I
f)0.
AN INVESTIGATION OF
THE AIR FORGE OFFICER QUALIFYING TEST
Warren B. Shull
A Dissertation
Submitted to the Graduate School of Bowling Green State University in partial fulfillment of the requirements for the degree of
DOCTOR OF PHILOSOPHY
August 1975 Pagination Error <373 77/ 610300 /3 7¿-e-1 /3d,<3£/
ABSTRACT
(The Air Force Reserve Officers’ Training Corps (AFROTC) currently administers the Air Force Officer Qualifying Test (AFOQT) to all applicants for AFROTC scholarships and candidates for commissioning. This test contains five composites« pilot, navigator-tech- nical, dfficer quality, verbal, and quantitative A If a significant relationship exists between these scores and other indicators, AFROTC could conduct a less ex tensive aptitude testing program at a substantial savings.
Research was conducted to test the null hypotheses that there is no significant correlation between the various composite scores of the AFOQT as dependent vari ables and the various subscores and total/composite scores of the Scholastic Aptitude Test (SAT) and the American College Test (ACT) as independent variables. The group of persons who applied for four-year AFROTC scholarships during the 1973-74 academic year was used as the popu lation from which to sample.
The data collected and analyzed in this study indi cated a significant correlation between each composite of the AFOQT and both the verbal and mathematics subscores and the total score of the SAT as well as one or more subscores and the composite score of the ACT. Each AFOQT composite except the pilot composite can be adequately predicted from either the individual’s SAT or ACT score. Using the regression equations based upon SAT or ACT scores determined by this study, the AFROTC could make two pro cedural changes.
First, instead of requiring all scholarship applicants to take the AFOQT, the managers of the scholarship program could predict the relevant aptitudes of each applicant. The predicted scores could be used in lieu of the AFOQT scores to determine scholarship selection. Second, because accurate prediction of all AFOQT composites and the apti tudes they represent except pilot is possible, a new test covering only flying aptitude could be constructed and given only to pilot applicants.
iii ACKNOWLEDGEMENT
I wish to thank all the members of my committee; Dr. Virginia B. Platt, Dr. Willard Fox, Dr. Bill J. Reynolds, and Dr. Stewart Berry, and my chairman, Dr. Neil A. Pohlmann, for their assistance as I completed my research and reported the results. In addition, I would like to thank my wife, Martha, for the en couragement and support she has given me during the past four years.
iv TABLE OF CONTENTS
Page INTRODUCTION...... 1
The Problem...... 1
Background...... 1
Statement of the Problem...... 6
Justification...... 6
Orientation...... 6
Significance ...... 7
Delimitation...... 9
Purpose of the Study...... 9
Sample and Procedure...... 10
Treatment of Data...... 12
RELATED LITERATURE...... 14
AFOQT History...... 14
AFOQT Correlation...... 22
Prediction of Success...... 33
Achievement/Aptitude Tests...... 4l
ACT and SAT...... 44
PRESENTATION AND ANALYSIS OF THE DATA ...... 58
General...... 58
Correlations by AFOQTC omposite ...... 6l
Pilot Composite...... 6l
v VI
Navigator-Technical Composite ...... 71
Officer Quality Composite ...... 77
Verbal Composite...... 85
Quantitative Composite...... 92
Correlations by SAT Subscore ...... 99
Correlations by ACT Subscore...... 101
SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS .... . 103
Summary...... 103
Conclusions...... 105
Recommendations...... 107
Need for Further Research...... 110
BIBLIOGRAPHY 112 LIST OF TABLES
Table Page
1 Item Difficulty and Internal Consistency 17 of AFOQT Form L
2 Single Correlation Between the Various 18 Composites and Interest Items and ACE Tests
3 Effectiveness of Pilot Stanine in 25 Predicting Elimination from Pilot Training
4 Estimated Intercorrelations of Subtests 31 and Composites for AFOQT Form L
5 Estimated Reliability of Composites, 32 AFOQT Form M
6 Estimated Intercorrelation of Composites, 32 AFOQT Form M
7 Correlation between Aptitude Tests (MCT 40 and BI) and the CBQ and FBQ Items
8 Correlations between Predictor and Cri 45 terion Variables
9 Median Multiple R‘s and Standard Errors 47 of Estimate for Predicting College Grades, Using Tests (T Index), High School Grades (H Index), and the Combination (TH Index)
10 Multiple Correlation for College Freshmen/ 48 Sophomores for Criteria of Achieve ment Highly Comparable to High School Achievement Scales
11 Beta Weights and Multiple Correlations for 50 Predicting Academic Accomplishments
vii viii
12 Intercorrelations of High School Rank 51 in Class, ACT Program Variables, and GPA in Eight Semesters
13 Intercorrelations Among Subtests Scores 53 and Total Tests Scores for RQ and SAT
14 Correlations and Subtests Scores and 54 Total Tests Scores for RQ and SAT with Certain Subject Area GPAs (N=77)
15 Median Multiple Correlations of Pre- 56 dictors and Criteria
16 Means and Standard Deviations for the 59 Variables AFOQT and SAT
17 Means and Standard Deviations for the 59 Variables AFOQT and ACT
18 Simple Correlation Coefficients among 61 Composites of the AFOQT and Sub tests of the SAT
19 Simple Correlation Coefficients among 62 Composites of the AFOQT and Sub tests of the ACT
20 Distribution of Pilot Composites and 63 SAT-V Scores
21 Distribution of Pilot Composites and 64 SAT-M Scores
22 Distribution of Pilot Composite and 65 SAT-T Scores
23 Multiple and Partial Correlations between 66 the Pilot Composite of the AFOQT and the Verbal and the Mathematics Sub scores of the SAT ix
24 Distribution of Pilot Composite and 67 ACT-EN Scores
25 Distribution of Pilot Composite and 68 ACT-MA Scores
26 Distribution of Pilot Composite and 68 ACT-SS Scores
27 Distribution of Pilot Composite and 69 ACT-NS Scores
28 Distribution of Pilot Composite and 69 ACT-CO Scores
29 Multiple and Partial Correlations between 70 the Pilot Composite of the AFOQT and the English and Natural Science Sub scores of the ACT
30 Distribution of Navigator-Technical 71 Composite and SAT-V Scores
31 Distribution of Navigator-Technical 72 Composite and SAT-M Scores
32 Distribution of Navigator-Technical 73 Composite and SAT-T Scores
33 Multiple and Partial Correlations between 73 the Navigator-Technical Composite of the AFOQT and the Verbal and Mathe matics Subscores of the SAT
34 Distribution of Navigator-Technical 75 Composite and ACT-EN Scores
35 Distribution of Navigator-Technical 75 Composite and ACT-MA Scores
36 Distribution of Navigator-Technical 76 Composite and ACT-SS Scores
37 Distribution of Navigator-Technical 76 Composite and ACT-NS Scores X
38 Distribution of Navigator-Technical 77 Composite and ACT-CO Scores
39 Multiple and Partial Correlations between 78 the Navigator-Technical Composite of the AFOQT and the Subscores of the ACT
40 Distribution of Officer Quality Composite 79 and SAT-V Scores
41 Distribution of Officer Quality Composite 80 and SAT-M Scores
42 Distribution of Officer Quality Composite 81 and SAT-T Scores
43 Multiple and Partial Correlations between 81 the Officer Quality Composite of the AFOQT and the Verbal and Mathematics Subscores of the SAT
44 Distribution of Officer Quality Composite 82 and ACT-EN Scores
45 Distribution of Officer Quality Composite 83 and ACT-MA Scores
46 Distribution of Officer Quality Composite 83 and ACT-SS Scores 47 Distribution of Officer Quality Composite 84 and ACT-NS Scores 48 Distribution of Officer Quality Composite 84 and ACT-CO Scores 49 Multiple and Partial Correlations between 85 the Officer Quality Composite of the AFOQT and the Subscores of the ACT
50 Distribution of Verbal Composite and 86 SAT-V Scores
51 Distribution of Verbal Composite and 87 SAT-M Scores xi
52 Distribution of Verbal Composite and . 88 SAT-T Scores
53 Multiple and Partial Correlations between 88 the Verbal Composite of the AFOQT and the Subscores of the SAT
5^ Distribution of Verbal Composite and ACT-EN 89 Scores
55 Distribution of Verbal Composite and ACT-MA 90 Scores
56 Distribution of Verbal Composite and ACT-SS 90 Scores
57 Distribution of Verbal Composite and ACT-NS 91 Scores 58 Distribution of Verbal Composite and ACT-CO 91 Scores
59 Multiple and Partial Correlations between 92 the Verbal Composite of the AFOQT and the Subscores of the ACT 6o Distribution of Quantitative Composite and 93 SAT-V Scores 6l Distribution of Quantitative Composite and 94 SAT-M Scores 62 Distribution of Quantitative Composite and 95 SAT-T Scores
63 Multiple and Partial Correlations between 95 the Quantitative Composite of the AFOQT and the Verbal and Mathematics Subscores of the SAT
64 Distribution of Quantitative Composite and 96 ACT-EN Scores
65 Distribution of Quantitative Composite and 97 ACT-MA Scores 66 Distribution of Quantitative Composite and 97 ACT-SS Scores
6? Distribution of Quantitative Composite and 98 ACT-NS Scores
68 Distribution of Quantitative Composite and 98 ACT-CO Scores
69 Multiple and Partial Correlations between 99 the Quantitative Composite of the AFOQT and the Subscores of the ACT LIST OF FIGURES
Figure Page
1 Flying Training Success by Age and 36 Aptitude
xiii Chapter 1
INTRODUCTION
The Problem
Background
The United States Air Force Officer Qualifying Test
(AFOQT) is designed to evaluate aptitudes which are im
portant for commissioned officer performance and success.
The aptitude scores derived from this test battery may also be used in counseling and classifying officers into the most suitable Air Force occupational fields and special ties. The AFOQT is given to applicants for a commission.
It consists of a number of aptitude subtests arranged in several test booklets. These subtests are included in one or more of five aptitude composites. The choice of sub tests included in each aptitude composite is made on the basis of studies which have shown that applicants who do well on these types of subtests also tend to do well in re lated types of officer training.
The Pilot Composite Is a measure of some of the charac teristics necessary for successful completion of pilot training, including subtests of mechanical experience, 1 2
spatial information, and ability to understand and interpret
information received from aircraft instruments. Applicants
with high scores in this composite have considerably better
chances of completing pilot training than those with low
scores. The Navigator-Technical Composite is a measure of
abilities to interpret dials and tables, to understand
scientific and mathematical principles, and to comprehend
mechanical and spatial concepts. It is designed to predict
success in training courses requiring these abilities such
as navigator training, communications, electronics, main
tenance, engineering, and technical intelligence. The
Officer Quality Composite is primarily a measure of general
learning ability and officer quality. It contains measures
of verbal and quantitative aptitude and reasoning ability,
background knowledge relevant to world events, and an in
ventory of biographical material predictive of officer
leadership. Applicants with high officer quality scores
may be expected to do well in any technical training having
appreciable academic content. The Verbal Composite is a
measure of verbal skills« vocabulary, English usage, back
ground for world events, and verbal analogies. This com posite predicts success in courses such as administrative
sciences, personnel, education and training, and historical activities. The Quantitative Composite is a measure of 3
mathematical and arithmetical reasoning ability, and ability
to interpret graphs and tables. It is predictive of success
in training courses in statistical services, accounting, and disbursing.1
The Air Force Officer Qualifying Test dates back to a period years ago when a baccalaureate degree was not a pre requisite for a commission in the Air Force. Ever since
1964, however, all officers have been required to hold a bachelor’s degree before they could be commissioned. Either the Scholastic Aptitude Test (SAT) or the American College
Test (ACT) is required for admission to most colleges and universities in the United States.
The Scholastic Aptitude Test consists of two subtests: verbal and mathematics. Each is divided into two parts.
The first part of the verbal subtest contains ten questions each of sentence completion, antonyms, analogies, and five reading questions on each of two articles. The second part is made up of ten reading questions on each of two articles, eight sentence completion, eight antonyms, nine analogies, and five reading questions on each of three articles. The
^Air Force. Air Force Manual 35-8, Air Force Mili tary Peyssmugl Testing SLysAem. (Washington, DC: U.S. Government Printing Office, 1971). 4 verbal test is scored as 200 plus seven times the number
right minus twice the number wrong. The first part of the mathematical test is 17 computational problems and 18 suf ficiency data problems. The second part has 25 computa tional problems. The mathematics test is scored as 200 plus ten times the number right minus two and one-half times the number wrong.The possible range of scores is 200 to 800. The SAT was described by Wayne S. Zimmerman as being carefully constructed and thoroughly analyzed, with a wide range of scores to differentiate between high 2 and low, and with many alternate forms and good security.
The American College Testing Program examinations have four subtestss English usage, mathematical usage, social studies reading, and natural science reading. The first test consists of 75 questions covering grammar and punctua tion, sentence structure, diction, and logic and organization.
The mathematics test covers arithmetic and algebraic opera tions, arithmetic and algebraic reasoning, advanced algebra,
Martin McDonough and Alvin J. Hansen, The College Boards Examination» Complete Preparation for the Scholastic Aptitude”Test (New York» Arco, 1972), pp. 2-3.
p Wayne S. Zimmerman, "Scholastic Aptitude Test" in The Sixth Mental Measurements Yearbook, ed. Oscar Krisen Buros, vol. 1 (Highland fcark, N.J.» Gryphon, 1972), p. 707. 5
and geometry. The social science test includes European
and Ancient history, government and American history, cur
rent social issues, sociology, and economics. The natural i science test covers biology, chemistry, physics, geology, astronomy, and general science.\ The range on the ACT is
1-36. The median for an unselected national sample of
first semester high school seniors is 16; for college bound 2 first semester high school seniors the median is 20.
The verbal composite of the AFOQT seems to be measuring
the same things as the English subtests of the ACT and SAT.
Similarly, the quantitative composite of the AFOQT appears
to measure the same abilities and skills as do the mathe
matics subtests of the ACT and SAT. The officer quality
composite of the AFOQT is a weighted combination of the
verbal and quantitative composites, and therefore, it might
have some relationship to the composite score on the ACT.
Furthermore, the pilot and navigator composites both in
clude information on scientific and mechanical principles,
------— .------American College Testing Program, Highlights of the ACT Technical Report (Iowa City» Research and Development Division, 1973), PP « 5-10. p American College Testing Program, Using the ACT on the Campus; A Guide for the Use of ACT Services at Insti tutions of Higher Education (Yowa City» ACT, 1970), p. 5* 6
which may be similar to the natural science subtest of
the ACT.
Statement of the Problem
Is there a significant relationship between the com
posites of the AFOQT and the sub-scores on the SAT and/or
ACT? What is the possibility of prediction of AFOQT scores,
or the aptitudes they represent? How efficient or precise
might these predictions be?
Justification
Orientation
In 1971 Dr. Glenn C. Terrell, a member of the Air
University Board of Visitors, made some highly critical
comments about the use of the Officer Quality Composite of
the AFOQT as a screening device. He stated that the OQC
section of the AFOQT is unpredictable and unreliable as a measurement tool. By continuing to use it the Air Force may not be selecting the best officer material for the ad vanced AFROTC program. According to Terrell the OQC, which records verbal, aptitude and biographical information, is used as a screening device to predict which cadets best re flect qualities the Air Force wants in its officers primarily because fifteen years ago a statistician found a high cor relation between the OQC scores and the grade point average 7
(GPA) of AFROTC and U.S. Air Force Academy cadets. More
recent data shows a very low correlation between the two
factors. Terrell recommended that the Air Force undertake
a thorough study of the OQC and if low correlation is sub
stantiated, AFROTC should then in place of the OQC, rely
on officer evaluations and GPA or the latter and SAT scores
to screen cadets being looked at for entrance into the ad-
vanced program. In spite of this warning by a highly respected educator, AFROTC continues to use the AFOQT,
including the OQC, as a heavily-weighted factor in screen ing applicants for the advanced program.
Significance
If, as is expected, there proves to be a significant relationship among certain of the subtests of the ACT and
SAT and the composites of the AFOQT, then there is no need for the Air Force to duplicate testing for the same things.
The test is now being given to most applicants for an Air
Force commission. AFROTC alone tested over 14,700 appli cants between July 1, 1973 and June 30, 1974.2 Other Air
XROTC (AF Officer) Qualifying Test Hit — Other Parts of Program Praised,” Air Force Times, 31 (June 9. 1971). 47-
^Air Force Reserve Officers Training Corps. AFOQT Overall Means Report (Maxwell Air Force Base, Alabama: KtT? 197^,"pVT? 8
Force agencies also test large numbers of persons. In
AFROTC the average number of persons tested at a time is six.1 This means that the test battery was given a total I • o£ over 2,400 times in AFROTC. The test is six hours in
length. The number of man-hours involved in administering,
taking, and scoring the AFOQT annually is enormous. Based
upon the hourly pay rate of the lowest ranking personnel
involved in administering the AFOQT, $4.84 per hour, the
Air Force spent over $70,000 last year in administering the
test alone. The cost of grading the tests is $1,800 in p computer time. There is also an unknown handling cost.
