I. Introduction and Overview of Descriptive Erin J. Farley Ph.D. & Stan Orchowsky Ph.D.

Justice and Statistics Association 1/14/2016

Justice Research and Statistics Association 720 7th Street, NW, Third Floor Washington, DC 20001 Training and Technical Assistance Webinar Series

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Justice Research and Statistics Association 720 7th Street, NW, Third Floor Washington, DC 20001 Objectives

• Define key concepts in descriptive statistics

• Demonstrate how to run descriptive statistics in Excel and SPSS

Area of Interest, idea, or theory

Conceptualization Choice of Research Specify the meaning of the Population & concepts to be studied Method Whom do we want to be able to draw Research conclusions about? Who will be observed for that purpose? Operationalization How will we actually Existing research measure the variables under Comparative research study? Evaluation research

Observations Collecting data for analysis & interpretation

Analysis Analyzing data drawing conclusions

Application Maxfield & Babbie Reporting results & assessing their (2012) Basics of implications Research Methods Descriptive and Inferential Statistics

• Summarize, organize, and make sense Descriptive of a set of scores or observations • Describe characteristics of a

• Allows us to take measurements from a sample and to “infer”, or use this Inferential information to estimate the unknown characteristics of a larger population Data Types

•Measures Quantitative •Methods

•Measures Qualitative •Methods Data Types

Latent : Not observable & can only be measured indirectly Manifest : A variable that can be observed

Variables: Validity: Degree to which variable accurately reflects Any characteristic or the concept it is intended to measure attribute of persons, objects, or events that Reliability: can take on different Refers to the consistency or “repeatability” of numerical values the operationalization of the concept

Independent: Variable that is manipulated to determine impact (x) Dependent: Variable influenced by another(y) Collecting Quantitative Data

Unit of Measurement Analysis • Objective for • Process of • No meaningful observation assigning information for • Individuals numbers to a given observations • Towns observation • EX: • ≠ 0 • States • Likert-scale • -99 Levels of Measurement

• Qualitative, Nominal

• Quantitative, categorical variable Ordinal • Rank-ordered categories

• Quantitative, continuous variable Interval • Distance between values is known and constant

• Quantitative, continuous variable Ratio • Distance between values is known and equal w/ true zero pt & Distribution

Frequency • A table of response categories of a variable and the number of times each Distribution outcome is observed

Cumulative • For a given score the total number of cases in a distribution at or below that Frequency value

Cumulative • For a give score the percentage of cases Percent in a distribution at or below that value

recode avergrade (1 thru 9 eq copy)(-99 eq sysmiss) into avegd. exe. value labels avegd 1 'D' 2 'C-' 3 'C' 4 'C+' 5 ' B-' 6 'B' 7 'B+' 8 'A-' 9 'A'. exe. freq avegd. exe. Begin Excel Example Measures of

• of a group of scores

• The exact middle score in a distribution of ranked scores

• Most frequent score in distribution of scores

freq offense_1_sum /stats = mean median mode. exe. freq offense_1_sum temporary. /format=notable select if state ne 21. /stats = mean median mode. freq offense_1_sum /format=notable /stats = mean median mode.

Begin Excel Example Measures of Dispersion

Variance & Mean Deviation Example 1 Freq =offense_1_sum /format=notable /stats= stddev range minimum.

Example 2

Example 3 Begin Excel Example Future Topics in the Statistical Analysis for Criminal Justice Research Series • Sampling Basics • Significance Testing: Comparing Proportions • Significance Testing: Comparing • Correlation and Simple • Displaying Data • Multiple Linear Regression • • Exploratory