

0-TYPE 1NDUST DVPDDP SPDCAT MCDNVP WRTSFT TRNOuT I 47.469 58.653 63.523 54.786 47.443 38.822 2 56.435 45.397 37.907 49.208 54.524 56.329 3 72.989 58.740 57.111 47.225 4 1 .376 4 0.5 0 4 41.648 42.157 52.190 34.662 46.239 43.579 5 51.042 51.933 56.323 38.679 56.073 47.559 6 46.720 59.822 45.465 59.302 36.768 73.72 C 7 43.213 56.400 50.393 63.242 47.714 49.Q7 _ 8 50.434 50.968 58.946 59.055 69.765 50.3c 9 57.167 58.916 61.169 57.474 61.514 4 1 . 2 ': I 0 39.347 32.874 32.358 52.859 43.202 5 1 .Sc, I I 52.556 52.727 48.778 51.349 49.405 52 . 5 "6 ì 5 / Table \o . \ (3

STANDARD DEV I AT iONS OF WAHLKREI S O-TYPES (il O -T y p e s )

Variable Cluster

O-T ype 1 NDUST DVPDDP SPDCAT MCDNVP WRTSFT TRNOUT

1 . 0728 1.7979 .2446 5.3865 3.3123 2.8309 2 7.0636 1.2105 3. 1483 1.9504 3.3995 9.3010 3 1 . 8446 1 1 .8464 1.9100 1.3170 1.6576 1 .3934 4 2.7845 5.8022 3.4667 6.0019 4.9182 1 .443 1 6.1467 6.0054 2.8648 2.8687 3.7439 2.1272 6 2.0370 4.6044 5.2272 5.3675 5.8070 5.3917 7 1.9783 4.6972 4.996 , 2.7367 2.1 196 3.6577 8 5.6079 2.2539 2.5995 3. 1 996 5 . 1 270 4.9325 9 2.1373 .4894 .15 13 3.4 1 13 2.684 1 1 .2090 i 0 5.0167 3.7994 4.4433 5.0713 9.0736 4.2099 1 1 2.6574 1 . i 080 2.6578 1.1842 2.1662 4.9181 I

5 9 • “‘"A Table to •> \

EUCLIDEAN DISTANCES BETWEEN WAHLKREIS 0-TYPES (II O-Types)

0-Ty pe O-Ty pe I 2 3 4 5 6 7 I .00 36.06 28.09 c 29.38 22.80 40.99 19.78 9m . 00 35. 33 r 29.60 24.52 32 .'85 27.12 c . 00 38. 37 s 29.45 45.79 36.38 f

l . 00 18.15 44.59 32.69 c i

m . 00 4 1.00 28.20 3

r . 00 - 27.34 ~ . 00

8 9 10 || —

27.18 17.62 43.53 2 1.96 C

M 29.57 32.72 25.3 1 15.23 r

o 40.19 27.89 50.78 27.38 ^ i

- 37.35 36.90 29.91 24.72 L

n 24.87 23.09 38.2 1 17.02 k

o 43.68 45 .'05 38.86 27.69 r - ' C 25.67 24.85 . 32.03 15.95 O . 00 16.37 43.63 24.28 c r

i . 00 47.93 O 22,93 . 00 29.66 . 00

C'' o able » Ì &

HOMOGENEITY OF WAHLKREIS O-TYPES (6 O-Types)

Variable Cluster

O-Type 1 NDUST DVPDDP SPDCAT MCDNVP WRTSFT TRNOUT OVERALL NUMBER OF HOMOGENE 1TY ! MEMBERS . 7736 . 9625 .9015 . 9839 . 9453 .4322 . 8550 5 2 .9828 . 635 1 .9816 .9913 . 9862 . 9902 . .8623 3 3 . 7557 . 6455 . 9244 . 8506 .7514 .9640 . 8226 9 4 . 9790 .8877 ' . 8525 . 8437 .8141 . 8422 .8715 3 5 ■ . 8479 . 9035 . 7824 . 860 1 . 5403 . 727 1 . 786 1 1 0 6 . 865 1 . 9250 . 8959 .8619 . 4203 . 907 1 .8315 4 34 Eta .8517 . 7882 . 877 1 . 8879 . 7292 .8215 1 Reject (Index of Cor re I at I on)

CTl Table *0 , 1

MEANS OF WAHLKREIS O-TYPES ON VAR I ABLE CLUSTERS (6 O-TYPES)

0-Ty pe 1NDUST DVPDDP SPDCAT MCDNVP WRTSFT TRNOUT

56. 1 90', 46.642 39.549 49.399 53.934 54.588 2 72.989 58.740 57.111 47.225 41.376 40.510 3 45.823 46.502 54.027 36.448 50.609 45.348 4 46.720 59.822 45.465 59.302 36.768 73.720 5 48.251 55.917 57.111 58.272 54.058 46.605 6 39.347 32.874 32.358 52.859 43.202 5 1 .922

I 6 4 T 4 9 8 7 8 4 3 U 9 7 1 5 0 9 O 0 1 9 6 6 3 N 2 0 3 8 6

...... R

1 4 9 5 2 6 T 6 6 9 8

0 0 FT F 7 3 1 8 7 5 r O 5

7 6 0 1 9 TS e 6

0 2 8 4 5 . R . . . . . +

S S 1 3 9 s 5 8 W 6

E N u P

I O ) I

Y s C T

T - e A I p O P 3 8 0 6 6

5 e V l V y 7 5 1 1

8 7 E N b 1 T 8 7 S 0 6 5 e I - l D D 3 7 a 0 1 3 2 . . i E . . C . b . O

r 1 1 R 5 5 5 5 a M D

a K T 6 R L ( V A H D A N W A 3 0 2 0 5 2 T AT 0 3 7 7 3 7 S C 1 4 2 1 2 2 9 4 2 8 3 2 ...... 1 3 5 4 SPD 4 6 P 4 4 6 4 4 2 D 6 9 6 4 7 4 D 4 9 8 3 0 1 P 8 7 2 6 6 7 ...... V 1 3 7 4 2 4 D 1 T 6 7 7 0 0 1 S 4 6 6 9 7 2 U 4 1 3 4 3 0 D 8 0 3 5 0 3 ...... N 1 1 5 5 6 6 2 e

p

y — Cxi f'H xr tr\ vO T - O Table

EUCLIDEAN DISTANCES BETWEEN WAHLKREIS O-TYPES (6 O-Types)

O-Ty pe

O-Ty pe 2 3 4 5 6

33.13 24.11 32.51 24.49

N 25.68

. 00 33.50 45.79 30.66 I 50.78

" l . 00 42.08 24.37

' 32.63 N . 00 34.47 38.86 5 . 00 37.40 6 . 00 1 6 5 b 6

r able 6.22

Summary of Types from Object Clustering Solutions

Most Strongly O-type Solution (0-type Number) Type Defining Characteristics Kreis W a h 1 k r e i s Wahlkreis Ï7 ) (6) (II)

1 EH + ]a RURAL/EH+] CATHOLIC 4 ' 6 1 0

1 1 EH + ] URBAN/EH+] PROTESTANT 5 2 3

I 1 1 URBAN/CATHOL1C/EH] WP/EL] DDP, DVP AND COMBI NED CENTR1ST/EH] TURNOUT/EH] DNVP 7 1 2

1 V El] catholic/Ch+] turnout/ Eh] DDP AND DVP 6 4 6

V RURAL/PROTESTANT/EH] centrist/El] dnvp/El] TURNOUT 1 5 7

V 1 rural/protestant/El] CENTR1ST/EH]DNVP/EL] TURNOUT 2 3 4

V i 1 URBAN/PROTESTANT/EH ] WP/ El] turnout £ Vo J

VIII URBAN/PROTESTANT/EH] WP/ Em] TURNOUT L b s

1 X URBAN/CATHOLIC/EL] WP/EH] DDP, DVP, AND COMBINED CENTRIST PARTIES b b 1 1

X RURAL/EH] PROTESTANT/EL+] TURNOUT b b 1

X 1 URBAN/PROTESTANT/EH] DDP AND DVP/EH] WP/EH] DNVP/ El] TURNOUT b b 5 aThe letters H, M, and L indicate a consistently high, medium, or low dimension profile, respectively. An H or an L followed by a plus sign ( + ) indicates that ihe Type had the highest or \ c c s V scores on these general dimensions of the seven Types for which there is a cross level parallel ( I -V I I ) . bQ-type is exhausted. I 6 7

While the specific centrist middle-class parties were not particularly strong in these areas, (low DVPDDP and low WRTSFT), the scores on the summated middle-class variable dimen­ sion (MCDNVP) were above the mean. From this last variable, it is also possible to see that the support for the Nationalist Party in these areas was not particularly great. Finally, it should be noted that the turnout increase in these areas was uniformly in the high-middle range.