The cost of testing to the person being tested is incalcul
able. The grand total spent by Air Force ROTC annually on
the AFOQT is close to $75,000. Based upon the results of
the survey, the AFOQT could be redesigned to eliminate its redundant portions. This action would benefit both those
individuals applying for an Air Force commission by not re quiring them to duplicate a prior test and those in the
Air Force who are responsible for giving the AFOQT by re ducing their workload.
^Interview with M. Meriwether Gordon, Educational Specialist, AFROTC, January 23, 1975«
^Interview with Capt L. T. Smith, Chief, Data Auto mation Operations, 3841st Comptroller Service Squadron, January 31, 1975« 9
Delimitation
The literature reveals a great deal of evidence that mental tests such as the ACT, SAT (and AFOQT) are strongly- influenced by a set of variables which might be generally summarized as the socioeconomic status of the respondent.
Inasmuch as these factors are not known to the Air Force, they cannot be measured and examined for significance.
Purpose of the Study
This study examines the null hypothesis that there is no significant relationship between an individual’s scores on the various components of the AFOQT and the sub scores of the SAT or ACT. There are 90 subhypotheses.
Seventy-five pertain to the ACT and fifteen pertain to the
SAT; eighteen are associated with each composite of the
AFOQT. The first subhypothesis, H^, states that there is no significant relationship between an individual’s score on the pilot composite of the AFOQT and his score on the
ACT English subtest. The other subhypotheses, H^. . .H^, state that there is no significant relationship between each of the five scores on the AFOQT, in turn, with each indivi dual and combination of parts of the ACT.and SAT. The study is based upon two concepts. One concept is that 10 there exists a general applicability of aptitudes. Another underlying concept is that duplication of effort is contrary to the concept of administrative efficiency in any educa tional organization.
Sample and Procedure
The Air Force Reserve Officers Training Corps is the largest single source of new Air Force officers entering active duty each year. Between 3,500 and 4,000 officers are commissioned annually through the AFROTC program. As such, persons administered the AFOQT by the Air Force ROTC represent a large segment of the total number tested.
Within AFROTC those who have applied for a four-year col lege scholarship represent almost 30 per cent of the total number tested. This group of scholarship applicants is the only group for which both Air Force Officer Qualifying
Test and college entrance examination scores are known.
As such, AFROTC scholarship applicants represent a sample of the total number of people who have taken the examination.
The Air Force ROTC scholarship applicants are required to submit to AFROTC either their SAT or ACT scores and also to complete the entire Air Force Officers Qualifying Test.
For academic year 1973-74 over 17,000 people had their ACT or SAT scores reported to Air Force ROTC. Fewer than 5.000 11
went to the trouble of taking the AFOQT test. While there are individual case files maintained on all those who com pleted the application process, the use of these to collect data would be unreasonably time consuming. There is, however, a pair of rosters from which data can be gathered quite easily. One is an alphabetical composite roster of college board scores by applicant which includes name, social security account number (SSAN), and either the ACT or SAT scores. The other is a numerical roster which in-, eludes SSAN, name, and AFOQT scores of each applicant tested.
The procedure of this study was to collect data on a random sample of scholarship applicants. This included all five composites of the AFOQT and each of the subtests and the composite for the ACT or the SAT for the individuals in the sample. The data were collected by the researcher through a process of selecting every nth SSAN on the AFOQT roster and then finding that person’s name on the SAT/ACT roster. If the person had taken both tests, the scores were recorded on the data collection sheet. In some cases there were AFOQT scores recorded but no SAT or ACT; these people never had the entrance exam scores reported to Air
Force ROTC. In these cases the researcher went to the next 12
SSAN on the roster. Individuals who had taken the SAT and the AFOQT were recorded separately from those who had taken the ACT and the AFOQT. No one took both the SAT and the ACT, so no intercorrelation between the two entrance examinations was possible. The data that were recorded in this fashion represented a random sample of the parameters as they existed in the population of persons who took the AFOQT during academic year 1973-74• The sample included l6o people who took the ACT and 341 who took the SAT.
There appear to be no limits to the external validity of this study caused by using records of AFROTC scholarship applicants as sources of data. Inasmuch as the principal thrust of this study was to determine what, if any, re lationships existed between the composites of the AFOQT and the subtests of the SAT and ACT, demographic variables such as race, sex, income of parents, and education of parents which might influence performance on the tests are irrelevant because they would effect both the tests in the same manner. These other variables, therefore, have no ef fect upon the internal validity of the study. For this reason, no other variables were included in this survey.
Treatment of Data
The statistical procedure used to analyze the data was the coefficient of correlation and regression analysis. 13
There were eight independent variables* the five ACT scores for one sample and the three SAT scores for the other sample. The five dependent variables, the five composites at the AFOQT, were the same for both samples
For the ACT sample a total of five partial and six two- way and four three-way multiple correlations were com puted for each dependent variable, a total of 75 corre lations . For the SAT sample a total of three partial correlations were computed for each dependent variable, a total of 15 correlations. Chapter two is a review of the literature related to this study. Chapter three contains a complete presentation and analysis of the data. Chapter four contains the summary, conclusions, and recommendations. CHAPTER 2
RELATED LITERATURE
AFOQT History
The Air Force’s use of selection and classification test instruments for officer personnelStarted early_in
.World War II with the development and use of the Aviation
Cadet Qualifying Test and the Aircrew Classification bat teries. Current officer testing programs were developed from research on the aircrew batteries and from a second line of research with the Aviation-Cadet Officer-Candidate
Qualifying Test which began in 1949.The Air Force Offi cer Qualifying Test appears in editions, called forms, which are used for two or three years. The revised edition is then used throughout the Air Force. The edition for this investigation, Form L, was used throughout Air Force
ROTC from January 1, 1972 through February 28, 1975» The cycle of each test is substantially the same.
1Lonnie D. Valentine, J'r., and John A. Creager, Officer Selection and Classification Tests» Their Develop ment and Use (Lackland AFB, Texas» Personnel Laboratory, Aeronautical Systems Division, 1961), p. 2.
14 15
The United States Air Force first developed jthe Air Force Aircrew Classification Battery in 194?.X From^this
examination was derived the Air Corps Officer Candidate
Qualifying Test which in turn led to the AFOQT in 1953«
These batteries of tests were compiled by the Air Force’s
Human Resources Research Center at Lackland Air Force Base,
Texas. They were validated against air cadets, officer
candidates, and Air Force Reserve Officers Training Corps p (AFROTC) graduates and undergraduates.
The testg_jmera -developied along two related routes «
(1) the series of selection and classification devices
started during World War II, and (2) the Aviation Cadet-
Officer Candidate Qualifying Test used first in 1949 for
aircrew prescreening and later for non-aircrew officer — - ■ — ■ — ■ - —■ ' - • selection and classification.Beginning in January, 1942,
XJoy Paul Guilford, et. al., "A factor-analytic study of Navy reasoning tests with the Air Force Aircrew Classi fication Battery," Education and Psychological Measurement, 14 (Summer, 1954’)« 302.
^William E. Byers, The Degree to Which the Air Force Officer Qualification Test Battery Measures Factors that are Independent of more easily obtained data~ (Madison« IJnxversity“of“Wisconsin, 1954j , p. J.
^Lonnie D. Valentine, Jr., and John A. Creager, Officer Selection and Classification Tests« Their Development and Us”e3tackland Air Force Base, Texas« Personnel Laboratory, Aircraft Systems Division, 1961), p. 1. 16
the Army Air Force Qualifying Examination served as the
preliminary selective device for officers in the aircrew.
It was designed to select men who possessed the qualities
of leadership and judgment that enable them to handle merl
under their command and to maintain the personal standard of the officer corps^.1 '“The various editions of each of the
four generation tests have been tested for both difficulty
and_internal consistency. The most recent edition of the
AFOQT is Form L which was used throughout AFROTC from
January, 1972 through February, 1975* Table One shows
the results of a study of the reliability of Form L indi
cating that all parts of the AFOQT demonstrate a high degree
of internal consistency and have similar difficulty.
An attempt has been made to correlate earlier genera
tion tests with commercially available examinations. Table
Two displays the results of a study which found a wide range
of correlation between ACE tests and AFOQT parts.
<6 Shortly after World War II, when manpower requirements
were sharply reduced, the Air Force imposed a requirement
that all officer candidates must have completed two years
1 Frederick Barton Davis (ed.), The Army Air Force Qualifying Examination (Washington, DC» U.S. Government FHrrtlngOfflee, 19^7), pp. 9-H • 17
TABLE 1
Item difficulty and internal consistency of AFOQT Form Li
Internal Difficulty Consistency Range Median Range Median
Quantitative Aptitude .12-.88 • 56 .19-.87 .47 Verbal Aptitude .23-.83 • 52 .32-.78 •54 Scale Reading .22-.93 •58 .17-.82 .44 Aerial Landmarks .21-.86 .62 .24-,80 • 54 Gen'l Science .21-.80 • 56 .29-.88 • 53 Meeh Info .29-.88 .58 .23-.83 • 58 Meeh Principles .32-.84 .57 .18-.79 .54 Aviation Info .27-.82 • 52 .24-.90 .52 Visualization of Maneuvers .22-.95 • 56 .06-.6l .35 Instrument Comp .23-.97 • 52 .06-.71 .43 Stick & Rudder Orientation .45-.96 • 76 .28-.83 • 52
(Note» Based on samples of 400 or more student officers
Robert E. Miller, Development and Standardization of the Air Force Officer Qualifying Test - Form L TTack'XandAPB“,"Texas» Sir Force Human Res our c es Labora tory, 1972), p. 2. TABLE 2
Single correlation between the various composites and interest items and ACS tests!
Flying Of Ver Flying Adm Tech Quant Aptitude ficer bal Tech Quant Interest Interest Interest Interest
ACE Quanti .18 .50 .49 • 51 • 55 .10 .09 .29 .27 tative Test
ACE Lan • 39 .61 • 52 ■ 50 .62 .02 .10 .32 .20 guage Test
1 Byers, p. 40.
H* 19
of college. In 1952, due to the increased numbers of of
ficers needed in the Korean War, this requirement was
dropped.
Since the rated Air Force officer usually per forms a variety of nonflying as well as flying duties in the course of his career, it was de sirable that a minimum cutoff point on verbal and quantitative intellectual factors be es tablished so as to increase the probability of selecting cadets whose 'officer quality' would fit them for officer duties in addition to flying.!
This need led to an increase in^Ahe—number-of—verbal and_
^quantitative questions. A short time_later researchers
found that the Officer Quality Composite (OQC) of the AFOQT
j,s__pxii5arily a measure of "intelligence^—rather_jfchan of of-
-fi.cer qualities. The use of the OQC was believed preferable
to college entrance examinations because insufficient in
formation is available to establish equivalent scores on
the various devices then in use. ’Since that time the Air
Force has continued to use the Air Force Officer Qualifying
Test as a screening device for persons requesting entry in
to the Air Force's precommissioning programs.
~- - -- T—- - — Major Willys W. Folsom, Development of a Revised Officer Quality Stanine Effective with the March 1952 Air- crew Classification Battery (Lackland AFB, Texas» Harman Resources Research Center, 1952), p. 1. ? 'Raymond E. Christal and John D. Krumboltz, Use of the Air Force Officer Qualifying Test in the AFROTC Selection Program (Lackland AFB, Texas» Air Force Personnel and Train ing Research Center, 1957), P* 35« 20
The AFOQT-64 replaced the Form G in September 1963.
A period of standardization was conducted which yielded
reliability calculations, distribution statistics, and
item statistics. Because the United States Air Force
Academy no longer gave the AFOQT to all applicants/ the
normative base for the AFOQT-64 was the nationwide twelfth
grade male population, A^new test for use at the student
officer level was constructed for implementation in'/iscal
year 1966. The AFOQT-66 succeeded the AFOQT-64 in the
normal two-year replacement cycle and follows it closely
in format, content, arid procedures for construction and
standardization. The standardization was accomplished in
a manner which permits relating scores on the new test to
performance of Air Force Academy candidates and twelfth grade 2 males even though the latter groups did not take the AFOQT.
The AFOQT-68 closely resembles the previous form in type of
content, organization, and norming strategy. Standardiza
tion was accomplished in the same manner as the 1966 edi tion. A new feature of AFOQT-68 was the provisions of
Robert E. Miller and Lonnie 0. Valentine, Jr., De velopment and Standardization of the AFOQT-64 (Lackland AFB, Texas» Personnel Research Laboratory, 1964), p. 1.
2 Robert E. Miller, Development of Officer Selection and Glassification Tests 1966 (“Lackland AFB, Texas* Personnel Research Laboratory, 1966), p. 3, 21
separate norms for AFROTC and other use. These norms took
into account the effects of differences in level of formal
education at the time of testing in various commissioning programs.'1' This new feature resulted from a reliability
study which had set out to evaluate the hypothesis that
maturation and education have an elevating effect on AFOQT
scores.
Since the AFOQT is administered at different educa-
tional levels for the several commissioning programs, dif
ferences which are largely spurious exist between the
programs with respect to their score distributions, ^o
evaluate the extent of differences produced by maturation
and education, the AFOQT was administered experimentally-
riearthe end of their senior year to 4l5~ÄFROTe—cadets—in—.
32 institutions. Scores were compared5otE~tRbsn—obtained
for the same group when they were tested as freshmen or r ’ ’ ' ■ ■ .. ... ~ ~ ' - ■ . sophomores for selection by the AFROTC program. For the
experimental group as a whole, the ficer Quality^score
showed an increase of approximately 30 percentile points
over the national mean for AFROTC applicants. The increase
was -greatest„for. cadets -who—were—flying candidates and those
^Robert £. Miller, Development of Officer Selection and Glassification Tests — 1968 [Lackland AFB, Texas* Air Force Human Resources Laboratory, 1968), pp. 1-2. 22
majoring in scientific or technical subjects. Because of
statistical artifacts, the increase was greater for those
whose initial scores were high. The increase in Pilot
scores for the total group was about~~2'0 percentile points,
with the greatest increase (30 to 50 points)occurring"''
among those who received light plane training as part of
the AFROTC curriculum. The increase in the Navigator-
JFechnical scores amounted to about six points for the total
group but it approached 30 points for cadets majoring in
scientific or technical subjects whose initial scores were
below the 75 percentile. Those cadets had initial scores
about 30 points higher than cadets in nonscientific pro
grams, and this difference persisted in the final testing.
The data also permitted determination of test-retest reli abilities and intercorrelations of AFOQT scores.X JThe^
scores received on the Air Force Officer Qualifying Test
have been correlated with many different variables.
AFOQT Correlation
The United States Air Force has been using the Air
Force Officer Qualifying Test, in its various forms, as a selection and classification device for over three decades.
——:— G. Gregg, The Effect of Maturation and Educational Experience on AFOQT Scores "(Lackland AFB, Texas: Air” Force Human Resources Laboratory, 1968), pp. 2-4. 23
During the intervening time much has been written to in
dicate that the various composites are valid, that is, that
they are predictive of relative success after commissioning. The earliest attempt at correlation goes back to Form A.
During the latter half of 1957 criterion data matured on
those nominees who had entered Navigator Training after completion of AFROTC. The aptitude composites and sub
tests of the AFOQT were validated against three criteria
of success in Navigator Training. It was found that the
Navigator-Technical composite of the AFOQT was a valid predictor of success in Navigator Training for this popu lation.1
A study of 975 reserve officers in seven different
technical courses provided data on the predictive validities
of AFOQT composite scores for final technical course grades. Satisfactory validity coefficients were obtained for the AFOQT aptitude composites against the course criteria.
Most of the composites were valid for each separate criterion, s and coefficients as high as .58 were obtained. These validities persisted in different samples of officers
enrolled in the same course at different times. Validities
1Lonnie D. Valentine, Jr., Validity of the AFOQT (Form A) for Prediction of Student-Officer Success m Observer Training (LacklandT^FB, Texas: AirForce, 19), p. 1. 24
of the AFOQT interest composites were markedly lower and
frequently negative. The highest in terms of absolute value was . 32.X On the earlier Glassification Battery,
one of the predecessors of the AFOQT, a validity study conducted with success at Undergraduate Pilot Training
being the criterion of success. The pilot score was cor
related at .54 with Undergraduate Pilot Training (UPT) elimination, .34 for non-flying deficiency elimination,
and .21 for reading comprehension. Instrument comprehen
sion was correlated at .31, mechanical principles at .28,
mechanical information at .27, biographical data at .27,
and general information at .45 for flying deficiency¿elim- 2 ination. Intercorrelations between the Aviation Cadet Qualifying Test composites and the 1951 Aircrew Classi
fication Batteries, both forerunners of the AFOQT, were
.65 between the two pilot scores, .70 for the navigator scores, and .77 for the officer quality scores.-^
^Robert E. Miller, Prediction of Technical Criteria from AFOQT Composites (Lackland AFB, Texas: Personnel Lab oratory, Air Research and Development Command, i960), p. 2. 2 John T. Dailey and Donald B. Grogg, Postwar Research on the Classification of Aircrew (Lackland AFB, Texas: Human Resources Research Center, 1949), pp. 13-37• -''Capt Virginia Zachert and SSgt Franklin L. Hill, The Aviation Cadet Qualifying Test, PR73 and 3A, compared with the April 1931 Aircrew Classification Battery (Lackland AFB"," Texas: Human Resources Research Center, 1952), p. 6. 25
A 1957 study of the Air Force Officer Qualifying Test found that the pilot score could be used as an effi
cient predictor of elimination from pilot training. The
stanine score is approximately equal to a percentile score divided by ton. The survey revealed an obvious relation ship between the pilot stanine and elimination. (Table 3)
TABLE 3
Effectiveness of pilot stanine in predicting elimination from pilot trainingl
Pilot Stanine Number % Eliminated (N=185, 367) 9 21,474 4 8 19,440 10 7 32,129 14 6 39,398 22 5 34,975 30 4 23,699 40 3 11,209 53 2 2,139 67 1 904 77
Whereas the overall attrition rate was 24 per cent, the data clearly showed that the pilot score was related to elimination; the likelihood of elimination could be pre dicted from the pilot score.
