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# Basic Statistics WE MOST OFTEN USE - PowerPoint PPT Presentation

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

Student Affairs Assessment Council

Portland State University

June 2012

• Introduction to statistics

• Things to know before you run statistics

• How to run & understand descriptive statistics using Campus Labs

• 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)

• 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,…)

• 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

• 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

• Statistics are appropriate or inappropriate based on the levels of measurement in your data.

• Levels of measurement

• Nominal

• Ordinal

• Continuous

• 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

• 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

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• 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

• 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

• Acceptable for all data levels

• Count/Frequency – the # who gave response

• Percent – count/total possible responses. Use when comparing data.

• 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

Count

Percentage

Mean

• 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.

Inferential Statistics

• Compare groups

• Generalize from the sample to the population

• Determine if the difference between groups is dependable or by chance

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