Type II: HIGHEST URBAN/HIGHEST PROTESTANT EK5,WK(6)2,WK(11)3] These units clearly represented the major urban counties and electoral districts. Sup­ port for both of the working-class parties, KPD and SPD, was quite high, while the Catholics found some of their weakest backing in these units. The support for the Economic Party was somewhat below the mean whi le the German People’s and German Democratic parties exhibi­ ted considerable strength. The levels of combined centrist party support and the strength of the DNVP both tended to fluctuate around the mean and in each case felI within the plus-or-minus one standard deviation range. Additionally, these areas which pre­ viously had sent the largest proportions of the eligible electorate to the polls, uniformly experienced among the lowest increases in voter turnout

Type III: URBAN/CATHOLIC/HIGH WP PARTY/LOW DDP, DVP, AND COMBINED CENTRIST PARTIES/HIGH TURNOUT EK7,WK(6)I,WK(I 1)2] Units classified within this type were character- ized by their considerable support for the Catholics (second highest), in addition to a rather high degree of urbanity (at least second or third highest). Support for the Democratic and People's parties, as we I I as that for the,combined centrist parties, was relatively weak. The Economic party was rather strong in these areas, however, and this group may have experienced a large portion of its loss of strength within this type. As with the other predominantly Catholic type (Type I), the turnout increase for this type was generally in the High-Middle region. This suggests the Catholic parties may have been among the primary beneficiaries of the increased participation by the electorate. I 68

Type IV: CATHOL I C/H I GHEST TURNOUT ,/H I OH DDP AND DVPCK6,WK(6)4,WK(11)6] Areas wi+hin This type were The regions providing greatest support to the two largest centrist parties in both the 1928 and 1930 elections. Support for the combined centrist parties was, similarly, quite extensive (HIgh-MIddIe) whi le scores on the WRTSFT dimension, representing fhe strength of the Economic Party, were consistently below the mean. The 1928— 1930 Increase In voter turnout reached Its peak in these units while the discrimination of scores on many of the other dimensions was less clear.

Type V: HIGH CENTRIST PART I ES/RURAL/PROTESTANT/ LOW DNVP/LOW TURNOUTCKI,WK(6)5,WK(I I )7] These areas were among the lowest, but not quite the lowest on variable clusters INDUST and TRNOUT and among the highest, although not quite the highest on V-types DDPDVP, SPDCAT, and WRTSFT. The mean of the cluster scores within this O-type on variable cluster MCDNVP was the one exception to this general- pattern. These areas possessed the highest mean value on this dimension in two out of the three typological analyses and was a close second i n the th i rd.

Type VI: RURAL/PROTESTANT/HIGH DNVP/LOW CENTRIST BOURGEOIS PARTIES/LOW TURNOUTCK2,WK(6)3,WK(I I )4] Areas within this O-type were similar to those in Type V, largely Protestant and clearly rural districts and counties. Indeed, among the Protestant areas, they represented fhe most rural portions of the Reich. In fact, ihe primary characteristics dis­ tinguishing between Types V and VI involved divergent profiles on the variable cluster MCDNVP. While those members of the former O-type exhibited high cluster scoi-s on this dimension, the members of the latter type exhibited high scores on its reflection.

Type VII: URBAN/PROTESTANT/HIGHEST WP/LOW TURNOUT EK3,WK( I I )9,WK(6)-- + ypology exhausted] This O-type was marked by strongest support for the bourgeois Economic Party which laid claim to the support of the small business artisans, and craftspeople. These units were, similarly, among the strongest Protestant districts of the Reich. Although not the most urban portions of Germany, these areas were well above the mean on this dimension. The strength of the Economic Party was backed up by the other middle-class parties in making this type a Centrist Party stronghold. I 6 9

The final four clusters, Types VIII through XI, all emerged in only one of the object clustering solutions. These types, which can be seen as special cases of many previously discussed clus­ ters, were definable only in the WK(I I ) O-type solution in which the minimum number of cases required.for the identifica­ tion of a type was two. The relaxation of more stringent clus­ tering requirements allowed for the identification of more refined or "pure" types, while at the same time, limiting the extent to which conclusions about a given unit can be carried over to the other units under consideration.

Type VIII URBAN/PROTESTANT/HI GH WP/MIDDLE TURNOUT CWK(¡1)8] In general, the units making up this type exh i b i t V-type profiles not too dissimilar to those found in Type VII. This is not to suggest, however, that there are no differences between these two clusters. Whi le the preceding type was characterized by. a high score on WRTSFT, indicating strong support and considerable I 928-1930 losses for the Economic Party, the scores on Type VI II units on this dimension are even higher. Similarly, while the two types are both above the mean on INDUST and DVPDDP, the refined Type VIII is only moderately so, whi le Type VII is .cons i derabIy g.eater than the mean value of 50 on each of the two dimensions, One final point differentiating these two object clusters is worthy of note. The .most distinctive feature setting these two types apart is the difference in their mean scores on variable cluster TRNOUT. While the mean value for Type VII was particularly low on this variable cluster (41.2), Type VIII (50.4) actually exceeded the mean for alI units on this di mens i on.

Type IX: URBAN/CATHOLIC/LOW WP/HIGH DDP, DVP, AND COMBINED CENTRIST PARTIES CWK< I I ) I I□ In terms of industrial characteristics, the considerable support for the Catholic parties, and the patterns of voter turnout, units falling into this type most clearly resemble Type III. Considerable differences can be noted, however, with respect to the support accorded the various non-Catholic middle-class parties. Type IX represented units which provided considerable support for the DVP, the DDP, and the' combined centrist parties. I 70

Similarly, these units witnessed much of the losses incurred by the latter two parties. The strength and ihe extent of the loss of the Economic Party, however, was considerably less significant than it was for the Reich as a whole. This pattern of support for and defection from these centrist parties and pa rty groupings was virtually a mirror-image of that found in Type Ilf.

Type X: RURAL/PROTESTANT/LOW TURNOUT CWK(I I )|J The clearest description of this type must suggest that it is like Type V but more so. This type was tlie most Protestant/I east Catholic of all clusters identified through BC TRY. Similarly, units within this type can also be distinguished from others by reference to the mean-score on variable cluster TRNOUT. The mean of 38.8 Identified this type as that with the lowest increase in voter turnout. Apart from the more extreme scores on these dimen­ sions, however, the cluster score proflles on this type closely paralleled those defining Type V.

Type XI: URBAN/PROTESTANT/HIGH DDP, DVP, WP/HIGH DNVP/LOW TURNOUT EWK( I I ) 5-]- - Units in this final Type were quite similar to those In Type VIII. The key difference between these two clusters involved their complementary pattern of support tor the Nationalist Party. The mean of Type XI cluster scores on this variable cluster (MCDNVP) is greater than one standard deviation below the overall mean while that of Type VI i I members is greater than one standard deviation above it.

Fol lowing the above discussion of the process of object cluster analysis and the specific O-types and more general Types derived in its application, it is possible to utilize these c usters to aid in the understanding of the 1930 electoral success of the Nazis. The final section of this chapter is de­ voted to a discussion of this issue. I 11

0-types and 1930 Nazi Support

Whi le the creation of typologies of units offers some

information regarding the empirical relationships between

a number of areal characteristics, their relationship to

the vote of the Nazis has yet to be addressed. The nature

and extent of the association between the contextual proper­

ties defining these clusters and the 1930 Nazi electoral

support is the focus of this section.

