I. Introduction and Overview of Descriptive Statistics Erin J. Farley Ph.D. & Stan Orchowsky Ph.D. Justice Research 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 This webinar is being audio cast via the speakers on your computer and via teleconference. To access the audio stream information, select audio and audio conference from the menu bar. This will display the call information and the button to access the audio stream. If you have speakers or headphones for your computer there is no need to call in, simply select call using computer. Justice Research and Statistics Association 720 7th Street, NW, Third Floor Washington, DC 20001 Training and Technical Assistance Webinar Series All telephones have been muted. If you would like to ask a question please use the chat feature unless instructed otherwise. Please remember to select Host. 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 & Sampling concepts to be studied Method Experiments Whom do we want to be able to draw Survey Research conclusions about? Who will be Field Research observed for that purpose? Operationalization Content analysis How will we actually Existing data 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 sample • 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 Missing Data 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, categorical variable 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 Frequency & 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 Central Tendency • Average of a group of scores Mean • The exact middle score in a distribution Median of ranked scores • Most frequent score in distribution of Mode 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 & Range Mean Deviation Standard Deviation Skewness 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 Means • Correlation and Simple Linear Regression • Displaying Data • Multiple Linear Regression • Logistic Regression • Exploratory Data Analysis .