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Basic Statistics WE MOST OFTEN USE

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  1. Basic Statistics WE MOST OFTEN USE Student Affairs Assessment Council Portland State University June 2012

  2. Overview of the Session • Introduction to statistics • Things to know before you run statistics • How to run & understand descriptive statistics using Campus Labs

  3. How we use statistics in assessment • Produce information for decision making & improvement • Take data points and transform them into information • Descriptive rather than inferential. Need to know if do these: • Surveys (focus of today’s examples) • Experiments • Quasi-experiments • Secondary data analysis (e.g., using institutional datasets) • Rubrics (the scored part)

  4. Consider these before you run statistics! • What does your instrument measure & how well does it do it? (reliability and validity) • Who participated and how representative are they? (sampling) • What levels are you measuring, as it matters for the types of analyses you can run (ordinal, nominal,…)

  5. What does your instrument measure & how well does it do it? • Face and Content Validity • How to do: • Review by subject-matter expert • Link to literature review and/or theoretical framework • Align with content of your program. • Pilot-test item quality with representative sample

  6. Who participated and how representative are they? (sampling) • Population: Entire group that is of interest to you (e.g., all enrolled undergraduate students). • Sample: Sub-set of your population (e.g., sample of 1000 undergraduate students). • Respondents: are then the number of people who respond to your survey. • Match to original population by looking at demographics of your respondents

  7. What levels are you measuring • Statistics are appropriate or inappropriate based on the levels of measurement in your data. • Levels of measurement • Nominal • Ordinal • Continuous

  8. Nominal Data • Categorizes without order = categorical data • Applies to data which are only classified by name, labels, or categories (e.g., gender, living on or off campus, political affiliation, yes/no) • N, %, Mode

  9. Ordinal Data • Assigned order that matters • Differences between categories may not be equal (e.g., Strongly agree, Agree, Disagree, Strongly disagree) • N , %, mode often treated as continuous 4 3 2 1 -

  10. Continuous • Interval & Ratio • Categorizes based on difference, order, AND units of equal difference between variables (e.g., individuals’ IQ scores and difference across and between those scores; age, salaries) • N, &, Mean, Median if skewed

  11. Two kinds of statistics • Descriptive • Discuss a large amount of data in an abbreviated fashion • Highlight important characteristics of data • Inferential • Go beyond description • Show relationships between groups • Use sample data to draw inferences about the population

  12. Descriptive Statistics

  13. Measures of Frequency • Acceptable for all data levels • Count/Frequency – the # who gave response • Percent – count/total possible responses. Use when comparing data.

  14. Measures of Central Tendency • Ordinal and Continuous data • Mean: the average (e.g., 3.25) • Median: value of the data that occupies the middle position when the data is ordered from smallest to largest Mode: data point/answer that occurs most frequently

  15. Reporting Counts, Percents or Means Count Percentage Mean

  16. Measures of Dispersion: How spread out are the data? • Is there a large variation in student answers to how welcomed they feel in the Student Union? • Standard deviation: Average distance from the mean. • small standard deviation means that scores or values cluster around the mean.

  17. Correlations • Chi-square tests • T-tests • ANOVA • Regression Inferential Statistics • Compare groups • Generalize from the sample to the population • Determine if the difference between groups is dependable or by chance

  18. Comparisons in Campus Labs • Key-Performance Indicators (KPI): track means or percentages over time • StudentVoice Benchmarking T-Test Calculations in their comparative reports • https://www.studentvoice.com/app/wiki/Print.aspx?Page=Viewing%20Benchmark%20Project%20Results • Directions for these under WIKI • https://www.studentvoice.com/app/wiki/MainPage.ashx

  19. Example of a comparative analysis report

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