-^Raymond E. Christal and John D. Kaumboltz, Use of the Air Force Officer Qualifying Test in the AFROTC Selection Program (Lackland AFB, Texas; Personnel Laboratory, 1957), p. 2. 26
A battery of experimental tests was administered on
entry to the Air Force Academy class of 1962 to determine
which ones best predicted course grades. Prediction of
mathematical and science course grades was best accom plished by the Quantitative composite of the AFOQT, while
the English achievement test of the College Entrance Ex
amination Board was the most adequate for the prediction of English grades. These findings were consistent with those from other Academy classes.1 Of the first class
graduating from the Air Force Academy, 1?2 entered flying training. Scores from the Academy selection tests given
five years earlier were correlated with a pass/fail cri
terion in both Primary and Basic Flying Training and with final grades in the latter. None of the College Entrance
Examination Board scores was predictive of success in fly ing training. The Pilot composite of the AFOQT had moderately high validity for passing both Primary and
Basic Flying Training. Neither of the sets of selection
tests showed much discrimination for final grades of the 2 successful students. In another survey, scores on the 2
^Robert E. Miller and John A. Creager, Predicting Achieve ment of Cadets in their First Year at the Air Force Academy, Class of 1967 (Lackland AFB, Texas* Air Force, i960), pp. 3-5« 2 Lonnie D. Valentine, Jr., Air Force Academy Selection Variables as Predictors of Success in Pilot Training (Lack- lancTAFB” Texas* Aerospace Systems Division, I96I), p. 1. 27
Pilot, Navigator-Technical, and Officer Quality composites
of the AFOQT were compared with measures of success in un
dergraduate pilot and navigator training. The samples
consisted of 4,993 student pilots and 2,132 student naviga tors who entered training over a period of approximately
two years. Each sample was subdivided according to source
at commission and, in the case of the pilot sample, by type
of aircraft and curriculum. Correlations of AFOQT compo
site scores with criteria of success were computed within each of 16 samples and subsamples. The criteria used were
training grades and graduation versus elimination by various categories.X In general, there was good prediction of
training grades, academic elimination, flying deficiency elimination, and elimination for all reasons combined.
Elimination for motivational reasons was predictable for some groups. Military elimination occurred infrequently but was negatively predictable in the total navigator
sample. 2 In a more recent study, Miller summarized a large
body of data relevant to the proper interpretation and
^Robert E. Miller, Relationship of AFOQT Scores to Measures of Success In Undergraduate Pilot and Navigator Training (Lackland AFB, Texas: Personnel Research Labora tory, 1966), p. 2. 2Ibid., p. 5. 28
use of aptitude scores on the AFOQT. His technical data
includes an extensive sampling of validation studies
covering prediction of success in pilot training, navi gator training, technical training, and academic courses. The test vzas found to be reliable and there was a signi ficant intercorrelation among subtests.1 Another study-
investigated the joint and independent relationships between aptitude test performance and certain demographic and cultural variables, as well as the relationship be tween these variables and the aptitude test factor content. Multiple linear regression analysis indicated that there were significant interaction effects. The relationship between the combined cultural variables and each aptitude test was significant for all tests. Significant net re lationships of race, educational level, and geographical area were found with a majority of tests although wide dif ferences were found among aptitude tests in their sensitivity to demographic and cultural influences. With regard to factor content, race appeared to be related to tests in most factor areas, with its highest relationships in the
Robert E. Miller, Interpretation and Utilization of Scores on the Air Force Officer Qualifying Test, (Lackland AFB, Texas; Air Force Human Resources Laooraiory, 1969), PP« 2-5- 29
mechanical area. Education had the highest relationships
with verbal, numerical, and reasoning factors and the
lowest relationship with the mechanical area. No dis cernible trend with regard to factor content was noted for geographical area.1
In 1970 the AFOQT Form K replaced the AFOQT-68.
It v/as standardized to insure the same degree of validity
and reliability as existed on previous editions. Another
study was designed to explore the relationships between
aptitude index composite and final school grade in tech
nical training for various cultural subgroups based on
race, educational level, and geographical area. Regressions of final school grade on aptitude index were compared for the different subgroups in ten samples of technical school
graduates. The results indicated that where the relation ship between aptitude score and performance in technical training differed for the various subgroups, the performance of blacks and high school non-graduates was overestimated.
No consistent trend in prediction error was noted for the
iNancy Guinn, Ernest C. Tupes, and W.E. Alley, Demo graphic Differences in Aptitude Test Performance (Lackland AFB, Texas: Air Force Human Resources Laboratory, 1970), pp. 7-9. p Robert E. Miller, Development and Standardization of the Air Force Officer Qualifying Test Form K (Lackland AFB, Texas: Air Force Human Resources Laboratory, 1970), p. 1. 30
various geographical areas across all technical schools.
There was, however, a general tendency for the final school
grade for people from the North-Northeast area to be over
predicted while those from the Far West-Pacific Coast area tended to be underestimated.1
Form L was the edition of the Air Force Officers Qual
ifying Test which was the object of this study. In an
earlier study of Form L at the time of its publication, Robert Miller, who by sheer volume written about the AFOQT
must be considered a foremost authority on it, analyzed
the various subtests and composites. The results of the analysis, shown in Table 4, revealed a wide range of in
tercorrelations of AFOQT subtests and composites. In his analysis of Form M, v/hich was first used in
Air Force ROTC on March 1, 1975» Miller estimates both the reliability and intercorrelations of the five composites.
The high reliability indicates a probability of comparable scores being achieved on subsequent test administrations.
Miller's results are shown in Table 5« The range of inter correlation could be a function of the overlap among compo sites, that is, the degree to which they measure the same
^ancy Guinn, Ernest C. Tupes, and W.E. Alley, Cultural Subgroup Differences in the Relationships Between Air Force Aptitude Composites and Training Criteria (Lackland AfTm Texas* Air Force liuman Resources Laboratory, 1970)« P« 8« TABLE 4
Estimated intercorreiations of subtests and composites for AFOQT Form Li
Subtest or Composite 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
1 Quantitative Aptitude 2 Verbal Aptitude 35 3 Officer Biographical Law 99 17 4 Scale Reading 58 26 yf 5 Aerial Landmarks 28 16 05 33 6 General Science 57 43 0! 31 17 7 Meeh Info 32 14 (06) 16 06 50 8 Meeh Principles 45 21 (09) 29 22 5^ 62 9 Pilot Bio Inventory 00 (16) y 02 08 10 42 28 10 Aviation Info 20 38 07 16 16 47 ^5 43 26 11 Visualization of Maneuvers 27 27 02 28 30 33 30 35 19 36 12 Instrument Comprehension 29 23 05 29 30 35 30 39 26 38 51 13 Stick & Rudder Orientation 29 16 02 28 30 26 27 41 26 36 42 51 14 Pilot Composite 36 23 04 31 31 50 67 70 60 65 65 72 74 15 N-T Composite 86 39 05 70 53 71 55 68 16 40 42 ^5 43 62 16 OQ Composite 76 74 55 48 25 52 22 31 (02) 32 28 2? 25 33 69 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Robert E. Miller, Development and Standardization of the Air Force Officer Qualifying Test Form L (Lackland AFB, Texas: Air Force Human Resources Laboratory, 1972), p. 4, 32
TABLE 5
Estimated reliability of,composites, AFOQT Form
Composite Reliability
Pilot .91 Navigator-Technical .95 Officer Quality .94 Verbal .89 Quantitative .93
thing, or it could be a function of the correlation between
aptitudes being measured. The results are shown in Table 6.
TABLE 6
Estimated intercorrelation.of composites, AFOQT Form Mz
Composite Pilot N-T OQ V
Navigator-Technical .70 Officer Quality .50 • 79 Verbal .43 • 57 .80 Quantitative • 55 .87 .85 .55
Robert E. Miller, Development and Standardization of the Air Force Officer Qualifying Test Form M (Lackland AFB. Texas« Air Force Human Resources Laboratory, 1974), p. 7.
2Ibid. 33
Miller's 1974 study goes beyond Miller's earlier studies
or those conducted by Guinn, Tupes, or Valentine; it cor
relates AFOQT scores with other external predictors of
success.
Prediction of Success
Much has been written on the subject of the predic tion of success in an academic endeavor. Because the Air
Force uses the Air Force Officer Qualification Test as a measure of the probability of success in college, techni cal training, or flying training, this section will dis cuss only what has been written about prediction of success in one of those areas. A test "should be reliable enough to provide a fairly accurate indication of an individual's relative standing in terms of the characteristics measured; the really important consideration is predictive validity."1
The objective in prediction is not to develop identical sets of variables for all individuals in all situations but those most critical groups of variables which will take 2 into account differences in individuals and in situations.
_ r...... Leona Elizabeth Tyler, Tests and Measurements, 2d ed. (Englewood Cliffs, NJ» Prentice-Ball, 1971)» p« Bl. ? Morris Isaac Stein, Personality Measures in Admissions; Antecedent and Personality Factors as Predictors of College Success (NY; College Entrance Examination Board, T3>65), pT 6l. 34
A word of warning about testing appeared regularly.
"Aptitude tests do not measure determination, persever ance, and interest in learning."1 2 To this extent they
will be less accurate as predictors of academic success.
"Within a context of predicting a specific criterion, the
predictive validity of the test is the focus of major
concern . . . because the use of a criterion variable
to investigate the fairness of a test places a very heavy reliance on the assumption that the criterion variable is
itself fair." How, for example, does one measure officer quality? Goslin found little evidence that on-the-job evaluations correlate either with training grades or with test scores. He attributed this to three different reasons. First, the skills and abilities required on the job may be quite different from those required for out standing performance in technical training. Secondly, the influence of situational variables on a given individual’s behavior is unpredictable. Third, a large portion of the error in prediction is contributed by poor on-the-job
1Louis K. Wechsler, Martin Blum, and Sidney Friedman, College Entrance Examinations, 2d ed. (New York« Barnes and Noble, 1
evaluation.1 2 L*a v4in cautions that just because two vari
ables are found to be related there is not necessarily a
causal relationship. He also found that although a large
number of variables appear to be useful in the prediction
of academic performance, a close examination would re
veal that the variables are not independent
In a study of the Navy flying program it was found
that Naval ROTC students take an Aviation Qualification
Test, a general intelligence test, and a Flight Aptitude
Rating which is a composite of Mechanical Comprehension
Test, Spatial Aperception Test, and a Biographical In
ventory. The aptitude for service ratings, a subjective
evaluation of students by officer instructors, "were found
to add significantly to the validity of the current selec- 4 tion test battery for prospective naval aviators. An
^avid A, Goslin, The Search for Ability: Standardized Testing in Social Perspective (New Yorks Russell Sage Foundation,’ 19^3)," pp. 164-65•
2 David E. Lavin, The Prediction of Academic Per formance (New York: Russell Sage Foundation, 1965)» p» 40.
^Lavin, p. 36.
4 Lieutenant P.M. Curran and Rosalie K. Ambler, "College performance as a predictor of the flight training success of NROTC students," Aerospace Medicine, vol. 14 (Summer, 1954), p. 324. 36
earlier study found that verbal comprehension, perceptual
speed, visualization, judgment, and attention to percep
tual detail were significant in the Aircrew Classification Battery.1 Guilford found that success in Air Force flying
training was related significantly to both pilot aptitude
scores and age. The younger student pilots and those with
the higher aptitude test scores were the most successful.
The results are shown in Figure 1.
FIGURE 1 2 Flying training success by age and aptitude * 2
~-- “T------— Joy Paul Guilford, et. al., "A factor-analytic study of Navy reasoning tests with the Air Force Aircrew Classification Battery," Educational and Psychological Measurement, vol. 14 (Summer, 1954), p. 324.
2 Joy Paul Guilford and J.I. Lacey (eds.), Printed Classification Tests (Washington DC« U.S. Government Printing Office, 1947), P- 391• 37
This finding bears out Linn’s statement that overpredic
tion can occur "simply because the predictors are less
than perfectly reliable. Failure to include a predictor
in the regression equation on which there are pre-existing
group differences can also yield this result." When the
item to be measured is some sort of performance, there is
always a possibility that a written examination used, as
in the case of the Air Force Officer Qualifying Test, will yield spurious results. There is a lack of research in formation on the valid substitution of written for per- formance examinations. A summary of findings of various studies reveals a certain divergence of opinion.
Anastasi found that the correlation of biographical inventory data and success in college, success being de fined as positive, average or negative, was greater than those obtained with the College Entrance Examination Board
Verbal or Mathematics Aptitude Tests.She also found,
^Linn, p. 145.
2 'J.P. Foley, Jr., Performance Testing; Testing for What is Real (Wright-Patterson AFB, Ohio; Air Ma"teriel ResearchTaboratory, 1963), p. 2.
-^Anne Anastasi, Martin J. Meade, and Alexander A. Schneiders, The Validation of a Biographical Inventory as a Predictor of College Success (New York; College Entrance'Examination Board, 1966), p. v. 38
however, that
. . . the student most likely to fall in the Positive and least likely to fall in the Neg ative criterion group received high school awards for scholarship or for both scholarship and extracurricular activities; participated in several high school activities—often of a predominantly intellectual and including espe cially publications and student government; and was an officer in such activities.!
This finding agrees with those found by Stein in a study
of personality measures as predictors. With a reliability
of +.85, Stein found high school achievement to be "the
best single predictor of college success." In another
study of personality measures, an honor point ratio was
computed using items from the Minnesota Multiphasic Per
sonality Inventory and other similar inventories. The
ratio indicated attitudes on various subjects and was
found to be a predictor of academic success with an
average r of .38 from eight different samples. An in
vestigation of noncognitive measures as predictors of
success in naval aviation training yielded some interest
ing results.
1 Anastasi, Meade, and Schneiders, p. 39« o Stem, p. 1.
^Harrison G. Gough, "The Construction of a Personality Scale to Predict Scholastic Achievement," Journal of Ap plied Psychology, vol. 37 (1953), p. 366. 39
A study by Bale and Ambler examined the effective ness of the Navy's Flight Background Questionnaire (FBQ) and their College Background Questionnaire (CBQ) in the
selection process of naval aviators. The CBQ includes
the highest level of education attained, college major, type of school attended, the number of students at the college, the number of colleges attended, the grade point average, the number of times the academic major was changed, the geographical location of the college where one com pleted the major part of the work, and whether or not one has a teaching certificate. The FBQ includes experience as an airline passenger, experience as an aircrew member, previous flight instruction, collegiate aviation activities, and the age at which one first became motivated to enter military aviation.1 The study attempted to increase the predictive ability of the existing tests, the Mechanical
Comprehension Test (MCT) and a Biographical Inventory (BI).
The results showed that the FBQ arid CBQ increased the pre dictive ability of these tests using multiple correlation.
Ronald M’. Bale and Rosalie K. Ambler, "Application of college and flight background questionnaires as sup plementary noncognitive measures for use in the selection of student naval aviators," Aerospace Medicine, vol. 42 (November, 1971), 1178. , 40
A significant correlation between the mechanical compre
hension test and the biographical inventory on one hand
and the Flight Background and College Background Question
naires on the other is apparent in the data contained in
Table 7.
TABLE 7
Correlation between aptitude tests -, (MCT and BI) and the CBQ and FBQ items1
Selected Aptitude Items Cumulative Value and CBQ-FBQ Items Shrunken R of F
MCT .173 19.443 BI .221 12.914 GPA 2.00-2.49 • 239 6.384 Private Pit License .251 4.893 Mot to fly military since age 22* .263 4.969 Attended 3 or more coll* .270 3.264 Coll located in a Pacific Coast State .278 3.905 Comm’l pilot’s license .284 3.235 Flown less than twice commercially .291 3.284 Mbrs of coll flying club* .295 2.674 Motivation to fly mil since age 15-17 .298 2.151 Attended coll w/l6,000 students • 300 2.076 Motivation to fly military before age 12 .301 1.067
1 Bale and Ambler, p. 1180. 41
There have been complaints, from time to time, that cer
tain tests are biased against certain subgroups of the
population. The results of one recent study indicated
that even ". . .if culture-fair tests could be devised,
the usefulness of such measures is open to question. Most
psychometrists would suggest that the intentional masking
of cultural differences would in general make the tests less predictive."1 The following section contains general
information from related literature on achievement and
aptitude tests.
Achievement/Aptitude Tests
"An aptitude is an individual’s potential for
learning a given skill when he is provided with appro-
priate instruction." Conversely, the label "achievement
test" is usually given to tests which measure the results
of learning which has already taken place as the result of 3 some relatively formal and structured educational experience.