These findings are derived from the tabular analysis

of the 0-types and each of the two dependent variables

previously addressed: the percentage of the total vote going

to the Nazis In 1930 and the increase in the percentage of

the electorate captured by the NSDAP between 1928 and 1930.

Independent tabulations were undertaken for data at each

level of aggregation, with each of the two Wahlkreis

O-type solutions, similarly, being treated separately.

Given the parallel structures of the derived typologies,

it is possible to arrive at conclusions which are not

tied to a particular analytical level or O-type solution.

An examination of the relationship between 0-types and the Nazis' share of the vote in 1930 Reichstag election (see

Tables 6.23 through 6.25) yields several significant observa tions. While some support for the Nazis was found in all of the WahIkrei s and Kre i s clusters, the 1930 showing of the Nazi Party was particularly strong in two of the 0- types derived in each of the three O-type solutions. In I 72

the case of the Kre i s-I eve I data, O-types I and 2 were set

apart from the others as the only two clusters In which

more than 20.0 per cent of the member-units provided

Hitler's National Socialist Party with a minimum of 30.0

per cent of the tota, vote cast. Thirty-one of the 148

Kre i se in 0-type I (21 per ^ent) and 49 of those in object

cluster 2 (24.3 per cent) gave the Nazis between two and

three times the strength they received in the Reich as a

whole. Comparatively, less than 12 per cent of all K r e i s e

offered the NSDAP this level of support.

In turning to the Wahlkreis typologies, it must be

initially noted that while the success of the Nazis ranged

considerably from cluster to cluster, In no Instance did the Party's strength In 1930 reach 30.0 per cent of the total vote. Although the gre.fer homogeneity of the larger aggregates reduced the range of values on the dependent

variable, a familiar pattern can be observed in those WahI - kreis 0-types with unit-members in which the Nazis reached at

Iea st 20.0 pe r cent.

In the I I-WahIkreis solution, six clusters contained at least one electoral district in which the Nazis won

20.0 per cent of the district's 1930 total. In only two of these six clusters (4 and 7) however, was this vote re­ corded in every WahIkrei s.

This two-type pattern of Nazi strength emerges once again in the six-type Wahlkreis typology. The Nazis were i a b I e 6.23

¡930 Nazi Vote by 0 type Ek]

Percent 1930 Vote for Nazis 1 2 3 4 5 6 7 Rejects Tota 1 s

0.0-9.9$ i 8 2 1 1 20 9 1 3 1 3 1 1 77 (12.2) (1.0) (0.7) (53.8) (7.1) (13.0) (17.1) (10.0) (17. 3)

10.0-19.9% 5 1 54 6 1 84 67 56 25 2 400 (34.5) (26.7) (43.6) (37.7) ( 52.8') (56.0) (32.9) (20.0) (39. 0 ) 20.0-29.9$ 48 97 59 1 8 42 29 32 5 330 (32.4) (48.0) (42.1) (8.1) (33.i) (29.9) (42. 1 ) (50.0) (32. 2 )

30.0-39.9$ 22 45 1 8 1 8 2 5 2 103 (14.9) (22.3) (12.9) (0.4) (6.3) (2.0) (6.6) (20.0) ( I 0 . 0 )

40.0-49.9$ 5 4 0 0 1 0 1 0 i i z (3.4) (2.0) (0.0) (0.0) (0.8) (0.0) (1.3) (0.0) k i . 1 )

50.0-54.9$ 4 0 1 0 0 0 0 0 (2.7) (0.0) (0.7) (0.0) (0.0) (0.0) (0.0) (0.0) (0 . 5 )

Totals 1 48 202 1 40 223 i 27 100 76 i 0 1 026 ( 1 00 . 1 )

*Does not equal 100.0 due to rounding error. I 74

able to win 20.0 per cent of the vole in 88.9 and 60.0 per

cent of the member-units of Types 3 and 5, respectively. Cv’-’-.jv ii O) Referring back to Table 6.22, the meaning and sign i f i — A cance of these observations may be clarified. The strongest bases of Nazi support fell into one of two more general Types.

Kre i s 0-types I and 2, WahIkre i s 0-types (11—types) 5 and 3, and WallIk re i s 0-types (6-types) 7 and 4 are associated with

Types V and VI, respectively, making the identification of areal characteristics associated with the most successful cam­ paigns a relatively simple task.

These data reaffirm some of the conventional wisdom about the sources of the Nazi vote in this election. Ini­ tial ly observing that areas in each of these two Types were most frequently rural-agricultural districts, it appears that the efforts undertaken by the Nazis prior to the 1930 election to gain the support of rural voters met with some measure of success.

Another commonly held view of the Nazis' electoral accomplishments in 1930 and beyond is that they took place without any significant assistance from Catholics and sup­ porters of the Catholic Center and Bavarian People’s parties.

It should be recalled that the aggregated Catholic parties did not decline during the final elections of the Weimar

Republic. As with the V-type analysis discussed earlier in this chapter, the object clustering provided rather strong evidence in support of this hypothesis regarding the rela­ tionship between the NSDAP and Catholic party constituencies. Table 6.24

1930 Nazi Vote by u-Type

Percent 1930 ______O-Type Vote for Nazis 1 2 3 4 5 6 7 8 9 10 11 Toi n

0.0-9.9% 0 0 0 0 0 1 0 0 0 1 0 O (0.0) (0.0) (0.0) (0.0) (0.0) (33.3) (0.0) (0.0) (0.0) (25.0) (0.0) (5.7)

10.0-19.9% 1 4 3 0 1 2 - 0 1 2 3 1 18 (50.0) (100.0) (100.0) (0.0) (25.0) (66.7) (0.0) (33.3) (100.0) (75.0) (50.0) (51.4

20.0-29.9% 1 0 0 5 3 0 3 2 0 0. 1 i (50.0) (0.0) (0.0) (100.0) (75.0) (0.0) (100.0) (66.7) (0.0) (0.0) (50.0; (42.>

Totals 2 4 3 5 4 3 3 3 2 4 2 3a (100.0 Table 6.25

1930 Nazi Vote by O-type

Percent 1930 Vote ______O-type for Nazis 1 2 3 4 5 6 'Rejects Totals

0.0-9.9% 0 0 0 0 1 0 2 (0.0) (0.0) (0.0) (33.3) (0.0) (25 . 0 ) (0.0) (5.7) I 10.0-19.9% 5 3 I 2 4 3 0 1 8 (100.0) (100.0) (1 1. 1) (66.7) (40.0) (75 . 0) (0.0) (51.4)

20.0-29.9/» 0 0 8 0 6 0 1 1 5 (0.0) (0.0) (58.9) (0.0) (60.0) (0. 0 ) (100.0) (42.9)

Totals 5 3 9 3 1 0 4 1 35 (100.0) I 7 7

Not only were the 1930 seats of Nazi power predominantly

Protestant regions, but additionally, those districts which

were heavily Catholic generally proved to be unfertile ter­

ritories for the elector'al growth of fhe NSDAP.

While the bulk of the Nazi strength can be identified as rooted in rural Protestant constituencies, these areas were neither the most rural nor the most Protestant sec­ tions of the Reich. Although the NSDAP did particularly well in rura I - agrIcu I tura I I y dependent regions, their 1930 electoral showing was quite unimpressive in single-most rural Wahlkreis and Kre i s because these were the areas in which the Catholics were predominant.

Similarly, while the Nazis had their strongest showing

in Protestant districts, it was In these more industrialized aggregates that a growirug portion of the working class turned to the Communists as the economic crisis of the 1930's became more severe. As was pointed out by most of those researching this question-and stressed by Kele (1972.) and Hunt (1964), the Nazis may have gained the backing of individual KPD members or supporters, but virtually all of the quantitative and qualitative evidence available leads to fhe conclusion that the bulk of the supporters of these two parties were to be found in different locales. Hence, the flow of KPD voters to the Nazis was negligible.

