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Justice Research and Statistics Association 720 7th Street, NW, Third Floor Washington, DC 20001
I. Introduction and Overview of Descriptive Statistics
Erin J. Farley Ph.D. &
Stan Orchowsky Ph.D.
Justice Research and Statistics Association 1/14/2016
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Training and Technical Assistance Webinar Series
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Justice Research and Statistics Association 720 7th Street, NW, Third Floor Washington, DC 20001
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Training and Technical Assistance Webinar Series
Justice Research and Statistics Association 720 7th Street, NW, Third Floor Washington, DC 20001
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.
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Objectives
• Define key concepts in descriptive statistics
• Demonstrate how to run descriptive statistics in Excel and SPSS
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Area of Interest, idea, or theory
Conceptualization Specify the meaning of the
concepts to be studied
Operationalization How will we actually
measure the variables under study?
Choice of Research Method
Experiments Survey Research Field Research
Content analysis Existing data research Comparative research Evaluation research
Population & Sampling
Whom do we want to be able to draw conclusions about? Who will be
observed for that purpose?
Observations Collecting data for analysis & interpretation
Analysis Analyzing data drawing conclusions
Application Reporting results & assessing their
implications
Maxfield & Babbie (2012) Basics of Research Methods
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Descriptive and Inferential Statistics
• Summarize, organize, and make sense of a set of scores or observations
• Describe characteristics of a sample Descriptive
• Allows us to take measurements from a sample and to “infer”, or use this information to estimate the unknown characteristics of a larger population
Inferential
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Data Types
•Measures •Methods
Quantitative
•Measures •Methods
Qualitative
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Data Types
Variables: Any characteristic or attribute of persons,
objects, or events that can take on different
numerical values
Latent : Not observable & can only be measured
indirectly
Manifest : A variable that can be observed
Independent: Variable that is manipulated to determine
impact (x)
Dependent: Variable influenced by another(y)
Validity: Degree to which variable accurately reflects
the concept it is intended to measure
Reliability: Refers to the consistency or “repeatability” of
the operationalization of the concept
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Collecting Quantitative Data
Unit of Analysis
• Objective for observation • Individuals • Towns • States
Measurement
• Process of assigning numbers to observations
• EX: • Likert-scale
Missing Data
• No meaningful information for a given observation
• ≠ 0 • -99
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Levels of Measurement
Nominal • Qualitative, categorical variable
Ordinal • Quantitative, categorical variable • Rank-ordered categories
Interval • Quantitative, continuous variable • Distance between values is known and constant
Ratio
• Quantitative, continuous variable • Distance between values is known and equal w/ true zero pt
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Frequency & Distribution
• A table of response categories of a variable and the number of times each outcome is observed
Frequency Distribution
• For a given score the total number of cases in a distribution at or below that value
Cumulative Frequency
• For a give score the percentage of cases in a distribution at or below that value
Cumulative Percent
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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.
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Begin Excel Example
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Measures of Central Tendency
Mean • Average of a group of scores
Median • The exact middle score in a distribution
of ranked scores
Mode • Most frequent score in distribution of
scores
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freq offense_1_sum /stats = mean median mode. exe.
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temporary. select if state ne 21. freq offense_1_sum /format=notable /stats = mean median mode.
freq offense_1_sum /format=notable /stats = mean median mode.
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Begin Excel Example
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Measures of Dispersion
Range Mean Deviation Variance &
Standard Deviation
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Skewness
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Example 1
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Freq =offense_1_sum /format=notable /stats= stddev range minimum.
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Example 2
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Example 3
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Begin Excel Example
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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