1 William A. Melvens and Irvin J. Lehmann, Standardized Tests in Education (New York: Holt, Rinehart and Winston, W, p. 125.
J. Stanley Ahmann, Testing Student Achievements and Aptitudes (Washington, D.C.: Center for Applied Research InEducation, 1962), p. 81. ^Frederick Gromm Brown, Measurement and Evaluation (Itasca, Illinois: Peacock, 1971), p. 95. Aptitude tests measure your potential ability and so pre
dict whether or not you will be able to do college work
successfully. Achievement tests seek to measure what you
have already learned and how well you can apply your know- ledge."1 /Emphasis in original./ Aptitude tests should
have a future reference for prediction. They usually
measure characteristics that will transfer to a variety
of situations, thus stressing learning skills rather than
mastery of specific content. The abilities measured are
less likely to be related to specific educational exper-
iences than those measured by achievement tests. Such
tests are based upon the assumptions that important edu
cational outcomes can be specified, that the tests accurate
ly assess important outcomes, and that meaningful norms
and standards are available to aid in the interpretation 3 of scores.
Frequently, the content of aptitude and achievement
tests is so similar, that it is impossible to differentiate
^Louis K. Wechsler, Martin Blum, and Sidney Friedman, College Entrance Examinations, 2d ed. (New York« Barnes and Noble, 1967), p. 1.
? Brown, pp. 115-16.
-^Brown, p. 96. 43 between items. This is due to the common assumption that the best way to measure one’s aptitude in a given area is to measure his achievement in that or related areas.
Some aptitude tests, therefore, particularly scholastic aptitude, look like achievement tests. Tests which are used to predict can be called aptitude tests while those used for evaluation can be called achievement tests; the 2 same test may be used for both purposes.
In screening applicants for a job or training program, it is usually necessary to try out tests we propose to use in the specific situa tion, as correlations between tests and cri teria may vary widely from place to place. In using aptitude tests to help persons make vo cational decisions, too, it is important to use tests that have been tried out in some practical situation so that their relation ship to real-life criteria is known.3
In a study of naval aptitude screening the authors found that the process of determining recruit suitability in cluded an initial screening evaluation, observation of training performance, personal psychiatric evaluation and evaluation by officers. "Comparable screening as a part
^Ahmann, p. 81.
2Tyler, p. 53•
3Tyler, p. 59. 44
of physical examination is done in the selection of can-
didates for . . . flight training ..." With this back
ground about aptitude testing, the next section will i ! examine the Scholastic Aptitude Test and the American
College Tests.
ACT and SAT
The ACT and SAT are designed to provide reliable in
dications of a student’s ability to do college work. Each
institution using these testing services sets its own
cutoffs, and the tests constitute only a part of the 2 material considered in making admissions decisions.
As far back as 1952, research confirmed previous
studies as well as the relationships to specific tests.-’
A later study found that the multiple correlation co
efficient among all grades, SAT, and the Purdue Placement
^.J. Connery and Richard R. Waite, "Aptitude screen ing of naval recruits," U.S. Naval Institute Proceedings, vol. 91 (February,,1965), p. l44.
p College Entrance Examination Board, The College Board Today, Description of the Organization, Services, and Pro grams of the CEEB (New York* CEEB, 1965), p. l6.
^Joy Paul Guilford, Benjamin Fruchter, and Wayne S. Zimmerman, "Factor Analysis of the Army Air Forces Shep pard Field Battery of Experimental Aptitude Tests," Psychometrika, vol. 17 (March, 1952), p. 61. ^5
Tests ranged between .55 and . 77«1 Boyce and Paxson
found a significant correlation between entrance examina
tions and college success. The ACT Composite, the SAT
Total, and the subscores of both were all found to be
significantly correlated to college success. The results of this survey are presented in Table 8.
TABLE 8
Correlations between predictor and criterion variables^
Predictor Correlation Year
ACT English .64* I960, 61 ACT Math .47* 1962, 63 ACT Social Science .50* 1964, 65 ACT Natural Science .46* ACT Composite .57* SAT Verbal .36* SAT Quantitative .38* SAT Total .46*
*Significant at .01 level.
^Richard Leo Burns, An investigation of the value of the American College Testing Program, the Scholastic Aptitude Test and the Purdue Placement Test as predictors of Academic Success of Purdue University Freshmen. Dissertation Ab stracts , vol. 24 (September, 1964), p. 1477• p Richard W. Boyce and R.C. Paxson, "The Predictive Validity of Eleven Tests at One State College," Educa- tional and Psychological Measurement, vol. 25, no. 4 “ 1145.------i>6
Munday found that the SAT and ACT tests had about the same degree of predictive validity.1 Another study found that
. . . there are (1) moderate correlations ; j among measures of academic potential and per formance, (2) moderate correlations among non-classroom achievements in the same or closely related areas, (3) low to moderate correlations among non-classroom achieve ments in the areas which are not closely related, and (4) low relationships between non-classroom achievements and measures of academic potential and performance.2
In a large-scale investigation of the relationships
between college grades and predictors, several significant
correlations were found. Tests, high school grades, and a
combination thereof, could all predict college grades. The results of Munday’s survey are presented in Table 9- In another survey, the researchers found it impossible to equate
SAT and ACT scores. They discovered, however, a limited relationship between either test and first semester per- 3 formance in college as reflected in first semester GPA.-' 7
7 Leo A. Munday, "Comparative Predictive Validities of the American College Tests and two other scholastic aptitude tests," ACT Research Report No. 6 (Iowa City« ACT, 1965), p. 6. o James M. Richards, Jr., John L. Holland, and Sandra W. Lutz, "The Prediction of Student Accomplishment in Col lege," ACT Research Report No. 13 (Iowa City« ACT, 1966), p. l4. ^L, Joseph Lins, Allan P. Abell, and H. Clifton Hutchins, "Relative usefulness in predicting academic suc cess of the ACT, the SAT, and some other variables,” Journal of Experimental Education, vol. 35 (Winter, 1966), p. 2. 4?
TABLE 9
Median multiple r’s and standard errors of estimate for predicting college grades, using tests (T index), high school grades (H.index), and the combination (TH index)1
Median Multiple R’s for Four Specific College Courses and Overall
Index English Math SS NS Overall «523 T .508 .420 .496 .486 .441 .485 .494 «553 H .508 .627 TH «593 .521 «578 «575
Median Standard Errors of Estimate for Four Specific College Courses and Overall
T «793 1.020 «839 .896 .647 H .786 1.007 .843 .893 .632 TH «729 «952 .782 .832 .582
// Students 176,779 76,039 128,201 112,638 211,324 # Schools 379 249 337 315 398
^Leo A. Munday, "Predicting college grades using ACT data," Educational and Psychological Measurement, vol. 27, (Winter, 1967)« pp« 1143-44. 48
Another survey by Passons found that both parts of the
SAT, all four parts of the ACT, both composites, and high
school recommending grades were correlated as predictors
for first semester GPA and for ten courses. The SAT Ver
bal score was the most productive single predictor followed by the ACT Composite.1 Another survey by Richards, et al,
found that the correlations between college grades and the
ACT social science score was higher for women than for men and higher for sophomores than for freshmen. The
results of the study by Richards, et al, are contained
in Table 10.
TABLE 10
Multiple correlation for college freshmen/ sophomores for criteria of achievement highly comparable to high school achievement scales2
Freshmen Sophomores
Males .29 .44 Females .44 .50
William R. Passons, "Predictive validities of the ACT, SAT and high school grades for first semester GPA and fresh man courses," Educational and Psychological Measurement, pp. 1143-44.
2James M. Richards, Jr., John L. Holland, and Sandra W Luntz, "Prediction of student accomplishment in college," Journal of Educational Psychology (1967), pp. 348-349.
i U-9
All of this earlier work shows a relationship between ap
titude measures and the criterion. This finding has been substantiated in later studies.
The ACT and the SAT scores have been consistently found to be related to success in college. These findings show that both exams can be used to make valid, reliable predictions of college success. "ACT test scores were found to be moderately to highly related to measures of intelligence, scholastic aptitude, and English and reading achievement."1 SAT reliability has increased as time has progressed. In 1965 the reliability of the verbal portion was .89 and the math part was .85. The correlation be tween verbal and mathematical part increased from .54 in 1953» to .56 in 1956, .62 in 1959» and .64 in 1962.2
Results of another study by Richards and Lutz is summarized in Table 11. Correlations were found between ACT scores and grade point average. The same study found that the correlations were higher for women than for men. Data
. T“ " Leo A. Munday, "Correlations between ACT and other predictors of academic success in college," College and University, vol. 44 (Fall, 1968), p. 75«
2John E. Bowers, "Scholastic Aptitude Test" in The Sixth Mental Measurements Yearbook, Oscar Krisen Buros, edTTHxghland Park, New Jersey» Gryphon, 1965). p. 705. 50
TABLE 11
Beta weights and multiple correlations for predicting academic accomplishments1
Male
Criterion Predictors Beta R
College GPA (All Rep) ACT-SS • 1790 • 55 ACT-MA .1536 • 57 College GPA (Stu Rep) ACT-SS .1966 .50
Female
College GPA (All Rep) ACT-EN .2861 .60
College GPA (Stu Rep) ACT-EN .2366 • 56
in Table 12 gives the results of a survey of intercorreia
tions among high school rank, ACT scores, and GPA. Signi
ficant results were found in several categories. High
school rank was correlated most highly with freshman grades
and least with senior grades. Each subscore and the com
posite score of the ACT exhibited the same tendency.
^James M. Richards, Jr. and Sandra VJ. Lutz, "Pre dicting student accomplishment in college from the ACT assessment," Journal of Educational Measurement, vol. 5 (Spring, 1968), p. 23.
! 4 TABLE 12
Intercorrelations of high school rank in class, ACT program variables, and GPA in eight semesters1
ITEM 3 5 6 7 8 9 10 11 12 13 14
HS Rank 1 .356 .368 .244 .291 • 393 • 387 .341 .278 .270 .240 .256 .244 .222 Engl 2 .485 • 545 .542 • 773 .345 .278 .226 .236 .236 .222 .216 .160 Math 3 • 395 .471 .764 .279 .189 .171 .171 .145 .162 .156 .121 SS 4 .637 .802 .279 . 244 .188 .198 .210 .225 .174 .149 NS 5 .829 .306 .255 .177 .200 .184 .202 .159 .126 Comp 6 .375 .298 .237 .255 .238 .252 .219 .173 1 7 .556 .456 .439 • 399 .415 • 387 .342 2 8 .490 .445 .478 .'383 .364 • 339 3 0 .562 .496 .456 .445 • 354 4 10 • 512 .469 .442 .416 5 11 • 551 .500 .453 6 12 .544 .482 7 13 • 54l 8 14
Lloyd G. Humphreys, "Fleeting nature of the prediction of college academic success," Journal of Educational Psychology, vol. 59 (October, 1968), p. 377. 52
Another survey found "little evidence that the Scholastic Aptitude Test is biased as a predictor of college grades."1
A later study by Davis and Temp found that the vali dity of the SAT for predicting grades of black students varies from college to college. In some institutions validities are the same for blacks as for whites; in others they are different. Where validities differ, they tend to be higher for whites than for blacks. If prediction from SAT scores is based upon a model for whites, then blacks will be predicted to do better than they actually do.
Another study found exceptionally low correlations of the
SAT and college grades for males in contrast to females.
The strength of relationships for male SAT findings, but not for female results, is also generally lower than that for the typical validity coefficients for male or female college freshman GPA, which usually range from .16 to • 6l„3
Anne Cleary, "Test biass prediction of grades of Negro and white students in integrated colleges," Journal of Educational Measurement, vol. 5 (Summer, 1968), p. 123.
2Junius A. Davis and George Temp, "Is the SAT biased against black students?" College Board Review, vol. 81 (Pall, 1971), p. 5-
-^Marvin Siegelman, "SAT and high school average pre dictions of four year college achievement," Educational and Psychological Measurement, vol. 31 (Winter, 1971), p- 9W. 53
In a study of the Army ROTC Qualifying Examination intercorrelations were computed with the SAT. As shown in Table 13 the verbal, quantitative, and total scores of the Army test were most highly correlated with the verbal, quantitative, and total scores of the SAT, respectively.
TABLE 13
Intercorrelations among subtests scores and total tests scores for RQ and SATl
RQ-Q SAT-V SAT-Q SAT-T
RQ-V .15 .69 ■ 38 .61 RQ-Q •15 .65 .49 RQ-T ■ 58 .64 .71 SAT-V .79
The same study also found intercorrelations with grade point averages as presented in Table 14. The RQ-V was significantly correlated with English and social science
GPAs. The RQ-Q was significantly correlated to the
J'T.M. Goolsby, Jr. and D.A. Williamson, "Use of the ROTC Qualifying Examination for Selection of Students to Enroll in Advanced Courses in ROTC as Juniors," Edu cational and Psychological Measurement, vol. 31 (Summer, 1971). p. 514. 54
TABLE 14
Correlations of subtests scores and total tests scores for RQ and SAT with certain subject area GPAs (N=77)1
Engl-GPA Math-GPA Science-GPA Soc St-GPA 0 0 0 RQ-V .24* • .05 .23* RQ-Q .19 .28* .26* .16 RQ-T .23* .08 .17 .20 SAT-V .20 .02 .07 .18 SAT-Q .23* .23* .15 .20 SAT-T .24* .12 .11 .22*
(♦Significant at the .05 level.)
mathematics and science GPAs. Where significant correla tion existed for the RQ subscore, it was more highly cor related with grades than v/as its SAT counterpart.
Using a random sample of 500 college freshmen, the quartile of high school achievement and ACT scores were compared with first semester college GPA using two-way analysis of variance. The study revealed that quartile rankings were a better predictor than the ACT and that p the ACT has a built-in sex and ethnic bias.' Merritt
1Ibid., p. 515.
p Jerry H. Borup, "Validity of American college test for discerning potential academic achievement levels« ethnic and sex groups," Journal of Educational Research, vol. 65 (September, 1971)» P« 3 • 55
found a .71 correlation coefficient between GPA predicted
by the ACT and that actually earned by 34? students from low socioeconomic backgrounds.1 The results of a later
survey were that for most colleges studied the ACT tests
were at least as efficient predictors of college overall
GPA as were the SAT tests. At only one college was the
SAT correlation decidedly higher (and the sample size for
this college was only 95), whereas the ACT correlation was
noticeably higher for over half of the colleges in the
sample. An ACT study found correlations between high
school grades and ACT scores and specific college grades.
High school grades were found to best predict biology and chemistry grades. ACT scores best predicted sociology grades. Both were poorest at predicting business grades.
In every case except political science, a combination of the two was a better predictor than was either one alone.
The results of this survey are shown in Table 15.
^oy Merritt, "Predictive validity of the American college test for students from low socioeconomic levels," Educational and Psychological Measurement, vol. 32 (Summer, 1972)7 P♦ 444. o Oscar T. Lenning and E. James Maxey, "ACT versus SAT prediction for present-day colleges and students," Educational and Psychological Measurement, vol. 33 TSummer, 1973)» p. 403« 56
TABLE 15
Median multiple correlations of predictors and criterial »
HS Grades & College Course -J Colleges HS Grades ACT Scores ACT Scores
Biology 21 •59 .48 .60 Business 6 .21 • 25 •39 Chemistry 20 • 53 .49 .60 College Algebra 9 • 47 .44 • 55 Engl Comp 9 .44 .41 • 51 Fgn Lang 6 •^5 .37 • 50 History 30 .46 • 37 • 55 Poli Sci 6 .44 . 46 .%6 Psych 1? .46 .46 • 55 Sociology 6 .45 • 58 .62
Summary
This chapter has examined much of the literature re
lated to the Air Force Officer Qualifying Test, the Scholastic Aptitude Tests, and the American College Test ing Program. In various studies over the past three
decades or so the AFOQT has been found to be significantly
related as a predictor to the criteria of success in dif ferent officer training courses. Likewise both the SAT
*1 American College Testing Program, Highlights, p. 20. 57 and the ACT have been found in numerous studies to be significantly related as a predictor to the criteria of success in college. No study to date has attempted to correlate the AFOQT with either the ACT or the SAT. This study has correlated both ACT and SAT with the AFOQT. The next chapter is a presentation and analysis of the data. CHAPTER 3
PRESENTATION AND ANALYSIS OF THE DATA
General
This chapter presents data collected in the study
and statistical tests of the various subhypotheses
presented in the first chapter. Several parameters of
measurement for the data collected are described in
this section. Variable by variable analyses of the cor
relations between an individual’s score on the Air Force
Officers Qualifying Test and that person's score on either
the American College Test or the Scholastic Aptitude Test
are offered in the following sections.
The minimum, maximum, mean, and standard deviation
for each of the eight variables recorded for the sample
which took both the AFOQT and the SAT are summarized in
Table 16. For each composite of the AFOQT the range was
from the lowest possible to the highest possible score.
The mean for each composite score was lower than the
theoretical mean of these scores. The ranges for the
three SAT scores almost reached the maximum possible
200-800. One of the means was below and two were above the theoretical mean. Similar data are presented in
Table 17 for those who took the AFOQT and the ACT.