Reviewing the other object clusters, particularly

Types VIII through XI, prior observations on the relative strength of Nazi support In CathoI Ic-Protestant areas are rein forced. All four of these Types, derived only in the Il-type I 78

Wahlkreis clustering, counted among their members at least

one unit which provided the Nazis with a minimum of 20.0

per cent of the vote. Unlike the two areas of Nazi strength

discussed above, not one of the latter four Types consisted only of districts with Nazi support at this 20.0 per cent

level.

The view that the Nazi strength predominated in rural

Protestant regions is further supported by the data from

Type X. Like Types V and VI, this cluster is character­

ized not only by a rather strong backing of the Nazis, but also by a predominantly rural Protestant constituency.

Types VI LI and XI are largely Protestant districts.

In the case of each cluster, with a mean score only slightly above the overall mean of 50, these Types are of units which were more urbanized and industrialized than those in Types V and VI.

The final Type in which at least a single Wahlkreis member-unit provided the Nazis with 20.0 per cent of the total vote is Type IX. Unlike the other clusters meeting these minimum criteria, Type IX would be classified as predominantly urban Catholic. Reca-ling the particularly low level of support given the Nazis in rural Catholic areas, these findings echo those of Waldman (1973) who noted that the success of the Nazis was progressively less extensive from rural Protestant to urban Protestant to urban Catholic, and finally, to rural Catholic districts.

A more extensive examination of the two types of areal units in which the Nazis achieved their most sub- I /9

s t a n t i a I success in 19 30 uncovers other information of note. The strong showing of the Nazis in these districts

is particularly significant in light of the hypothesized

"sources" of Nazi support in the 1930 Reichstag election.

Types V and VI are both characterized by low mean scores on

V-type TRNOUT. A low score on this dimension, it will be recalled, was characteristic of units with a big voter turnout in 1928 and a rather small increase in voter parti­ cipation in the subsequent balloting. These data, there­ fore, appear to support the conclusion drawn by a number of researchers (Heberle, 1951; Heberle, 1970; Lipset, I960;

Pollock, 1944; Orlow, 1969; Shively, 1972; Waldman, 1973) that the 1930 influx of new voters could not account for a substantial portion of the success of the Nazis in this parliamentary contest.

Focusing on the other general Types al lows for the construction of a possible scenario which might have stimu­ lated the sudden surge of participation by the electorate.

While the apparent success of i ¡e massive Naiional Socialist propaganda and organizational efforts has previously been pointed to, it must be recognized that the Nazi Party's activities did not take place in an otherwise inert politica stage. On the contrary, the Nazis were very much a part of a dynamic setting in which the strategies of at least one of their opponents, fhe Catholic Church and the parties associated with it, Z and BVP, ai tempted to counter the continued drive by the NSDAP. While the Nazis seem to have been less than successful in gaining the support of those O J

who joined the active electorate in 1930, the activities

of the Catholic bloc appear to have stimulated considerable

support for the anti-Nazi effort of this period. Even

when people were not moved to support the Catholic parties,

it is quite conceivable that the extensive anti-Nazi propa­

ganda of the Catholics directed people into the ranks of

other parties in search of a non-Catholic opponent to the

National Socialists. Evidence in support of this interpre­

tation is pointed to in Types III and IV, both of which

Included units which were heavily Catholic, weak support

for the Nazis, and which experienced the greatest increase

in voter participation. Given the limited gains for the

Catholic parties and the weak support for Hitler's forces,

it seems most logical to conclude that much of the new

turnout went elsewhere.

With respect to two final variables, the Types which

contributed most substantially to the Nazis in 1930 exhi­

bited complementary patterns. Type V is composed of units

in which the DVP, DDP, WP and the combined forces of the

liberal and moderate parties of the middle and low-middle

classes were alI quite strong. The electoral support for

the DNVP, on the other hand, was among the weakest of all

districts in the Reich.

For Type VI, however, the observed relationship between these two variables Is the Inverse of Type V. While Type VI members were among the strongest in their support for the

Nationalist Party, the strength of the People's, Democratic, and Economic parties as wel.l as the combined centrist parties

was quite low.

The Types strongest in centrist party support and the

Nationalist Party stronghold were distinguished from each

other by the former's possession of high cluster scores

on variable clusters MCDNVP, WRTSFT, and DVPDDP and the

latter's low scores on these clusters. RecaI I ing the ful I

meaning of the V-types on which these object clusters

are distinguished will greatly amplify the significance of these observations.

High scores on MCDNVP represented areal units which

in 1928 had been strong seats of combined centrist support and in which these parties suffered the greatest of their

1928-1930 losses. This was coupled with the relatively weak support for the DNVP in 1928 and the fewest of their

1928-1930 losses and a limited number of Nationalist Party ga i ns.

The high score on WRTSFT pointed to the strength of the

Economic Party In both the 1928 and 1930 Reichstag elections

These units similarly found the WP suffering their greatest losses in the second balloting.

Final ly, a high cluster score on DVPDDP reflected the greatest support for the German People's Party and the

German Democratic Party In 1928 and we I I as 1930. Addi­ tional ly, whi le the losses of the DVP were greatest here I 82

those of the DDP tended to be random with respect to a I I

of the variable clusters.

High cluster scores on each of these V-types would characterize Type V. For Type VI units, the scores on each of these variable clusters would be among the lowest, pointing to the 1928 strength and most substantial 1928-

1930 losses by the Nationalist Party. They similarly represent the relative weakness of the centrist parties

in these areas and their generally greater showing in the

1930 election.

Thus, whi le these data lead to the rejection of the hypothesized relationship between increased voter turnout and fhe 1930 Nazi vote, those suggesting that the Nazis may have gained at the expense of the Nationalist Party and those which have previously been labeled the "non-

Catholic mi dd le class" parties appear to be on firmer ground. Although the Naz is received some degree of sup­ port throughout alI portions of the Reich, their most substantial backing was found in districts where these other parties had been strong and in which they tended to lose much support between the 1928 and 1930 elections.

A more complete evaluation of the hypotheses regarding the 1930 successes of the NSDAP is possible only with an examination of the change in Nazi Party support between

1928 and 1930. Tables 6.26, 6.28, and 6.29 present the

Nazi gain by O-type for each of the three BC TRY O-type solutions. Types V and VI, which exhibited particularly strong

NSDAP support in 1930 also defined the K r e i s e with the

most substantial Nazi gains between 1928 and 1930. Type

VI (composed of 0-types K2, WK(II)4, and V,'K (6) 3) was the

object cluster in which the Nazis found their greatest

increase in support.

for the Kre i s-IeveI typology, ful ly 55.0 per cent of the

O-type 2 members saw the Nazi vote increase by at least

20.0 per cent and in nearly 15.0 per cent of these units,

the NSDAP increased by 30.0 per cent or more. In no other

O-type did even seven per cent of the Kre i se show a Nazi

gain as high as the latter figure.

In Cluster I, the high Centrist Party aggregation,

the Naz is received a 20.0 per cent minimum increase in

37.9 per cent of the Kre i se-members. Similarly, while the

Nazis failed to gain at least 10.0 per cent on their 1928

vote in only 2.0 per cent of the cluster 2-members, the

National Socialist share of the vote did not reach this

minimum in 16.2 per cent of fhe K r e i s e included in 0-type I.

