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Basic Statistics WE MOST OFTEN USE

Basic Statistics WE MOST OFTEN USE. Student Affairs Assessment Council Portland State University June 2012. Overview of the Session. Introduction to statistics Things to know before you run statistics How to run & understand descriptive statistics using Campus Labs.

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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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