58 59
TABLE 16
Means and standard deviations for the variables AFOQT and SAT
Composite Mean SD Min Max
Pilot 45.2059 30.6549 1 95 Nav-Tech 38.8412 29.6016 1 95 OQC 40.3112 30.0885 1 95 Verbal 38.3529 25.3313 1 95 Quant 34.4206 28.1179 1 95 SAT-V 493.9118 95-5823 230 780 SAT-M 572.5882 105.1354 310 800 SAT-T 1065.9412 179.8082 540 1470 (N = 340)
TABLE 17
Means and standard deviations for the variables AFOQT and SAT
Composite Mean SD Min Max
Pilot 45.7000 29-5643 1 95 Nav-Tech 36.9250 30.0613 1 95 OQC 39.3000 30.0021 1 95 Verbal 33.7250 24.4031 1 90 Quant 32.7562 28.2114 1 95 ACT-EN 20.5500 4.2954 7 31 ACT-MA 25.3937 5.4428 3 36 ACT-SS 24.0250 5.2293 8 33 ACT-NS 26.7875 4.9556 8 34 ACT-CO 24.3125 4.0874 12 32
(N = 160) 60
The ranges were wide for both tests. The AFOQT means
were lower than the theoretical means. The ACT means
were higher than the theoretical means.
In instances where one is able not only to rank ob
jects with respect to the degree to which they possess a
certain characteristic but also to indicate the exact dis tances between them, an internal scale is said to exist.1
This is the case with AFOQT, SAT, and ACT scores. When
interested in significance tests and measures of associa
tion between internal scales, the appropriate statistic
is the correlation coefficient, r. Once one has found the
significant variables, regression analysis is used to at
tempt to predict the exact value of a variable from the
other. The appropriate test of significance for the cor- 3 relation coefficient is the analysis of variance test, F.
The square of the correlation coefficient is interpreted as the proportion of the total variation of one variable 4 explained by the other. * 2
^Hubert M. Blalock, Jr., Social Statistics, (New York» McGraw-Hill, i960), p. 14.
2Blalock, p. 273.
-^Blalock, p. 302.
^Blalock, p. 298 61
The simple correlation coefficients between the var ious composites of the AFOQT and the subscores of the SAT are summarized in Table 18. A similar summary for the
AFOQT and the ACT is shown in Table 19.
TABLE 18
Simple correlation coefficients among composites of the AFOQT and subtests of the SAT
Composite Pit N-T OQC Ver Quant SAT-V SAT-M SAT-T
Pilot 1.000 Nav-Tech 0.644 1.000 OQC 0.411 0.698 1.000 Verbal 0.382 0.535 0.765 1.000 Quant 0.393 0.806 0.800 0.501 1.000 SAT-V 0.373 0.493 0.660 0.795 0.484 1.000 SAT-M 0.369 0.702 0.689 0.571 0.758 0.602 1.000 SAT-T 0.417 0.673 0.75^ 0.757 0.702 0.884 0.904 1.000
(N = 340)
Correlations by AFOQT Composite
Pilot Composite
When tested against the verbal score of the SAT, the pilot composite of the AFOQT had a computed r2 =0.1394, a = -13.943, b = 0.1198, and F =54.76. The value of r2 can be interpreted as the proportion of variation in the pilot composite which can be explained by the verbal score 62
TABLE 19
Simple correlation coefficients among the composites of the AFOQT and subtests of the ACT
Composite Pit N-T OQC Ver Qnt EN MA SS NS CO Pilot Nav-Tech 657 OQC 385 694 Verbal 328 574 693 Quant 356 816 791 566 ACT-EN 131 296 481 553 416 ACT-MA 223 526 516 419 622 494 ACT-SS 209 451 543 571 487 594 526 ACT-NS 409 539 522 527 475 515 601 650 ACT-CO 292 556 620 613 612 769 811 847 843 (Decimals Omitted) (N = l6o)
of the SAT. The standard form of a regression equation is Y = a+bX where Y is the dependent variable, X is the inde
pendent variable, a is the value of Y when X equals zero,
and b is the slope of the regression line. The regression equation can be written, Pit = (.1198)(SAT-V) - 13.943« The standard error was 0.0162. As mentioned above, an ana
lysis of variance test is performed to determine the signi ficance of the correlation coefficient. The critical region of any statistic are those possible outcomes which will
cause the researcher to reject the hypothesis. The sum of 63
the probabilities of each outcome in the critical region
is the probability of making a type I error, which is referred to as the significance level of the test.1 The
other primary factor that determines the critical value of
F is the number of degrees of freedom, v/hich is "equal to the number of quantities which are unknown minus the 2 number of independent equations linking these unknowns.""
Because the computed value of F exceeds the critical value
of F for 1 and N-2 (338) degrees of freedom, the null hy pothesis that there is no significant correlation between
the SAT-V score and the pilot composite may be rejected at p = .001. The distribution of pilot composite scores and SAT-V subscores is shown in Table 20. The positive axis of this matrix corresponds to the correlation found between the two variables. TABLE 20 Distribution of pilot composite and SAT-V scores
SAT-V______Pilot 230-450 460-550 560-780
01-20 55 40 14 75-50 21 31 19 55-95 34 71 55
1Blalock, p. 122-23. ^Blalock, p. 156. 6k
The computed values of the correlation between the
mathematics test of the SAT and the pilot composite were r2 = .1358, a = -16.325, b = .1075, and F = 53.13» The
regression equation can be written Pit = (.1075)(SAT-M)
- 16.325. The standard error was 0.0147« Because the
computed value of F exceeds the critical value of F for
one and 338 degrees of freedom, the null hypothesis that
there is no significant correlation between the SAT-M score
and the pilot composite may be rejected at p = .001. The
distribution of pilot composite and SAT-M scores is shown
in Table 21.
TABLE 21
Distribution of pilot composite and SAT-M scores
SAT-M Pilot 31P-Z±20 530-620 630-800
01-20 48 41 20 25-50 19 32 20 55-95 31 56 76
For the SAT total score and the pilot composite, the computed values were r2 = .1741, a = -30.630, b = .0711, and F = 71.27• The regression equation can be written,
Pit = (.0711)(SAT-T) - 30.63. The standard error was 0.0084.
Because the computed value of F exceeds the critical value 65 for one and 338 degrees of freedom, the null hypothesis that there is no significant correlation between the SAT-T score and the pilot composite may be rejected at p = .001.
The distribution of pilot scores and SAT total scores is shown in Table 22. TABLE 22 * Distribution of pilot composite and SAT-T scores
SAT-T Pilot 540-970 980-1170 1180-1470
01-20 49 50 10 25-50 20 34 17 55-95 20 75 65
In addition to the simple correlations for the two subscores and the total SAT, a multiple correlation was computed for the amount of variation in the pilot com posite explained by each of the subscores of the SAT. Partial correlation refers to correlation between any two variables when the effects of the other variables have been controlled. Multiple correlation is used to indicate how much of the total variation in the dependent variable can be explained by all of the independent variables acting together.1 Acting together, the two subscores of the SAT
1Blalock, p. 326. 66 explain a greater variation in the pilot composite than either subscore alone.
TABLE 23
Multiple and partial correlations between the pilot composite of the AFOQT and the verbal and the mathematics subscores of the SAT
Subscore DOF Partials Beta Wt Std-Err F Ratio Coefficient
SAT-V 338 .1394 .2376 0.0199 14.64 .0762 SAT-M 337 .1718 .2254 0.0181 13.18 .0657
The constant vzas -30*074.
Because the F ratios are significant at p = .001, the null hypotheses that there is no significant correlation between the pilot composite and either the SAT-V or the SAT-M subscores may be rejected. Furthermore, by using both subscores the predictability is improved over using either subscore alone. When comparing the various composites of the AFOQT with the subtests of the ACT, the computer was programmed to print out the various computations only for the ACT subscore which vzas the best single predictor for each composite. Because predictive ability was desired in this survey, those subscores vzhich are relatively less signi ficant are not pertinent. Only the best predictor, the 6?
most significant ACT subscore, and combination of subscores
are reported for each AFOQT composite. When tested against the composite score of the ACT, the computed values for p the pilot composite was r' = .0854, a = -5.682, b = 2.113,
and F = 14.75« The regression equation can be written, Pit =■ (2.113)(ACT-CO) - 5.682. The standard error was
O.55O3. Because the computed value of F was significant at p = .001, the null hypothesis that there is no signi
ficant correlation between the ACT-CO score and the pilot composite may be rejected.
The distributions of scores for the pilot composite
and the ACT English, mathematical, social science, natural
science, and composite scores are shown in Tables 24 through 28, respectively.
TABLE 24 Distribution of pilot composite and ACT-EN scores
ACT-EN Pilot 20-22
01-20 22 16 13 25-50 5 9 14 55-95 27 29 25 68
TABLE 25
Distribution of pilot composite and ACT-MA scores
ACT-MA Pilot 3^ 24-27 28-36
01-20 24 16 11 25-50 8 8 12 55-95 20 28 33
TABLE 26
Distribution of pilot composite and ACT-SS scores
. ACT-SS Pilot 8-23 24-26 2Z--.33 01-20 23 26 8 25-50 10 7 11 55-95 21 30 30 69
TABLE 27
Distribution of pilot composite and ACT-NS scores
ACT-NS Pilot £^24 30-3^
01-20 25 17 9 25-50 6 13 9 55-95 14 30 37
TABLE 28
Distribution of pilot composite and ACT-CO scores
ACT-CO Pilot 12-22 23-26 27-32
01-20 22 21 8 25-50 7 9 12 55-95 19 29 33 70
The computed values for the ACT-NS score and the
pilot composite were r = .1673, a = -19.67, b = 2.440,
and F = 31.75. The regression equation can be written,
Pit = (2.440)(ACT-NS) - 19.67. The standard error was
0.4331« Because the computed value of F exceeds the cri
tical value, the null hypothesis that there is no signi
ficant correlation between the pilot composite of the AFOQT
and the ACT-NS score may be rejected at p = .001. The
significant multiple correlations for the ACT subscores
and the pilot composite are given in Table 29« These
TABLE 29
Multiple and partial correlations between the pilot composite of the AFOQT and the English and natural science subscores of the ACT
Subscore DOF Partials Beta Wt Std-Err F Ratio Coefficient
ACT-EN 158 .1760 .1087 «5819 1.65 - «7484 ACT-NS 157 .1673 .4651 .5044 30.26 2.7747
The constant was -13.247.
correlations confirm the rejection of the null hypothesis that there is no significant relationship between the pilot composite and the ACT-NS score. They also indicate that the other subscores add no significant explanation of the vari ation in the pilot composite. 71
Navigator-Technical Composite
The navigator-technical composite of the AFOQT, when
tested against the verbal score of the SAT, had computed values of r2 = .2428, a = -36.5368, b = .1526, and
F = 108.40. The regression equation can be written,
N-T = (.1526)(SAT-V) - 36.5368. The standard error was
0.014?. The computed value of F exceeds the critical
value. Hence, the null hypothesis that there is no signi
ficant correlation between the navigator-technical com
posite and the SAT-V score may be rejected at p - .001.
The distribution of navigator-technical and SAT-verbal
scores appears in Table 30»
TABLE 30
Distribution of navigator-technical composite and SAT-V scores
SAT-V N-T 23O.-45O 460-550 560-780
01-20 68 53 10 25-50 23 44 25 55-95 19 45 53
For the mathematics score of the SAT, when tested against the navigator-technical composite of the AFOQT, the computed values were r = .4926, a = -74.308, b = .1976, 72
and F = 328.13. The regression equation can be written,
N-T = (.1976)(SAT-M) - 74.300. The standard error was
0.0109. The computed value of F far exceeded the critical
value of p = .001, and, therefore, the null hypothesis
that there is no significant correlation between the nav-
tech composite and the mathematics score may be rejected.
The distribution of navigator-technical and SAT-M scores
is reported in Table 31.
TABLE 31
Distribution of navigator-technical composite and SAT-M scores
SAT-M N-T 230-450 460-550 560-570
01-20 80 42 9 25-50 15 51 26 55-95 3 36 78
The computed values for a nav-tech SAT-T comparison were r2 = .4534, a = -79.321, b = .1109, and F = 280.36.
The regression equation may be written, N-T = (.1109)(SAT-T)
- 79.321. The standard error was 0.0066. Since the com puted value of F exceeds the critical value at p = .001,
the null hypothesis that there is no significant corre lation between the navigator-technical composite of the AFOQT 73 and the total score of the SAT may be rejected. The dis-
stribution of navigator-technical and SAT scores is pre
sented in Table 32.
TABLE 32
Distribution of navigator-technical composite and SAT-T scores
______SAT-T______N-T 540-970 980-1170 ll80-l470
01-20 72 52 7 25-50 11 63 18 55-95 6 44 67
The multiple correlations between the navigator-tech nical composite and the SAT-V and SAT-M scores are shown in Table 33» TABLE 33
Multiple and partial correlations between the navigator-technical composite of the AFOQT and the verbal and mathematics subscores of the SAT
Subscore DOF Partials Beta Wt Std-Err F Ratio Coefficient
SAT-M 338 .4926 .6356 .0136 173.64 .1790 SAT-V 337 .5003 .1100 .0149 5.20 .0341
The constant was -80.453« 7^
These statistics indicate that at p = .001, the null hypothesis that there is no significant correlation be
tween the navigator-technical composite and the SAT-M score may be rejected. The data also reveal that the verbal score does not add significantly to the explana tion of the variation in the nav-tech composite. For a comparison between the navigator-technical composite of the AFOQT and the ACT composite score, the computed values were r2 = .3092, a = -62.506, b = 4.087, and F = 70.72. The regression equation can be written,
N-T = (4.0897)(ACT-CO) - 62.506. The standard error was
0.4863. Because the computed value of F exceeds the cri tical value at p = .001, the null hypothesis that there is no significant correlation between the navigator-technical composite and the ACT-CO score may be rejected. The dis tributions of navigator-technical composite and ACT English, mathematics, social science, natural science, and composite scores are shown, respectively, in Tables 34 through 38. When comparing the navigator-technical composite with 2 the ACT-NS score, the computed values were r = .2903, a = -50.621, b = 3.268, and F = 64.62. The regression equation can be written, N-T = (3.268)(ACT-NS) - 50.621. The standard error v/as 0.4066. Because the computed value of F exceeds 75
TABLE 34
Distribution of navigator-technical composite and ACT-EN scores
ACT-EN II-.T 7-19 20-22 2±J1 01-20 32 18 16 25-50 15 18 13 55-95 7 18 23
TABLE 35 Distribution of navigator-technical composite and ACT-MA scores
ACT-MA N-T 24-27 28-36
01 20 36 20 10 25-50 li 20 15 55-95 5 12 31 76
TABLE 36
Distribution of navigator-technical composite and ACT-SS scores
ACT-SS N-T 8-23 24-26
01-20 31 20 15 25-50 20 20 6 55-95 3 17 28
TABLE 37
Distribution of navigator-technical composite and ACT-NS scores
ACT-NS N-T 8-24 30-34
01-20 31 27 8 25-50 11 20 15 55-95 3 13 32 77
TABLE 38
Distribution of navigator-technical composite and ACT-CO scores
ACT-NS N-T 12-22 27-32
01-20 30 29 7 25-50 12 24 10 55-95 6 6 36
the critical value at p = .001, the null hypothesis that
there is no significant correlation between the navigator-
technical composite and the ACT-NS score may be rejected.
The significant multiple correlations of the ACT sub
scores with the navigator-technical composite are shown in
Table 39« These data indicate that while there is a significant correlation between the navigator-technical composite and the natural science subscore, the use of the mathematics and social science subscores add signi ficantly to the explanation of the variation in the navigator-technical composite, but that the use of the
English subscore actually reduces the ability to explain the variation.
Officer Quality Composite
A correlation between the officer quality composite
(OQC) of the AFOQT and the verbal score of the SAT produced 78
TABLE 39 multiple and partial correlations between the navigator-technical composite of the AFOQT and the subscores of the ACT
Subscore DOF Partials Beta Wt Std-Error F Ratio Coefficient
ACT-NS 158 .2903 .3487 0.4430 18.90 2.1151 ACT-MA 157 .3543 • 3165 0.4865 15-58 1.7842 ACT-NS 158 .2903 .2941 0.5579 10.23 1.7841 ACT-MA 157 .3543 .2943 0.4538 12.83 1.6257 ACT-SS 156 .3603 .1044 0.4972 1.46 0.6001
ACT-NS 158 .2903 .3082 0.5608 11.11 1.8695 ACT-MA 157 .3543 • 3159 0.4624 14.24 1.7449 ACT-SS 156 .3603 .1468 0.5317 2.52 0.8437 ACT-EN 155 .3669 -.1059 0.5807 1.63 -0.7412
The constants for each of the three sets of data are, from top to bottom, - 64.128, --66.566, and -62.501 • 79 values of r2 = .4350, a = -62.237, b = .2076, and F =
260.26. The regression equation can be written, OQC = (.2076)(SAT-V) - 62.237» The standard error was 0.0129»
Because the computed value of F exceeded the critical value at p = .001, the null hypothesis that there is no significant correlation between th6 SAT-V and the OQC may be rejected. The distribution of the OQC and the
SAT-V scores is shown in Table 40. TABLE 40
Distribution of officer quality composite and SAT-V scores
SAT-V OQC 230-450 460-550 560-780
01-20 75 48 3 25-50 22 41 18 55-95 13 53 67
For the correlation between the OQC and the SAT-M p the computed values were r = .4741, a = -72.520, b = .1971, and F = 304.70» The regression equation was OQC = (.1971)(SAT-M) - 72.520. The standard error was 0.0113. The computed value of F exceeded the critical value at p = .001, and the null hypothesis that there is no signi ficant correlation between the OQC composite and the SAT-M 80 score may be rejected. Distribution of the OQC and the
SAT-M scores is shown in Table 4l.