The most apparent difference between the two 0-types

that provided the Nazis with their greatest 1930 support

was the relative strength (in 1928) and change in support

(between 1928 and 1930) of the Nationalist and middle class

parties in these districts. An examination of the Nazis'

ability to improve their relative electoral positions

highlights the likely role of the Nationalist Party defec­ tors in the Nazi victory. i a b i e 6.26

1930 Nazi Gain by 0-type CK]

- - - Percent 1930 Nazi 0 -1 y p e Vote Gain i 2 3 4 5 6 7 R e j e c t s Tota 1 s

0.0-9.9$ 24 4 4 1 50 1 5 28 1 7 2 244 (16.2) (2.0) (2.9) (67.3) (11.8) (28.0) (22.4) (20.0) (23.8)

10.0-19.9$ 68 87 86 66 80 64 30 4 485 (45.9) (43.1) (61.4) (29.6) (63.0) (64.0) (39.5) (40.0) (47.3)

20.0-29.9$ 46 8 I 43 7 32 8 27 4 248 (31.1) (40.1) (30.7) (3.1) (25.2) (8.0) (35.5) (40.0) (24.2)

30.0-39.9$ 1 0 29 7 0 0 0 2 0 48 (6.8) (14.4) (5.0) (0.0) (0.0) (0.0) (2.6) (0.0) (4.7)

1 40.0-49.9$ 0 1 0 0 0 0 0 0 (0.0) ( 0. 0 ) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.1)

Totals 1 48 202 1 40 223 1 27 1 00 76 1 0 i 026 ( I 00. I )*

*Does not equal 100.0 due to rounding error. I 8 5

The particularly strong association of the DNVP's

1928 strength and 1928-1930 losses with 1928-1930 NSDAP

gains suggests that the non-NationaI ist Nazi .strongholds

had already been among the strongest Nazi areas by the

1928 election. Further support for this estimation can

be found in Table 6.27. The highest mean Nazi vote for

al I Krei se within a cluster was the 4.28 recorded for

O-type I. Cluster 2, on the other hand, consisted of units

of high 1930 Nazi strength with a' mean 1928 figure which,

at 2.75, was the second lowest of the Kre i s 0-types.

Turning to the Wah1krei s clusters, a similar pattern

begins to emerge. In the Il-type solution, cluster 4 (Type

VI) was one of only two clusters in which any member pro­

vided the Nazi Party with a 20 per cent Increase in the

portion of the total vote captured. This strongly Nationalist cluster, in which the National Socialists made their greatest advances in 1930, has previously been pointed to as a Nazi stronghold.

Cluster 7 (Type V), the ruraI/Protestant/strong-centrist- party O-type, which counted many 1930 Nazi seats of power among its members was found to be less generous in supplying the Party with new supporters. All of the WahIkrei s e com­ posing this O-type provided the Nazis with an increase in support ranging between 10.0 and 19.9 per cent. While indicative of a substantial increase in support, these figures did not set this cluster off from most others with respect to 1930 Nazi gains. Table 6.27

Mean Nazi Vote (Percentage of Total Vote) by Kre i s 0-types

O-type ------—------Mean for Al I (V) 2 (VI) 3 (VII) 4 (I) 5 (II) 6 (IV) 7(111) Kreise

Nazi Vote i n I 928 X28 4.28 2.75 3.27 2.18 3.25 4.11 2.93 3'. I 7

Nazi Vote i n 1930 x30 22.27 24.53 21.59 10.76 19.14 16.64 19.21 19.01

Nazi Gain 1928 - 1930 X30-28 17.99 21.78 18.32 8.58 15.89 12.53 16.78 15.84

I 48 202 42 223 127 I 00 76 I 026

'..J I 6 1

Aside from cluster 4, only one other 0-type witnessed

a 20.0 per cent vote increase in at least one of the dis­

tricts comprising it. In one Wahlkreis in cluster 5 (Type

XI) the Nazis achieved this plateau. This cluster, it will be recalled, was derived only in the Il-type Wahlkreis solution and like clusters 4 and 7, was characterized by a strong Protestant constituency. Also like these other 0- types, the mean TRNOUT value was below the means for all

Kre i se.

Whi le labeled urban, the mean score for this cluster on V-type INDUST, at about 52, Is only moderately above the mean across all 0-types. The greater-than-average standard I deviation (over 6) and the I ess-1han-average homogeneity of this cluster suggest that some of this 0-type's members could be found on the rural end of the rural/urban con­ tinuum represented by this variable.

As was the case In cluster 4, 0-type 5 had served as a strong base of support for the Nationalist Party in

1928. Similarly, the drastic reductions In electoral strength experienced by the DNVP in the subsequent ballot­ ing were especial ly pronounced in both of these clusters.

Quite unlike the other 0-type, however, cluster 5 also exhibited some degree of significant support and 1928—

1930 losses tor many of the centrist middle-class parties.

In particular, the losses in support tor the German People's and Economic parties suggest a secondary role for these parties In the 1930 success of the Nazis. 'able ó. 2'2

1930 Nazi Vote Gain by 0-Type \WK(11)~]

Percent 1930 O-Type Nazi Vote Gam 123456789 10 11 Tota

0.0-9.9% 0 0 0 0 0 1 0 0 0 2 0 3 (0.0) (0.0) (0.0) (0.0) (0.0) (33.3) (0.0) (0.0) (0.0) (50.0) (0. C) . ö /

10.0-19.9% 2 -/i.- 3 1 3 2 3 3 2 2 2 7 7 (100.0) (100.0) (100 .0) (20.0) (75.0) (66.7) (100.0) (100a0) (100.0) (50.0) (100.0) (77., ,

20.0-29.9% 0 0 0 4 1 0 0 0 0 0 0 » (0.0) (0.0) (0.0) (80.0) (25.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) .14.3)

Totals 2 4 3 5 4 3 3 3 2 4 2 35 (100.0) I 89

The 6-+ype Wahlkreis clustering provides the clearest

indication of the contribution made by the prior supporters

of the Nationalist Party to the NSDAP victory. Of the 35

electoral districts into which the Weimar Republic was

divided, the National Socialists Increased their 1928

showing by at least 20 per cent in only five. With all

five of these districts falling in cluster 3 (Type VI), the

association between Nazi gains and DNVP losses appears even

st ronge r.

The preceding discussion concludes the formal data

analysis. Following a brief overview of the issues and

methods of this investigation the following chapter will turn to an evaluation of the many hypotheses derived from prior research into the electoral success of the Nazis. labié 6.50 / 1930 Nazi Vote Ga i n by 0-type [”WK( 6 ) ]

Pe rcent 1930 0-type Nazi Vote Gain i 2 3 4 5 6 Rejects Tota 1 s

0.0-9.9$ 0 0 0 1 0 2 0 3 (0.0) (0.0) (0.0) (33.3) (0.0) (50.0) (0.0) (8.6)

10.0-19.9$ 5 3 4 2 1 0 2 1 27 (100.0) ( i 00.0) (44.4) (66.7) (100.0) (50.0) (100.0) (77.1)

20.0-29.9$ 0 0 5 0 0 0 0 5 (0.0) (0.0) (55.6) (0.0) (0.0) (0.0) (0.0) (14.3)

Tota1 s 5 3 9 3 i 0 4 1 35 (100.0) 1 9 0 NOTES

The da+a analyzed and presented in this chapter were provided by the Inter-University Consortium for Political Research (ICPR) located at the University of Michigan, Ann Arbor, Michigan. They were collected by the Historical Archive staff from official returns originally published by the German National Statistical Bureau. 7 All da+a were in percentage form.

^The original correlation matrix Included and the cluster­ ing process was undertaken on only the independent variables. The vote for the Nazi Party (the dependent variable) was not considered at this stage to allow for further analysis after the independent-variabIe clusters were derived.