TABLE 4l
Distribution of officer quality composite and SAT-M scores
SAT-M OQC 310-520 530-620 ' 630-800
01-20 75 39 12 25-50 13 47 21 55-95 10 43 80
The correlation between the OQC composite and the SAT total score produced values of r = .5679» a = -94.1033, b - .1261, and F = 444.17. The regression equation was
OQC - (.5679)(SAT-T) - 94.1033« The standard error was
0.0060. Because the computed value of F exceeded the cri tical value at p = .001, one may reject the null hypothe sis that there is no significant correlation between the
OQC composite and the SAT total score. Distribution of
OQC and SAT-T scores is found in Table 42.
The results of a multiple correlation between the
OQC composite and the two SAT subtests is shown in Table
43. 81
TABLE 42
Distribution of officer quality composite and SAT-T scores
I . ______SAT-T______OQC 540-970 980-1170 ll8O-l47O
01-20 78 45 3 25-50 8 60 13 55-95 3 54 76
TABLE 43
Multiple and partial correlations between the officer quality composite of the AFOQT and the verbal and mathematics subscores of the SAT
Subscore DOF Partials Beta Wt F Ratio Coefficient Constant
SAT-M 338 .4741 .4571 103-93 .1308 SAT-V 337 .5682 .3843 73.44 .1210 -94.3409
These data support the previous decision to reject the null
hypotheses that there were no significant correlations be
tween the OQC composite and either subscore of the SAT.
It further indicates that using both the verbal and math ematical subscores together adds significantly to the ex planation of the variation in OQC scores over the use of either subscore alone. 82
For a correlation between the OQC composite of the
AFOQT and the ACT composite score the computed values were r2 = .3841, a = -71.299, b = 4.5491, and F = 98.53«
The regression equation was OQC = (4.5491)(ACT-CO)
- 71«299« The standard error was 0.4583. Because the computed value of F exceeds the critical value at p =
.001, one may reject the null hypothesis that there is no significant correlation between the OQC composite and the
ACT composite score. Distributions of officer quality composite scores and ACT English, mathematics, social science, natural science, and composite scores are shown, respectively, in Tables 44 through 48. Multiple corre lations between the OQC and the ACT subscores are presented in Table 49.
TABLE 44
Distribution of officer quality composite and ACT-EN scores
ACT-EN OQC 20-22
01-20 38 19 12 25-50 10 12 9 55-95 6 23 31 83
TABLE 45 Distribution of officer quality composite and ACT-MA scores
ACT-MA OQC lz23 24-27 28-36
01-20 37 24 8 25-50 8 11 12 55-95 7 17 36
TABLE 46
Distribution of officer quality composite and ACT-SS scores
______ACT-SS______OQC 8^23 24-26 27-33 01-20 40 21 8 25-50 7 18 6 55-95 7 18 35
STATE UNIVERSITY LIBRARY bowling GREEN 84
TABLE 4?
Distribution of officer quality composite and ACT-NS scores
ACT-NS OQC 8-24 25-29 30-34
01-20 31 30 8 25-50 7 10 14 55-95 7 20 33
TABLE 48
Distribution of officer quality composite and ACT-CO scores
£ ACT-CO OQC 12-22 2J-26 2,7-32
01-20 » 37 29 3 25-50 5 16 10 55-95 6 14 40 85
© TABLE 49
Multiple and partial correlations between the officer quality composite of the AFOQT and the subscores of the ACT
Subscore DOF Partials Beta Wt Std-Err F Ratio Coefficient
ACT-SS 158 • 2950 .5431 0.3832 66.11 3.1162
ACT-SS 158 .2950 .3758 0.4281 25.36 2.1559 ACT-MA 157 .3681 .3179 0.4113 18.15 1.7526
ACT-SS 158 .2950 .2988 0.4749 13.03 1.7142 ACT-MA 157 .3681 .2760 0.4223 12.98 1.5215 ACT-EN 156 .3848 .1668 0.5655 4.24 1.1650
ACT-SS 158 .2950 .233^ 0.5180 6.68 1.3392 ACT-MA 157 .3681 .2238 0.4505 7.50 1.2334 ACT-EN 156 .3848 .1498 0.5658 3.42 1.0466 ACT-NS 155 .3968 .1583 0.5^63 3.08 0.9583
The four constants are -35*566, -56.999» -64.411, and -71.373, respectively.
These data indicate that one can add significantly to the explanation of the variation in the OQC composite by using all of the ACT subscores rather than any lesser combination of subscores or any one alone.
Verbal Composite -
The computed values for the correlation between the ver bal composite of the AFOQT and the verbal score on the SAT 86
were r2 = .6325, a = -65.753, b = .2108, and F = 581.84.
The regression equation was Ver = (.2108)(SAT-V) - 65.753»
The standard error was 0.0087» The computed value of F
exceeded the critical value at p = .001, and, therefore,
the null hypothesis that there is no significant corre
lation between the verbal composite and the SAT-V
score may be rejected. The distribution of verbal com
posite and SAT scores appears in Table 50.
TABLE 50
Distribution of verbal composite and SAT-V scores
SAT-V Ver 230-450 460-550 560-780
01-20 79 30 1 25-50 28 71 15 55-95 3 41 72
When correlating the verbal composite and the SAT-M score, the computed values were r2 = .3256, a = -40.366, b = .1375. and F = 163.17» The regression equation was
Ver = (.1375)(SAT-M) - 40.366. The standard error was
0.0108. Because the computed value of F exceeded the critical value at p = .001, one may reject the null hypo thesis that there is no significant correlation between 8?
the verbal composite and the SAT mathematics score. The
distribution of verbal composite and SAT mathematics
score is shown in Table 51.
TABLE 51
Distribution of verbal composite and SAT-M scores
SAT-V Ver 310-520 570-670 630-800
01-20 62 37 11 25-50 22 52 40 55-95 14 40 62
The computed values for the correlation between the 2 verbal composite and the SAT total score were r = .5725, a = -75.274, b = .1066, and F = 452.71. The regression equation was Ver = (.1066)(SAT-T) - 75.274. The standard error was 0.0050. Inasmuch as the computed value of F exceeded the critical value at p = .001, the null hypo thesis that there is no significant correlation between the verbal composite and the SAT-T score may be rejected.
The distribution of verbal composite and SAT total scores is reflected in Table 52.
A multiple correlation between the verbal composite and the two SAT subtests was also computed. The results are shown in Table 53. 88
TABLE 52
Distribution of verbal composite and SAT-T scores
SAT-T Ver 540-970 980-1170 1180-1470
01-20 72 36 2 25-50 13 77 24 55-95 4 46 66
TABLE 53
Multiple and partial correlations between the verbal composite of the AFOQT and the verbal and mathematics subscores of the SAT
Subscore DOF Partíais Beta Wt Std-Err F Ratio Coefficient
SAT-V 338 .6325 .7088 0.0141 304.52 .1878 SAT-M 337 .6457 .1437 0.0128 12.53 .0346
The constant was -74.252.
These data supported the previously made decisions to reject the null hypotheses that there were no significant correla tions between the verbal composite and the two subscores of the SAT. It also indicated that, relatively speaking, little additional explanation of the variation of the verbal composite is gained by using both the SAT-V and 89
SAT-M instead of only the verbal score.
For the correlation between the verbal composite of the AFOQT and the ACT composite score the computed values were r2 = .3756, a = -55-235, b = 3-6590, and F = 95-05-
The regression equation was Ver = (3.6590)(ACT-CO) - 55-235»
The standard error was 0.3753- One may reject the null hy pothesis that there is no significant correlation between the verbal composite and the ACT composite score, as the computed value of F exceeded the critical value at p = .001.
The distributions of verbal composite scores and ACT English, mathematics, social science, natural science, and composite scores are shown in Tables 54 through 58, respectively.
TABLE 54
Distribution of verbal composite and ACT-EN scores
ACT-EN Ver 2=12 20-22 23-31
01-20 38 21 5 25-50 12 21 21 55-95 4 12 26 90
.TABLE. 55 Distribution of verbal composite. and ACT-MA scores
ACT-MA Ver 3^33 24-27 01-20 .32 21 11 25-50 17 17 20 55-95 3 14 25
TABLE 56
Distribution of verbal composite and ACT-SS scores
..ACT-SS Ver . 8-2J : 24-26 32^33 01-20 38 22 4 25-50 ■10 . 28 16 55-95 6 7 29 91
TABLE 57
Distribution of verbal composite and ACT-NS scores
ACT-NS Ver BE24-— 25^22 32^34
01-20 32 25 7 25-50 9 26 19 55-95 4 9, 29
TABLE 58
Distribution of verbal composite and ACT-CO scores
ACT-CO Ver 12-22 2Zr32
01-20 37 23 4 2 5 50 7 30 17 55-95 4 6 32 92 The significant multiple correlations between the verbal
composite and the ACT subscores are shown in Table 59*
TABLE 59 Multiple and partial correlations between the verbal composite of the AFOQT and the subscores of the ACT
Subscore DOF Partials Beta Wt F Ratio Coefficient Std-Err
ACT-SS 158 .3265 • 5714 76.58 2.6664 0.3047
ACT-SS 158 • 3265 .3754 23.75 1.7519 0.3595 ACT-EN 157 • 3970 .3300 18.35 1.8747 0.4376 ACT-SS 158 .3265 . 2668 9.30 1.2452 0.4082 ACT-EN 157 .3970 .2892 13.91 1.6431 0.4405 ACT-NS 156 .4199 .2042 6.18 1.0054 0.4045 The three constants are, respectively, -30.334, -46.891 and -56.890.
These data indicate that a significant explanation of the variation of the verbal composite can be made by using the
ACT social science, English, and natural science scores acting together.
Quantitative Composite The computed values for correlating the AFOQT quanti- 2 tative composite with the verbal score of the SAT were r .2342, a = -35.897, b = .1424, and F = 103-38. The re gression equation was Quant = (.1424)(SAT-V) - 35.897« 93
The standard error was 0.0140. Because the computed value
of F exceeded the critical value at p = .001, one may re
ject the null hypothesis that there is no significant
correlation between the quantitative composite and the
SAT-V score. The distribution of quantitative composite
and SAT-V scores is shown in Table 60.
TABLE 60
Distribution of quantitative composite and SAT-V scores
SAT-V Quant 23O-45O ^6Q-55P 560-780
01-20 69 63 14 25-50 26 44 29 55-95 15 35 45
For the correlation between the quantitative composite
and the SAT-M score, the computed values were r = .5742,
a = -81.615, b = .2027, and F = 455.72. The regression
equation was Quant = (.2027)(SAT-M) - 81.615« The standard
error was 0.0095« The computed value of F exceeded the critical value at p = .001; therefore, the null hypothesis
that there was no significant correlation between the quant itative composite of the AFOQT and the mathematics score of the SAT may be rejected. The distribution of quantitative 94 composite and SAT-M scores appears in Table 6l.
TABLE 61
Distribution of quantitative composite and SAT-M scores
SAT-M Quant 310-520 53.Q-.62Q 630-800
01-20 87 50 9 25-50 11 55 33 55-95 0 24 71
The correlation between the quantitative composite and the SAT total score resulted in computed values of r2 = .4933, a = -82.6597, b = .1098, and F = 329.13« The regression equation was Quant = (.1098)(SAT-T) - 82.6597«
The standard error was 0.0061. Because the computed value of F exceeded the critical value at p = .001, the null hypothesis that there was no significant correlation be tween the quantitative composite and the SAT-T score may be rejected. The distribution of quantitative composite and SAT-T scores is shown in Table 62.
The multiple correlation computations between the quantitative composite of the AFOQT and the two subtests of the SAT are shown in Table 63. 95
TABLE 62
Distribution of quantitative composite and SAT-T scores
______SAM______..... Quant 540-970 980-1170 1180-1470
01-20 75 65 8 25-50 14 60 25 55-95 0 36 59
TABLE 63
Multiple and partial correlations between the quantitative composite of the AFOQT and the verbal and mathematics subscores of the SAT
Subscore DOF Partials Beta Wt F Ratio Coefficient Std-Err
SAT-M 338 .5742 .7316 270.73, .1957 0.0119 SAT-v 337 .5754 .0433 0.95 .0127 0.0131
The constant was -83.915.
These data supported the decision made previously to reject the null hypothesis that there is no significant correla tion between the quantitative composite and the SAT-M score.
The data also showed that no significant additional pre dictability of the variation of the quantitative composite 96
is gained by using both the SAT-V and SAT-M together in
stead of just the latter.
The correlation between the quantitative composite
of the AFOQT and the composite score of the ACT yielded computed values of r2 = .3748, a = -69.973, b = 4.2254,
and F = 94.71. The regression equation was Quant =
¿(4.2254)(ACT-CO) - 69.973« The standard error was 0.4342.
Because the computed value of F exceeded the critical value
at p = .001, one may reject the null hypothesis that there
is no significant correlation between the quantitative
composite and the ACT-CO score. The distributions of
quantitative composite scores and ACT English, mathe
matics, social science, natural science, and composite
scores appear in Tables 64 through 68, respectively.
TABLE 64
Distribution of quantitative composite and ACT-EN scores
ACT-EN Quant ZrlS 20-22
01-20 39 21 18 25-50 11 16 12 55-95 4 17 22 97
TABLE 65
Distribution of quantitative composite and ACT-MS scores
ACT-MA Quant 3^23 \ 24-27 26-36
01-20 44 27 7 25-50 6 19 14 55-95 2 6 35
TABLE 66
Distribution of quantitative composite and ACT-SS scores
ACT-SS Quant 8^23 24-26 g.7,r33
01-20 39 26 13 25-50 11 16 12 55-95 4 15 24 98
TABLE 67
Distribution of quantitative composite and ACT-NS scores
ACT-NS Quant £^24 25-29
01-20 34 31 13 25-50 7 15 17 55-95 4 14 25
TABLE 68
Distribution of quantitative composite and ACT-CO scores
ACT-CO Quant 12-22 23z26 22^22
01-20 38 34 6 25-50 6 20 13 50-95 4 5 34 99
Multiple correlations were computed between the quantitative composite and the ACT subscores, and the significant results are shown in Table 69.
TABLE 69
Multiple and partial correlations between the quantitative composite of the AFOQT and the subscores of the ACT
Subscore DOF Partials Beta Wt Std-Err F Ratio Coefficient
ACT-MA 158 .3871 .6222 .3228 99.78 3.2248
ACT-MA 158 .3871 .5062 .3699 50.32 2.6238 ACT-SS 157 .4221 .2202 .3850 9.53 1.1881
The two constants were -49.133 and -62 .417, respectively.
These data indicate that a significant explanation of the variation in the quantitative composite can be made by using the mathematics and social science subscores of the
ACT together.
Correlations by SAT Subscore
The verbal subscore of the Scholastic Aptitude Test was found to be significantly correlated to all five com posites of the Air Force Officer Qualifying Test. The values of r ranged from .1394 for the pilot composite to
.6325 for the verbal composite. The values of F ranged 100 from 54.76 to 581.84 for the same two composites, respec
tively. The SAT-V score was a better predictor than
either the SAT-M or SAT-T scores only for the verbal com posite. For each AFOQT composite except quantitative, however, a combination of SAT-V and SAT-M scores proved a better predictor than either SAT-V, SAT-M, or SAT-T scores alone.
The mathematics subscore of the SAT was found to be significantly correlated to all five composites of the AFOQT. The computed values of r2 ranged from .1358 for the pilot composite to .5742 for the quantitative composite.
The values of F ranged from 53.13 for the pilot composite to 455.72 for the quantitative composite. The SAT-M score was a better predictor than the SAT-V or SAT-T scores for the navigator-technical and the quantitative composites.
The total SAT score was also determined to be signi ficantly correlated to all five composites of the AFOQT. p The range of r values was from .1741 for the pilot com posite to .5725 for the verbal composite. The F ratios ranged from 71.27 to 452.71 for the same two composites, respectively. The SAT-T score was found to be a better predictor than the SAT-V or SAT-M scores for both the pilot composite and the officer quality composite. 101
Correlations by ACT Subscore
The English subscore, when compared to the other sub-
scores of the ACT, was not the best single predictor for any of the AFOQT composites. If used in combination with
the social science subscore, however, it did improve pre
dictability of the verbal composite. On the other hand, if combined with the natural science subscore, it reduced
the predictability of the pilot composite. Nevertheless, the ACT-EN score as a third variable increased the pre dictability of the officer quality composite, and as a
fourth variable it increased the predictability of the navigator-technical composite. The mathematics subscore vzas a better single pre dictor of the quantitative composite than were the other subscores on the ACT composite. Furthermore, the ACT-MA score as a second variable increased the ability to pre dict the navigator-technical and officer quality composites.