An additional problem in the statistical analysis of the Increased Nazi vote in 1930 that has been pointed to focuses on the problem of measuring change, In addressing this situation, Waldman (1973) Identified a number of al+erna- five procedures for the computation of change variables. The first formula, that employed by Lipset (I960), involves the computation of a relative measure of change between percentages:

$ vote 1930 - $ vote 1928 $ vote 1928

The primary problem associated with this measure involves the often-noted fact that ratios may distort fhe actual amount of'change which has occurred since it may yield results which are more influenced by the denominator (pre­ vious state) of the calculating formula than by the actual change measured by the numerator (O'Lessker, I 9 6 8 : 6 5 n ; Waldman, 1973:223). The second formula for the measurement of change, that offered by O'Lessker (1968), involves the net numerical changes in support for the various parties and voter turnout

# of votes 1930 # of votes 1928

As was noted by Waldman, however, this formula, too, suffers from certain definite weaknesses. The specific "problem is that community size differences have not been eliminated by standardization, and consequently spuriousness may arise" (Waldman, 1973:223). This factor may be responsible for the extremely strong relationships discovered by O'Lessker. The third formula identified by Waldman is offered as a change measure which does not suffer from the difficulties encountered in the first two. This indicator of change is taken to be the net difference between the per cent'ages. I 92

/ vote 1930 - / vote 1928

In I ight of its ability to overcome the' weaknesses of the two alternative procedures, this measure was employed in the current analysis. In spite of its benefits, however, it should be noted that the use of change variables, re­ gardless of how they are defined, it not without its critics. Schnaiberg, for example, expressed a note of skepticism aimed at any of the three procedures outlined above. Framing his concern in terms of the problem or serial or auto-cor­ relation, Schnaiberg noted that

The problem arises from the fact that a given correlation coefficient for two change variables actually involves combinations of four variables: each of the basic factors being studied, measured at two points in time. In this situation, problems arise between the two measures of the same factor, that is, at two points in time (1969:734).

As this analysis clearly demonstrates, however, the question of whether a change variable is strongly related to the static variables from which it is derived is an empirical question. Specifically, this necessitates posing and responding to the question of where change, in fact, occurs. Were the most substantial changes in a party's support between 1928 and 1930 exhibited in areas of prior (1928) or subsequent (1930) strength or were these shifts in party backing largely random with respect to these two variables? Rather than beginning this study by assuming one of these situations to be true, this is one of the fundamental questions that the current research effort undertook to answer.

^The correlation coefficients are squared to avoid the effects of negatively signed coefficients (Tryon and Bailey, 1970:289).

^This low dependency on agricultural employment is indicated by the negative (-) factor coefficient. Similarly, the minus sign preceding factor coefficients for definers of other clusters are indicative of negative correlations between these variables and the other definers within the same - cluster. In BC TRY, variables with negative factor coef­ ficients are known as "reflected variables". Areas with high scores on these clusters would be characterized by extremely low values on these negatively signed variables. Conversely, areas with low scores on the same clusters would exhibit high values on the negatively signed coef­ ficients and low values on those which are positive. I 9 5

7The statistic b is defined as an uns F_an d a r d i ze d measure with its standard i zed counterpart identified as beta. This absence o f a common base usually makes the comparing of two or more b'! s a questionable undertaking. With respect to the coef­ ficients under consideration, two factors which serve to overcome this common limitation should be noted. First, the values entered Into the regression equations were cluster scores, that Is, the indices of given areal units on each dimension, Cluster scores differ from the raw data not only because they represent a composite of values of several a priori variables, but also, because they are a I standardized scores (with a mean value of 50 and a standard deviation of 10). Thus in the present analysis, the values of b are, in fact, standardized rather than un­ standardized coefficients. Second, and not unexpected given the first point, in all instances, the values of b and beta were quite comparable. The b-values differed only minimally from the betas and In each case yielded a regression coefficient that was a slightly more conservative estimate of the impact of a dimension on the Nazi vote. o The estimation of the upper and lower limits of the strength of an Individual-level relationship from aggregate data can be traced to Duncan and Davis (1953). This approach was joined with the ecological regression techniques of Blalock and Goodman by Shively (1969). 9 4

While cutting points in each of 1hese examples could have been drawn anywhere, the BC TRY s + andar.d options were em- ployed in this investigation, With these options, one cutting point would yield two categories with one repre- senting values below the mean (EL]ow) and the other including cases exceeding the standardized mean value of 50 (EH]i g h ) . Two cutting points would result in the creation of three categories with the middle partition including all cases with values between 40 and 60 (EM]iddle). In this instance, the lower I imit is one standard deviation below the mean, while the upper boundary represents one standard deviation above it. Of the other two categories, one would include values above 60 EH], while the other would Incorporate those below 40 EL]. With three cutting points, four categories would be identified. The BC TRY default would place the cutting points at one standard deviation beneath the mean (40), the mean (50), and one standard devialion above the mean (60) would, similarly, produce four categories in the division of each dimension. The resulting categories could be labeled EL]ow, EL]ow-MIddIe, EH]igh-EM]iddIe, and EH]igh, respect IveIy.

'^The RMS Is computed by dividing the euclidean distance D by the square root of the number of dimensions.

''hereinafter "Type" (capitalized) refers to the Types defined in Table 6.22. Specific types within each 0-type solution continue to be identified by the BC TRY term "0-type."

' 2 T h e notation used in the subsequent discussion is as follows 0-type number four for the ______Kre i s-I eve I data would be repre- sented K4; similarly 0-type three for these data is K3, etc. The fifth O-type of the Wahlkreis typology producing I I 0- types is represented as W K ( I I ) 5, 0-type eight is W l< ( I I ) 8, and so on. Finally, for the Wahlkreis typology yielding six object clusters, 0 -1 y p e one is W K ( 6 ) 0-type two is WK(6)2, . . . 0-type six is WK(6). I 9 5

CHAPTER SEVEN

"Who Voted for Hitler?" Revisited: Some I ssues and Answers

This concluding chapter begins with a review of the

substantive and methodological issues addressed in this

study. Following this overview, a summary of all data analyzed In the preceding chapter will be presented in the evaluation of the key hypotheses that have been advanced about the showing in the 1930 election. Finally, a number of suggestions for further research will be offered.

The electoral victories of the Nazi Party and the earliest curiosity of researchers about these victories occurred virtually simultaneously. focusing on the

Identification of the possible sources of support for the Nazis during this period, investigators from several disciplines have been uniform in their responses. Between

1928 and the final election of the Weimar period in 1933, the Nazis have generally been viewed as gaining considerable support from prior supporters of the Nationalist Party, prior supporters of several moderate or liberal "non-CathoI Ic middle-class parties", and individuals who, because of either ineligibility or disinterest, had not previously voted. Additionally, the bulk of this support has been seen as occurring among Protestant voters In rural portions of the Reich. I 9 6

The most startling improvement for the National

Socialists took place in the 1930 election when the

Party increased its share of the total vote from the

2.6 per cent it captured in 1928 to 18.3 per cent. This

election has further drawn the attention of researchers

because of its close temporal proximity to other events

of significance: the onset of the depression, the re­

structuring of the Nazi Party, the re-focusing of NSDAP

propaganda to attract the rural voter, the success of the

Nazis in several local and regional election, and the

substantial increase in voter turnout. Unlike the more

extended period, however, evaluations of the Nazi gains

in 1930 have been characterized by a marked lack of

consensus. For the most part, researchers have offered

one (or some combination of) the ihree groups advanced

as having most substantially aided the Nazis over the

longer time-period. In each instance,.the hypotheses

regarding the possible sources of increased support for

fhe Nazis can be identified with specific theoretical

agruments about the supporters .of social movements.

Waldman (1973) identified the view that the primary

source of the increase in 1930 came from those who had previously backed the centrist moderate and liberal middle- class parties with what he termed a "correspondence model",

This class or status model of social movements emphasizes the correspondence of a movement's ideology and its base of support within the population. It has frequently been 197

asserted that fascist movements, such as Nazi ism, are most

successful in attracting supporters from the middle class.

In particular, with the worsening of the economic situation

of the middle-class, especially the lower middle-class,

this section of the German electorate was seen as turning

to Hitler in an effort to stave off any further erosion of

their already declining status.

Turning to more directly political variables, Waldman

suggested that researchers stressing the role of the new

participants in the Reichstag elections were advancing the

"Party Identification Model" of political support. The

crux of this argument revolves around the degree of attach­ ment that individuals have to political parties. Supporters of this view argue that in multi-party political systems,

Individuals undergo a "binding In" of loyalty to one of the mainstream political parties. When these loyalties

have not been able to develop, frequently because of a short period of political participation, there is a greater

likelihood that Individuals will turn to radical or extremist political organizations or movements.