The social science subscore of the ACT was a better predictor than the other subscores or the composite score for both the officer quality composite and the verbal composite. As a second variable it increased the predict ability of the quantitative composite, and as a third vari able it increased the predictability of the navigator- 102
technical composite.
The natural science subscore was a better single pre
dictor for the pilot and navigator-technical composites
than were the other subscores. When used as a third
variable, it increased the predictability of the verbal
composite.
The composite score of the ACT was better than any
single subscore as a predictor of the navigator-technical,
officer quality, and verbal composites of the AFOQT. A combination of three or four subscores proved to be better
predictors yet.
The data collected in this survey and presented above
showed that there was a substantial and significant cor relation between each composite score of the Air Force
Officer Qualifying Test and both subscores of the Scholas tic Aptitude Test, the total score of the SAT, the composite score of the American College Test, and at least one of the
ACT subscores. The results of this survey are summarized in the final chapter which concludes with recommendations. CHAPTER 4
SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS i
Summary
This study was conducted to investigate the relation ships between the Air Force Officer Qualifying Test
(AFOQT), which is a selection device in admission to
Air Force officer pre-commissioning programs, and the two most widely used college entrance examinations, the Scholastic Aptitude Test (SAT) and the American Col lege Test (ACT). Air Force personnel spend thousands of dollars and man-hours of time administering, handling, and grading the AFOQT. A significant relationship among the subtests of the ACT and SAT and the composites of the AFOQT would preclude the need for the Air Force to conduct the extensive testing program it does today.
Air Force Reserve Officers' Training Corps (AFROTC), the largest single source of new Air Force officers, spends in excess of $70,000 annually to conduct the
AFOQT program. A substantial reduction in the scope of the testing program could result in considerable savings, in
103 104
both money and man-hours, for Air Force ROTC.
Applicants for four-year Air Force ROTC scholarships
represented 30 per cent of those who took the AFOQT during
Academic Year 1973-74. Scholarship applicants were also
the only group which had submitted ACT or SAT scores to
AFROTC. Scholarship applicants became, therefore, the
population from which a sample was taken to test hypotheses
of this study. Using records which existed prior to this
survey, the AFOQT scores and either the SAT or ACT scores
were recorded for a random sample of scholarship applicants.
The data thus collected were examined using simple, par
tial, and multiple correlation techniques as well as re
gression analysis.
The data collected and analyzed in this survey indi cated a significant correlation between each composite of the Air Force Officer Qualifying Test and both the verbal and mathematics subscores and the total score of the Scho
lastic Aptitude Test as well as one or more subscores and
the composite score of the American College Test.
The pilot composite of the Air Force Officer Qualify
ing Test was found to be significantly correlated to both subscores and the total score of the Scholastic Aptitude
Test and to the natural science subscore and the composite 105
score of the American College Test. The navigator-tech
nical composite was found to be significantly correlated
to both subscores and to the total score of the SAT and
to the natural science subscore and the composite score
of the ACT. The officer quality composite was found to
be significantly correlated to both subscores and the
total score of the SAT and to the social science subscore
and the composite score of the ACT. The verbal composite was found to be significantly correlated to both sub scores and the total score of the SAT and to the social science subscore and the composite score of the ACT.
Finally, the quantitative composite of the AFOQT was determined to be significantly correlated to both sub scores and the total score of the SAT and to the mathe matics subscore and the composite score of the ACT.
Conclusions
An AFOQT composite score can be adequately predicted from either SAT or ACT scores. The pilot composite can be predicted from either one of the following equations, depending upon which college entrance examination scores were available.
PLT = (.0762)(SAT-V)+(.0657)(SAT-M) - 30.0738
PLT = (2.4403)(ACT-NS) - 19.6694 106
The navigator-technical composite can be predicted by
either of the following equations.
NAV = (.0341)(SAT-V) + (.1790)(SAT-M) - 80.4533
NAV = (1.6257)(ACT-MA) + (.6601)(ACT-SS) + (1.7841) (ACT-NS) - 66.5661
The officer quality composite can be predicted by either
of the following equations.
OQC = (.1210)(SAT-V) +(.1308)(SAT-M) - 94.3409
OQC = (1.0466)(ACT-EN) + (1.2334)(ACT-MA) + (1.3392)(ACT-SS) + (.9583)i (ACT-NS) - 71-3726
The verbal composite can be predicted by either of these
equations.
VER = (.1878)(SAT-V) + (.0346)(SAT-M) - 74.2519
VER = (1.6431)(ACT-EN) + (1.2452)(ACT-SS) + (1.0054)(ACT-NS) - 56.8897
The quantitative composite of the AFOQT can be predicted
by either of the following equations.
QUANT = (.1957)(SAT-M) + (.0127)(SAT-V) - 83-9148
QUANT = (2.6238)(ACT-MA) + (1.1881)(ACT-SS) - 62.4175
In each case above the equation listed was the best pre dictor of the AFOQT composite from ACT or SAT data, as applicable. Almost seventy per cent of the variation in the composite can be explained by the appropriate equation except the two for the pilot composite. The predictors of the pilot composite were less than fifty per cent effective 10? even though the variables exhibited a significant corre lation. The results of this study have proved that the
Air Force Officer Qualifying Test is significantly corre lated to the two most widely used college entrance exam inations, the American College Test and the Scholastic
Aptitude Test. The question is, then, to what use can this information be put by the United States Air Force?
There is no need for the Air Force to continue to expend its resources by administering the current Air Force
Officer Qualifying Test to so many thousands of people each year. A suitable substitute source of data exists in other information available to the Air Force. The Air
Force can use the results of this survey in two ways.
The first could be an immediate procedural change. The second could be a policy change which would require more time to be put into practice.
Recommendations
The current procedure in the Air Force ROTC is to administer the entire Air Force Officer Qualifying Test to all applicants for scholarships. This applies to both the
5,000 high school seniors who are applying for a four-year college scholarship and the several thousand college stu dents who are applying for two or three-year scholarships. 108
Approximately nine hundred four-year, four hundred and
fifty three-year, and nine hundred and fifty two-year
scholarships are awarded by AFROTC each year. Instead
of requiring all applicants to take the AFOQT, the managers
of the scholarship program could use the equations listed
above to predict each applicant’s AFOQT scores. Then
those predicted scores could be used as a factor in the
decision as to whether or not a given individual should
be awarded a scholarship in place of the actual AFOQT
scores used under current procedures. Certain selectees
would then be required to take the AFOQT as a precondition
for activating the scholarship. The new procedures would
mean that the Air Force would be administering the AFOQT
to approximately 10,000 fewer persons annually. This change
would save the Air Force ROTC approximately $50,000 per
year.
The second recommendation for the Air Force has a more
far-reaching implication. Using the equations produced
by this survey, a high degree of accuracy can be achieved
in predicting the navigator-technical, officer quality,
verbal, and quantitative composites of the AFOQT. The
Air Force should restructure the test so that only the flying aptitude portion, the pilot composite, would remain.
At present the pilot portion of the AFOQT uses 48 1/2 109
minutes of testing time; counting time required to read
instructions, that entire part takes about one hour. The
total test time for the current AFOQT is six hours. A
restructured test covering only flying aptitude would be
approximately one-sixth as long as the present AFOQT.
Five hours, therefore, would be saved each time the test
was administered. Furthermore, since the new test would
cover only flying aptitude, there would be no need to ad
minister the test to anyone who was not both medically
qualified for and interested in becoming a pilot. Those
wishing to become navigators, missile officers, or to work
in technical or non-technical fields would not be tested
with the AFOQT by the Aix' Force; they would have the ap
propriate "aptitude" predicted by one of the equations
presented earlier in this chapter. This change would mean
that the revised AFOQT, perhaps called the Air Force Pilot
Aptitude Test, would need to be given to fewer than 2,000 people each year at an annual cost of less than $1,500.
This survey was undertaken due to the belief of the author that the present construction and use of the Air
Force Officer Qualifying Test was redundant. It was be lieved that the information obtained from the AFOQT was readily available from other sources. Research was con ducted to determine whether or not there was a significant 110
correlation between the composites of the AFOQT and either
the ACT or the SAT or any of their subscores. The data
collected in this survey proved that such a correlation
did, in fact, exist. The study concluded with both a
short-range and long-range recommendation for action to
be taken by the United States Air Force.
Need for Further Research
In the course of this investigation, two significant
areas were uncovered in which additional research for
answers and solutions would be valuable. The first is a
possible replication of the study using a different sam
ple. VJhereas this study sampled from the population of
persons who had applied for a four-year scholarship, fur
ther valuable data might be obtained by sampling from the
other groups of persons taking the AFOQT.
The second area for further research has to do with
what the Air Force Officer Qualifying Test predicts. This
investigation has shown a significant correlation between
the AFOQT and the SAT and ACT. Likewise, previous re search has established a significant correlation between the latter scores and academic success in colleges and universities. Because the Air Force requires all new of ficers to possess at least a baccalaureate degree, it is Ill
reasonable for the Air Force to be interested in predicting
academic performance of cadets. The Air Force does not
wish to expend its scarce resources on those unlikely to
graduate. In the short range, the Air Force is concerned
with graduation and, therefore, needs an indicator of
probable performance.
In the long range, however, the Air Force needs to
identify more accurately those persons who will become
good officers. The Air Force would like to predict which
individuals will succeed as Air Force officers. There is
a need for further study of the criteria relating to
success of officers on active duty, and for investigation of added predictors of success in military assignments.
The Air Force could make adjustments to the admissions procedures into Air Force pre-commissioning programs based upon what such research might reveal. BIBLIOGRAPHY
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Educational Testing Service. ETS Builds a Test. Prince ton: ETS, 1965. ______. Multiple-Choice Questions: A Close Look. Princeton: ETS, 1963.
______. Selecting an Achievement Test: Principles and Procedures, Shortcut Statistics for Teacher- made Tests. Evaluation and Advisory Service Series No. 3. Princeton: ETS, 1961.
Freund, John E. Modern Elementary Statistics. Englewood Cliffs, New Jersey: Prentice-Hall, 1958. Fuess, Claude Moore. The College Board, Its First Fifty Years. New York: Columbia University Press, 1950.
Goslin, David A. The Search for Ability. Standardized Testing in Social Perspective. New York: Russell S age, 19^3.
Gruber, Edward C. and Morris Bramson. Scholastic Aptitude Test (SAT); for College Entrance. New York: Simon and Schuster, 1973»
Guilford, J. Paul and Benjamin Fruchter. Fundamental Statistics in Psychology and Education. 5th ed. New York: McGraw-Hill, 1973» . The Nature of Human Intelligence. New York: McGrawlHilTTÎ957 •
Gulliksen, Harold. Theory of Mental Tests. New York: Wiley, I95O.
Harcleroad, Fred F. (ed.). Issues of the Seventies. San Francisco: Jossey-Bass, 1970. 115
Harris, Chester W. (ed.). Encyclopedia of Educational Research. 3rd ed. New York: Macmillan, i960.
Hawkes, Herbert £., E. F. Lindquist, and C. R. Mann (eds.). The Construction and Use of Achievement Examinations. Boston: Houghton, Mifflin, 193'6~
Hildreth, Gertrude Howell. A Bibliography of Mental Tests and Rating Scales. 2d ed. New York: Psychological Corporation, 1939»
Holmen, Milton G. and Richard F. Docter. Educational and Psychological Testing; a Study of the Industry and its Practices. New York: Russell Sage, 1972.
How to Pass Graduate Record Examinations: Aptitude Test. New York: College Publishing, 1967.
Human Resources Research Organization. Bibliography of Publications, Human Resources Research Organization, as of 30 June 1969". Alexandria, Virginia: HumRRO, 1969.
. Bibliography of Publications and Presentations DuringTY 1972-'74. Alexandria, Virginia: HumRRO, 1974.
James, Newton E. An Investigation of Factors Affecting the Scores Made on the Annual AFROTC Achievement Examinations. Missoula: Montana State University, 1951.
Lavin, David E. The Prediction of Academic Performance. New York: Russell Sage, 19^5«
Lyman, Howard B. Test Scores and What They Mean. Engle wood Cliffs, New Jersey: Prentice-Hall, 1963.
McClelland, David C., et al. Talent and Society. Prince ton: Van Nostrand, 1958*
McDonough, Martin and Alvin J. Hansen. The College Boards Examination; Complete Preparation for the Scholastic Aptitude Test! SAT/PSATTnMSQT (verbal and mathema tic al~sections). New York: Arco, 1972. 116
Mehrens, William A. and Irvin J. Lehman. Measurement and Evaluation in Education and Psychology. New York: Holt, Rinehart, and Winston, 1973*
______and Irvin J. Lehman. Standardized Tests in Edu cation. New York: Holt, Rinehart, and' Winston, 1969.
Miller, Delbert C. Handbook of Research Design and Social Measurement. New York: MacKay, 1964.
Munday, Leo A. Comparative predictive validities of the American College Tests and two other scholastic ap titude tests. ACT P.esearchTeport No" 6. Iowa City: ACT, 1965.
______and Jeanne C. Davis. Varieties of Accomplish- ment After College: Perspectives on the Meaning of Academic Talent. Iowa City: ACT,“T974T
Myers, R. C. and D. G. Schultz. Predicting Academic Achievement with a New Attitu5e-lnterest Question naire" Trine e ton: El'S 7" 1949,
Richards, James M., Jr., John L. Holland, and Sandra W. Lutz. The Assessment of Student Accomplishment in College. ACT Research Report No. 11. Iowa Citv: Act, 1966.
, John L. Holland, and Sandra W. Lutz. The Pre- — diction of Student Accomplishment in College. ACT Research Report No. 13. Iowa City: ACT, 1966.
Selltiz, Claire, Marie Jahoda, Morton Deutsch, and Stuart W. Cook. Research Methods in Social Relations. New York: Holt, Rinehart, and W ins ton, 1964.
Shapiro, Murray, et al, (eds.). Barron's How to Prepare for the American College Testing Program. Woodbury, New York: Barron, 1972.
Stein, Morris I. Personality Measures in Admissions; Antecedent and Personality Factors as Predictors of College Success. New York: ÖEEB, 1963. 117
Tuckman, Bruce W. Conducting Educational Research. New York: Harcourt, Brace, Jovanovich, 1972.
Turner, David R. American College Testing Program; the Complete Study Guide" for Scoring High. New York: Arco, 1971.
Tyler, Leona Elizabeth. Tests and Measurements, 2d ed. Englewood Cliffs, New Jersey: Prentice-Hall, 1971.
Watkin, Harold. How to Pass College Board Admissions Scholastic Aptitude Test, SAT. New York: Cowles, 19^77 ~
Wechsler, David. The Measurement and Appraisal of Adult Intelligence. 5th ed. Baltimore: Williams and Wilkins, 1958«
Wechsler, Louis K., Martin Blum, and Sidney Friedman. College Entrance Examinations. 2d ed. New York: Barnes and Noble, 1967.
Whitney, Frederick L. The Elements of Research. New York: Prentice-Hall, 1942.
Periodicals
"AF to seek college admission as part of 'true-level' test," Air Force Times, 18 (July 12, 1958), 2.
"AFOQT (AF Off Qual Test) scoring changed." Air Force Times, 30 (November 5» 1969), 5«
Bale, Ronald M. and Rosalie K. Ambler. "Application of college and flight background questionnaires as sup plementary noncognitive measures for use in the selection of student naval aviators." Aerospace Medicine, 42 (November, 1971), II78-8I.
Borup, Jerry H. "Validity of American College test for discerning potential academic achievement levels; ethnic and sex groups." Journal of Educational Research, 65 (September, 197177 3~"S« 118
Boyce, Richard W. and R. C. Paxson. "The predictive validity of eleven tests at one state college." Educational and Psychological Measurement, 25. no. 4 (1965) . Il5>47.
Burns, Richard Leo. "An investigation of the value of the American College Testing Program, the Scholastic Aptitude Test and the Purdue Placement Test as pre dictors of academic success of Purdue University freshmen." Dissertation Abstracts, 24 (September, 1964), 1477.
Callander, Bruce. "’College credit' plan test slated for AU." Air Force Times, 18 (August 10, 1957). 1+.
Cleary, T. Anne. "Test bias: Prediction of grades of Negro and white students in integrated colleges." Journal of Educational Measurement, 5 (Summer, 1968), 115-24.
Connery, II. J. and Richard R, Waite. "Aptitude screening of naval recruits.’’ US Naval Institute Proceedings, 91 (February, 1965). 153-45.
Culpepper, Burford W., Charles L. Jennings, and Carlos J. G. Perry. "Psychiatric and psychometric predict ability of test pilot school performance." Aerospace Medicine, 43 (November, 1972), 1257-60.
Curran, P. M. and Rosalie K. Ambler. "College performance as a predictor of the flight training success of NROTC students." Aerospace Medicine, 39 (Julv, 1968), 686-87. “ -
Davis, Junius A. and George Temp. "Is the SAT biased against black students?" College Board Review, 81 (Fall, 1971), 4-9. "DOD plans mental tests for incoming officers," Air Force Times, 25 (December 23, 1964), 4.