Finally, Waldman Identified those advancing the hypothesis that the Nazi Party gained at the expense of the DNVP as stressing the "National 1st Issue". The role of national Ism In attracting voters to the Nazis was seen as being particularly strong In several border districts which had been lost territory in the aftermath of World War I.

The significance .of this Issue was increased in these very 198

same areas by the large number of foreign workers migrating

to Germany foil owing the war and finding employment at a

time when unemployment among Germans was on the rise.

Those districts most strongly affected by these circum­

stances had, through the 1928 election, served as bases of

considerable support for the Nationalist Party.

In the absence of individual voting records,' investi­

gators have turned to the analysis of aggregate voting

statistics which are available at several levels of aggrega­

tion. Attempts to utilize more powerful statistical pro­

cedures in the analysis Qf. those data, however, have been

hampered by the statistical problems of the "ecological

fallacy" and multicollinearity. Recognizing that the

existence of these problems may have confounded the inter­

pretation of data and led to the disagreement about the

sources of Nazi Party gains in 1930, this study offered a

meihodological strategy aimed at minimizing these problems.

Specifical 1y, the variable and object typing components of

the BC TRY system of cluster analysis, in conjunction with

multiple regression and tabular analyses, were employed in

this capacity.

At the end of Chapter Three, six sypotheses derived

from prior analyses of fhe 1930 election were presented.

A summary evaluation of these hypotheses based on the application of these p 'ocedures to fhe relevant electoral and demographic statistics is offered below.

I. In terms of religious identification, the Nazis were much less successful in attracting Catholic I 9 9

voters than in gaining the support of Protestants. Similarly, the Nazis met with considerably more success in predominantly Protestant than in Catholic states, electoral districts, and counties.

The preceding analysis concurs with the findings of

virtually all prior research Into the rise of the Nazis.

The regression analysis of variable clusters at each level

of analysis uncovered the strongest negative relationshi p

between support for the Catholic parties and both NSDAP

1930 vote and 1928—1930 Nazi gains.

Similar conclusions must be drawn from an examination

of the object clusters derived in Chapter Six. For each of

the three 0-type solutions, those clusters representing

Catholic districts were consistently found to be the weakest

contributors to the Nazi mobilization. As with the variable

clusters, this observation holds for both of the dependent

variables employed throughout the analysis; the percentage

of the total vote received by the Nazis in 1930 and the

increase in the percentage of the total vote won by the National

Socialists between 1928 and 1930.

2. In Protestant areas (regions with less Catholic party backing), the Nazis made their greatest gains in rural rather than urban regions. Those gains made in districts with high Catholic party affilia­ tion were more extensive in urban rather than rural areas.

The data clearly support this hypothesis previously

advanced by Hunt (1964) and Waldman (1973). Examining the

relationship between Nazi gains and the two variables of

religion and the degree of urbanity/rurality , it can be concluded that the maximum Nazi success in 1930 was, indeed, 200

achieved in rural Protestant districts. The predominance

of Protestant regions in supplying the Nazis with the

massive backing gained in 1930 continued even in more

urbanized districts. The second strongest support tor

the NSDAP came in urban Protestant areas.

Regardless of the degree of urbanIty/ruraI ity, the

Nazi percentage of the total vote was uniformly lower

In Catholic districts. Within this category of areal units, the best NSDAP showing, in terms of 1928-1930 vote gains, were the most urbanized Catholic-party strong­ holds. Finally, the National Socialists we re.consI stent Iy weakest In the most rural portions of the Reich, the rural-

Cathollc areas.

3. Few inroads were made Into the ranks of the working class parties. The Communist Party was particularly effective in resisting the advance of the Nazis. The Socialist Party lost some support to the Nazis, but the number of SPD de­ fections to the NSDAP was far overshadowed by support from other sources.

That the Nazi Party received few votes In 1930 from those who had supported the KPD In the prior election is suggested throughout the data. First, while the Nazis had their strongest showing in rural districts, the Inclu­ sion of the variables measuring support tor the Communist

Party in variable cluster INDUST points to the different home bases of these parties. Second, even were this former point not th-e case, the ability of the KPD to not only maintain but also to Increase their support challenges 20 I

the view that defectors from the Communist Party came to

the aid of the Nazis in 1930.

Turning to the Socialist Party vote, rather strong

associations repeatedly appear between this variable and the

Nazi vote and vote gains. A closer examination further

clarifies the indirect nature of this relationship. In

the case of the variable clustering procedure, it can be

noted that much of the association between SPD support

and Nazi support results from the strong negative associa­

tion between areal support for each of these parties

and the strength of the Catholic parties. This po-int,

previously advanced by Waldman (1973), is strongly reaffirmed

in the present analysis.

Additionally, the absence of any extensive SPD

defections to the Nazis is also suggested by the nature of the SPDCAT V-type itself. Whi le this cluster included

the variables of Socialist Party support in 1920 and 1930, the change in SPD backing was found to have been more or

less random throughout all areas.

One cannot ignore the fact, however, that whi le the National

Socialists were experiencing tremendous growth between these two elections, and a drop in support occurred for most other parties, the SPD lost approximately 600,000 votes. As has been suggested previously, the bulk of these voters most

likely shifted their support to the KPD. But in the case of the Wahlkreise, for example, five districts saw the 202

Socialists lose more votes than the KPD gained. This

observation may suggest that the strong association between

SPD and Nazi strength is not merely an artifact of their

common religious base. Finally, It must be concluded that

while this particular shift in support was not the most sig­

nificant factor contributing to the growth of the Nazis,

some Socialist defections to the Nazis were quite likely.

The validity of this conclusion can be further enhanced

with the recognition that during this period of Party history, ’ c. the conflict between the highly nationalist Hitler and the

more socialist-oriented members of the NSDAP, such as the

Strasser brothers, was quite strong.

4. The Nazis benefited from the Increased parti­ cipation by newly eligible voters and previous non-voters. Whi le some new voters were attracted to the KPD, the bulk of this group was won over by the NSDAP.

This hypothesis has been one of the most controversial

surrounding research into the 1930 success of the Nazis.

Whi le many early studies were quite strong In its support,

several recent works have chai lenged the view that the

increased parti c’pation by the electorate contributed to

the growth of the Nazis.

Al I portions of the current analysis have been quite

cons i stent i n reaffirming these challenges. In the mul­

tiple regression analysis at each level of aggregation,

V-type TRNOUT provided the weakest contribution to Nazi

vote of all variable clusters. Some clarification of this finding is apparent in the object cluster stage of 20 3

analysis. The greatest increase in voter Turnout appeared 14 consistent 'in Catholic areas, p reci seIy ' the same districts

in which the Nazis had their worst showing in 1930. This

leads to the suggestion that many of these new voters could

be accounted for in the Catholic party increase.

Similarly, although the non-Catholic middle class parties

lost many votes throughout Germany, their support actually

Increased in nearly one-third of the Krei se and Wahlkreise.

Many of these districts were among those experiencing the greatest increase in voter participation. These parties could therefore be seen as the beneficiaries of this new voting bloc.

While some new voters in 1930 undoubtedly offered their support to the National Socialists, these data clearly mini­ mize the extent of this contribution. This further leads to the rejection of the "voter participation model" as an explanation for the rise of the Nazi Parly.

5. The Nazis' strong nationalist orientation provided an attractive alternative for many voters iv h o had previously backed t h e - D N V P . This was particularly true In bord r areas which had been forced to cede land in the period following Germany's defeat in World War I.

The data offered in the present analysis point to a very strong association between the National ist Party and the

National Socialists. The support for the Nazi Party in 1930 and the increase in NSDAP support between 1928 and 1930 were greatest in DNVP strongholds in which the Nationalists suf­ fered their greatest losses between these two elections. Both the V-type and 0-type analyses consistently point to these 204 205 20f5

associations.

An examination of maps 7.I and 7.2 provides a greater

indication of the significance of this finding. With the

numbers in each WahIkre i s representing the O-type in which

the district was grouped, the greatest 1928-1930 gains for

the NSDAP in map 7.I were found in 0-types 4 and, secondari ly

5. Map 7.2, indicating the geographical location of the

6-type WahIkre i s solution, the greatest Nazi gains were in

O-type 3. Turning back to map 3.I it can be seen that these

districts were paramount among those forced to give up

territory following the German defeat in World War I.

It must therefore be concluded that the "Nationalist

Issue" was, indeed, an important one in the swelling of the

Nazi vote between 1928 and (930. In particular, many voters

in lands hard hit by postwar reparations- appear to have turned against the local holders of power, the DNVP. The nation­ alist ideology and activist orientation of the Nazis attracted many supporters of the more staid Nationalist Party to the

ranks of the former.

6. The Nazis' strength grew as a result of the massive defections from Germany's many national and regional parties which had been backed by the Reich's middle class. Previous supporters of the German Democratic Party, the German People's Party, the Economic Party and other moderate, liberal, or centrist parties wtro turned to the Nazis as their economic security worsened with the onset of the depression in the early I 9 30's.

Since they suffered the most eve re defeats in the

1930 Reichstag election, prior supporters of the moderate and liberal middle class parties have often been pointed 2 on

to as having substantially contribulod to the success of

the Nazis In this election. While the current analysis

definitely suggests that there is some vaI idIty to this

view, the extent of this contribution must be viewed as

Less than many researchers have posited.

First, the losses of the German People's Party, the

Economic Party, and a combined centrist party vote were

strongly related to the dependent variable "Nazi percen­

tage of the Iota I vote cast". This cannot, however, be

said for the losses of the Democratic Party.

Second, turning to the "change in the percentage of

total vote case captured by the Nazis between 1928 and

1930", the picture is altered somewhat. The strong centrist-party areas were among the highest In their support for the Nazis in 1928, but the extent to which the NSDAP vote Increased was less than for the Nationalist strongholds.

As was noted previously, In many areas these parties gained rather than lost support. Additionally, In a few districts in which this collection of parties suffered their greatest losses, some of these votes were picked up by the Nationalist Party rather than the Nazis.

The data do suggest that whi le some considerable support tor the Nazis had come from the centrist parties of the middle class, they appear to have made less of a- contribution than the Nationalist Party. Thus, the "cor­ respondence model" may be seen as having been partially

validated In this analysis with respect to 2 0 8

the Nazi Party in the 1930 Reichstag election.

At this point some attention should be paid to the

applicability of these findings to the more general study

of ¡he social bases of fascism. It is initially necessary,

however, to offer some words of caution about such an

undertaki ng.

First, as was noted by Hagopian (1974:354), the

literature has focused on three related, although concep­

tually distinct, forms of fascism: (I) fascism as an

ideology, (2) fascism as a social movement, and (3) fascism

as a political regime. While all of these are relevant

to the study of the German National Socialists, this

analysis has been most clearly directed at the manifesta­

tion of fascism as a social movement.

Second, it is important to distinguish between at least

two levels of participation in social movements, particularly

those organized around political parties. While most of

the literature on social movement participants in general,

and fascist movement participants in particular, have been

concerned with the study of members, this analysis has ad­

dressed the larger category of supporters. In addition, the focus of this study has been sharpened even more by

paying primary attention to a single election. Although

the significance of this election cannot be denied, the

limitations fhis places on efforts to generalize to all

fascist movements must also be recognized. 209

Finally, care must be taken to avoid the "tendency to attribute the more salient characteristics of the Nazis to all extremists, and especially right-wing groups"

(Brandmeyer and Denisoff, 1971:95). While fascist movements may possess several unifying characteristics, "analyses of fascism have frequently suffered from an inability to sepa­ rate a general definition of fascism from specific depictions of its main varieties" (Leggett, 1973:160). As John Leggett further argued, "In fascism there is variety" (1973:160).

Recognizing these limitations it is still possible to identify certain consistencies between the findings of this study and the conventional sociological wisdom about fascism. The "mass party" designation of fascist movements has derived from the fact that some support for fascism is drawn from a variety of classes, generations, and status groups.

For the most part, however, fascism is sti I I seen as a class-based movement which "derives its principal support from and showers multiple rewards on a particular class

(Leggett, 1973:161). National Socialism, or what Lipset termed center fascism^has been portrayed by most observers as deriving most of its support from the bourgeoisie.

Every moment has a "carrying class," and Fascism is no different in this respect. Most scholars agree that one of the constituent classes of Fascism is the upper-middle class, which became apprehensive about the implications of liberal democracy (Roberts and Kloss, 1974: 162). 2 I 0

Support for this general hypothesis can clearly be

found In the findings of the current study. These findings

further suggest, however, that fascist support emerged

from various segments of the bourgeoisie. In the case of the Nazi Party In the 1930 Reichstag election, the field

Included members of the lower-, middle-, and upper-middle- class as welI as previous supporters of the middle-class's

liberal, moderate, and conservative political parties.

While the extent of support provided by each of these groups to the Nazis was not uniform, all played an important role

in the rise of German fascism. The broad base of support coming from the middle class necessitates the conclusion that no single explanation for the rise of fascism is wholly adequate.

The ultimate value of any research rests not merely on the answers it is able to provide, but also on the questions it uncovers for further investigatory efforts. Given the apparent benefits of the methodological techniques employed

In the current undertaking, many suggestions can be made which involve the application of this technique to a broader field of variables.

It was-noted earlier that the success of the Nazis In the early 1930's would have been considerably less likely had It not been for the growth of the technology of commu­ nication. The microphone and the radio, for example, can be argued as having served crucial roles in the NSDAP's 2 i i

strategy for gaining power. These devices were an insepar­

able link in the Nazi propaganda efforts. For the most part,

however, the impact of this technology and the campaign practices which they made possible, such as mass meetings,

have usually been linked to the study of the Nazi electoral success in an anecdotal manner. Unfortunately, the most systematic research into Nazi propaganda have only peripher­ ally addressed its success in particular areas or in bringing particular segments of the electorate into the Nazi camp.

A more complete investigation of the success of these efforts, and more indirectly the impact of these techno­

logical innovations, could be achieved through the incorpora­ tion of some of the vast body of data avai table on the frequency or number of mee, ings, orientation and volume of propaganda, or other relevant variables into the clustering process with electoral data.

Other efforts could focus on the key role of the economic crisis of this period

NSDAP. While it has been concluded that the Nazis achieved their greatest success in rural Protestant areas, it is quite clear that the extent of this success wasnot completely uniform in all such districts. Similarly, while 1929 generally signalled the onset of the depression in rural districts, the precise point at which this occurred- dif­ fered for various agricultural and mine products. These data, along with information on the particular commodities 2 ! 2

grown or extracted in each areal unit could be incorporated into the BC TRY analysis to provide a clearer indication of the role of the depression- in the rise of the Nazi Party.

Although researchers are in considerable agreement about the sources of Nazi support in fhe period between

1928 and 1930, a further examination of therelevant data may be worthwhile. A final suggestion for future research would Involve the incorporation of the statistics for all elections throughout this period into the clustering routine.

This would allow not only for an understanding of the shifts in support for the entire period, but also for each election under consideration. Finally, in light of the relevance of the methodological problems discussed in this study to these data and given that the consensus of opinion on this subject is no guarantee of its correctness, this approach could provide an even more complete picture of "who voted for

Hitler. tf 2 I 5

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Additional References

Brandmeyer, Gerard and R. Serge Denisoff 1971 "Status Politics: An Appraisal of the Applica­ tion of a Concept." Pp . 90-98 in Gary B. Rush and R. Serge Denisoff, eds., Social and Political Movements. Now York: AppIeton-Century-Crofts.

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Roberts, Ron E. and Robert Marsh Kloss 1974 Social Movements: Between the Balcony and the Barricade. St. Louis: C.V. Mosby Co.

Tryon, Robert C. and Daniel E. Bailey 1970 Cluster Analysis. New York: McGraw-Hill.