Eckstrand, G. A. "Individuality in the learning process: Some issues and implications," Psychological Record, 12 (1962), 405-416. 119
Emrick, Charles W. "Annual written examinations (for Army aviators)," USA Aviation Digest, 9 (April, 1963), ?-8. Eyman, Richard K., C. Edward Meyers, and Robert Bendel. "New methods for test selection and reliability as sessment using stepwise multiple regression and jacknifing," Educational and Psychological Measure ment , 33 (Winter, 1973), 883-94. Fishman, J. A. "The use of tests for admission to college The next fifty years," in A. E. Traxler (ed.), Long- Range Planning For Education: Report of 22nd Educa tional Conference. (Washington, D.C.: American Council on Education, 1957), 74-79. "For officers only (revision of Air Force Officer Qualify ing Test effective 1 Sept)," Airman, 2 (October. 1958). Goolsby, T. M., Jr. and D. A. Williamson. "Use of the ROTC Qualifying Examination for selection of students to enroll in advanced courses in ROTC as juniors," Educational and Psychological Measurement, 31 TSuinmer, ”197177 3l>i57“^
Gough, Harrison G. "The construction of a personality scale to predict scholastic achievement," Journal of Applied Psychology, 37 (1953), 361-66.
______. "Misplaced Emphases in Admissions," Journal of College Student Personnel, 6 (1965), 131-35* Guilford, J. Paul, Benjamin Fruchter, and Wayne S. Zimmerman. "Factor analysis of the Army Air Force’s Sheppard Field battery of experimental aptitude tests," Psychometrika, 17 (March, 1952), 45-68.
, et al. "A factor-analytic study of Navy reasoning tests with the Air Force Aircrew Classi fication Battery," Educational and Psychological Measurement, 14 (Summer, 1954), 301-25.
Holmen, Milton G. and Richard F. Docter. "Criticisms of standardized testing," Today’s Education, 63, no. 1 (January-February, 1974), 50-W. 120
Humphreys, Lloyd G. "Fleeting nature of the prediction of college academic success," Journal of Educational Psychology, 59 (October, 1968)," 375-&O.
Jensen, A. R, "How much can we boost IQ and scholastic achievement?" Harvard Educational Review, 39 (1969). 1-123. Kohler, Emmett T. "Relationship between the Cooperative Mathematics Test, algebra III, ACT mathematics usage test, ACT composite and grade point average in college algebra," Educational and Psychological Measurement, 33 (WinterTL973Tr929-31.
Lenning, Oscar T. and E. James Maxey. "ACT versus SAT prediction for present-day colleges and students," Educational and Psychological Measurement, 33 (Summer, 197377 397-406.
Linn, Robert L. "Fair test use in selection," Review of Educational Research, 43 (Spring, 1973)» 139-61. Lins, L. Joseph, Allan P. Abell, and H. Clifton Hutchins. ".Relative usefulness in predicting academic success of the ACT, the SAT, and some other variables," Journal of Experimental Education, 35 (Winter, 1966), 1-29. "Major exam for officers is rewritten," Air Force Times, 24 (November 20, 1963), 46.
Merritt, Ray, "Predictive validity of the Americal College Test for students from low socioeconomic levels," Educational and Psychological Measurement, 32 (Summer, 1972),443-45. Munday, Leo A. "Correlations between ACT and other pre dictors of academic success in college," College and University, 44 (Fall, 1968), 67-76.
. "Predicting College Grades Using ACT Data," Educational and Psychological Measurement, 27 TSumm’er7"T967), 401-06?
"Officer aptitude tests (DORE—-Defense Officer Record Examination) to get under way May 1," Air Force Times, 25 (April 14, 1965), 2. 121
Passons, William R. "Predictive validities of the ACT, SAT and high school grades for first semester GPA and freshman courses," Educational and Psychological Measurement, 27 (Winter, 19 67)7" 1143^44.
Payne, D. A., et al. "Application of a biographical data inventoryTo estimate college academic achievement," Measurement and Evaluation in Guidance, 6 (October, 1973). 152-537-
Pfeiffer, C. M. and W. E. Sedlacek. "The validity of academic predictors for black and white students at a predominately white university," Journal of Educa tional Measurement, 8 (1971), 1-4.
Richards, James M., Jr., John L. Holland, and Sandra W. Lutz. "Prediction of student accomplishment in college," Journal of Educational Psychology, 58 (1967J, 34T3J7------
______and Sandra W. Lutz. "Predicting student ac complishment in college from the ACT assessment," J ournal. of Educational Measurement, 5 (Spring, 1968), 17-29.
"ROTC (AF Officer) Qualifying Test hit--other parts of program praised," Air Force Times, 31 (June 9, 1971), 47.
Schudson, Michael S. "Organizing the 'Meritocracy': A history of the College Entrance Examination Board," Harvard Educational Review, 42, no. 1 (February, 1972)^3^9 •
Siegelman, Marvin. "SAT and high school average predic tions of four year college achievement," Educational and Psychological Measurement, 31 (Winter, 1971), 9^7-50.
Temp, George. "Validity of the SAT for blacks and whites in thirteen integrated institutions," Journal of Educational Measurement, 8 (Winter, 1971), 245-51«
"Tests for AF Academy cadets," Army Navy Air Force Register, 78 (August 10, 1957), 6. 122
"Tests officer talents (Officer Leader Battery tests)," Army Navy Air Force Register, 79 (May 24, 1968), 10.
Thorndike, Robert L. "Concepts of culture-fairness," Journal of Educational Measurement, 8 (1971), 63-70. "'True education* tests studied as basis for college selection," Air Force Times, 18 (March 29, 1958), 39. Wherry, Robert J. and Richard H. Gaylord. "The concept of test and item reliability in relation to factor pattern," Psychometrika, 8 (1943), 247-64.
Worthington, Lois H. and Claude W. Grant. "Factors of Academic Success: A Multivariate Analysis," J ournal of Educational Research, 65 (September, 1971), 7-10.
Zimmerman, Wayne S. and William B. Michael. "A comparison of the criterion-related validities of three college entrance examinations with different content emphasis," Educational and Psychological Measurement, 27 (Summer, 1967), 407-12.
Government Documents
Air Force. Air Force Manual 35-8, Air Force Military Personnel Testing System. Washington, D.C.: U. S. Government Printing Office, 1971. Air Force Reserve Officers' Training Corps. AFOQT Overall Means Report. Maxwell AFB, Alabama: AFROTC, 1974. Austin, J. D. Comparison of Aircrew Stanines and Aviation- Cadet Officer-Candidate Qualifying Test Scores. Lackland AFB, Texas: Human Resources Research Center, 1952. Barlow, Esther (ed.). Abstracts of Personnel Research Reports : VIII, 1954-19687~ Lackland AFB, Texas: Air Force Human Resources Laboratory, I968. 123
Brogden, Hubert E., Hobart G. Osburn, Claire T. Machlin, June C. Loeffler, and Mary Render. Improvement of Tests and Techniques Used to Select ROTC. Lackland AFB, Texas: Personnel Research Laboratory, 1952.
Christai, Raymond E. Comparison of the Efficiency of the AFQT and ACQT-CX as Devices for Screening Applicants for Aircrew Training^ Lackland SFB, Texas: Air Force Personnel and Training Research Center, 1955« _____ , and John D. Krumboltz. Prediction of first semester criteria at the Air Force Academy. Lack- land AFB, Texas: Air Force Personnel and Training Research Center, 1957-
______and John D. Krumboltz. Use of the Air Force Officer Qualifying Test in the AFROTC Selection Pro gram . Lackland AFB, Texas: Air Force Personnel and Training Research Center, 1957«
"Computerized selection tests under development for pilot applicants." TIG Brief, 25, no. 3 (February 2, 1973), 6.
Cotterman, T. E. Task Classification: An Approach to Partiall?/ Ordering Information of Human Learning. Lackland AFB, Texas: Personnel Research Laboratory, 1969 • Cox, J. A. and D. J. Mullins. Evaluation of Light Plane Training among AFROTC Student Officers. Lackland AFB, Texas: Personnel Research Laboratory, 1959«
Creager, J. A. and Robert E. Miller. Predicting Achieve ment of Cadets in Their First Year at the Air Force Academy, Class of 1961. Lackland AFB, Texas: Per sonnel Research Laboratory, i960. Dailey, John T., Marion F. Shaycoft, and David B. Orr. Calibration of Air Force Selection Tests to Project TALENT Norms. Lackland AFB, Texas: Personnel Re search Laboratory, 1962. 124
Dailey, John T. and Donald B. Gragg. Postwar Research on the Classification of Aircrew. Lackland AFB, Texas: Human Resources Research Center, 1949.
Davis, Frederick Barton (ed.). The AAF Qualifying Ex amination. Washington, D.C.: US Government Print- ing Office, 1947.
______. The Construction of Spatial Orientation Items by Means of a Cyclorama. Lackland AFB, Texas: Air Force Personnel and Training Research Center, 1956. ______. Measurement of Mental Skills Employed in Arithmetic Reasoning Tests. Lackland AFB, Texas: Aeronautical Systems Division, 1961. "Diploma vs. knowledge (Tested educational level for officers)," Air Force Personnel News Letter, 11 (April, 1958), 14.
Foley, J. P., Jr. Performance Testing: Testing for What is Real. Brooks AFB,' Texas: Aerospace Medical Research Laboratory, 1963. Folsom, Willys W. Development of a Revised Officer Quality Gtanine Effective with the March 1952 Aircrew Classi fication Battery. Lackland AFB, Texas: Human Re sources Research Center, 1952. Fortura, Angelo L. (ed.). Personnel Research and Systems Advancement. Lackland AFB, Texas: Personnel Re search Laboratory, 196?• Glickman, Albert S. and David Kipnis. "Theoretical con siderations in the development and use of a non cognitive battery," Proceedings of Tri-Service Con ference on Selection Research, Washington, D.0.: Officer of Naval Research, i960.
Gregg, 0. The effect of Maturation and. Educational Ex perience on AFOQT Scores. Lackland AFB, Texas: Air Force Human Resources Laboratory,1968. Greenwood, Charles D. Performance Prediction. Maxwell AFB, Alabama: Air University, 1967. 125
Guilford, J. Paul and J. I. Lacey (eds.). Printed Glassification Tests. Washington, D.C.: US Govern ment Printing Office, 194?.
Guinn, Nancy, Ernest C. Tupes, and W. E. Alley. Cultural Subgroup Differences in the Relation ships between Air Force Aptitude Composites and Training Criteria. Lackland AFB, Texas: Air Force Human Resources Laboratory, 1970. ______/Ernest C. Tupes, and W. E. Alley. Demographic Differences in Aptitude Test Performance. Lackland AFB, Texas: Air Force Human Resources Laboratory, 1970. Krumboltz, John D. and Raymond E. Christal. Predictive Validities for First-year Criteria at the Air Force Academy. Lackland AFB, Texas: Air Force Personnel and Training Research Center, 1957«
Lecznar, William B» Comparison of Test Items Across Forms. Lackland AFB, Texas: Personnel Research Laboratory, 1964. McGrevy, David F., Stephen B. Knouse, and Ronnie A. Thompson. Relationships Among an Individual Intel ligence Test and Two Air Force Screening and Selec tion Tests. Lackland AFB, Texas: Air Force Human Resources Laboratory, 1974. Miller, Robert E. and Lonnie D. Valentine, Jr. Develop ment and Standardization of the AFOQT-64. Lackland AFB, Texas: Air Force Human Resources Laboratory, 1964.
____ . Development and Standardization of the Air Force Officer Qualifying Test Form K. Lackland AFB, Texas: Air Force Human Resources Laboratory, 1970. ___Development and Standardization of the Air Force Officer Qualifying Test - Form L. Lackland AFB, Texas: Air Force Human Resources Laboratory, 1972. 126
Miller, Robert E. Development and Standardi zation of the Air Force Officer Qualifying Test Form M. Lackland AFB, Texas: Air Force Human Resources Laboratory, 1974.
______. Development of Officer Selection and Classi fication Tests - 1966. Lackland AFB, Texas: Per sonnel Research Laboratory, 1966.
______. Development of Officer Selection and Classi- fication Tests - I9S8. Lackland AFB, Texas: Air Force Human Resources Laboratory, 1968.
______. Interpretation and Utilization of Scores on the Air Force Officer Qualifying Test. Lackland AFB, Texas: Air Force Human Resources Laboratory, 1969.
______. Predicting Achievement of Cadets in Their First Two Years at the Air Force Academy. Lackland AFB, Texas: Personnel Research Laboratory, i960.
_____Predicting Achievement of Cadets in Their First Year at the Air Force Academy, Class of i960. Lack- land AFB, Texas: Personnel Research Laboratory, I960.
______an^ *!• 4. Creager. Predicting Achievement of Cadets in Their First Year at the Air Force Academy, Class of 1962. Lackland AFB, Texas: Personnel Re search Laboratory, i960.
Predicting Achievement of Cadets in Their First Year at the Air Force Academy, Class of I963. Lack- land AFB, Texas: Aeronautical Systems Division, 1961.
___ . Predicting First Year Achievement of Air Force Academy Cadets, Class of 19647 Lackland AFB, Texas: Personnel Research Laboratory, 1964.
___ . Predicting First Year Achievement of Air Force Academy Cadets, Class of 1965. Lackland AFB, Texas: Personnel Research Laboratory, 1964. 12?
Miller, Robert E. Predicting First Year Achievement of Air Force Academy Cadets, Class of 19667 Lackland AFB, Texas: Personnel Research Laboratory, 1965« ______. Predicting First Year Achievement of Air Force Academy Cadets, Class of 1967■ Lackland AFB, Texas: Personnel Research Laboratory, 1966.
■ . Predicting First Year Achievement of Air Force Academy Cadets, Class of 19667 Lackland AFB, Texas: Air Force Human Resources Laboratory, 1968.
■ Prediction o£ £egJiu.i£fll. Training Criteria £rem AFOQT Composites. Lackland AFB, Texas: Air Research and Development Command, Personnel Laboratory, i960. ___ . Relationship of AFOQT Scores to Measures of Success in Undergraduate Pilot and Navigator Train ing. Lackland AFB, Texas: Personnel Research Lab oratory, 19 66. Tomlinson, Helen and John T. Preston. Development of a Short Test to Predict a Complex Aggregate Score on a Battery of Tests. Lackland AFB, Texas: Human Resources Research Center, 1952. Tupes, Ernest 0. Gross Validation of USAF Biographical Inventory, PRT-T/Officer Leadership Score. Lack- land AFB, Texas: Personnel Laboratory, 1956.
• and John A. Creager. The Development of a Re vised Officer Leadership Score for USAF Biographical Inventory, PRT-47 Lackland AFB, Texas: Personnel Research Laboratory, 1955•
______and Robert E. Miller. Equivalence of AFOQT Scores for Different Educational Levels. Lackland AFB, Texas: Air Force Human Resources Laboratory, 1969. ______and Raymond E. Christal. The Officer Aptitude Test, AFPRT 423• Lackland AFB, Texas: Personnel Research Laboratory, 1954. 128
Tupes, Ernest C. and H. L. Madden. Prediction of Officer Performance and Retention from Selected Character istics of the College Attended. Lackland AFB, Texas: Air Force Human Resources Laboratory, 1968.
- . A Proposal for an Officer Effectiveness Selec tion Battery Based On Measures Obtainable during Basic and Advanced AFROTC. Lackland AFB, Texas: Air Force ¡Personnel and Training Research Center, 1957«
______. The Validity of the Aviation Cadet-Officer Candidate Qualifying Test AXA and AXB for Prediction of Success in USAF Officer Candidate School. Lack- land AFB, Texas: Human Resources Research Center, 1953- Valentine, Lonnie D., Jr. Air Force Academy Selection Variables as Predictors of Success in Pilot Train ing. Lackland AFB, Texas: Aeronautical Systems Division, 1961.
■ Development of the Air Force Precommissioning Screening Test - 62. Lackland AFB, Texas: Aero nautical Systems Division, 1961.
and John A. Creager. Officer Selection and Class- ~" ification Tests: Their Development and Usd. Lackland AFB, T exas':' " Aeronautical Systems Division, 1961.
______. Validity of the AFOQT (Form A) for Prediction of Student-Officer Success in Observer Training. Lackland AFB, Texas: Personnel Research Laboratory, 1958« Validation of Two Aircrew Psychomotor Tests. Lackland AFB, Texas: Air Force Human Resources Laboratory, 1974.
Valverde, Horace H, and Eleanor J. Youngs (eds.). Anno tated Bibliography of the Training Research Division Reports, 1950-1969• Lackland AFB, Texas: Air Force Human Resources Laboratory, 1969.
Zachert, Virginia and Frank C. Ivens. April 1951 A and B Aircrew Classification Battery. Lackland AFB, Texas: Human Resources Research Center, 1952. 129
Zachert, Virginia and Franklin L. Hill. The Aviation Cadet Qualifying Test, PRT-3, Compared with the April 1951 Aircrew Classification Battery. Lackland AFB, Texas« Human Resources Research Center, 1952.
• and G. Friedman. The Factorial Content of the Aircrew Classification Battery. Lackland AFB, Texas» Human Resources Research Center, 1952.