1. Analyzing Survey Data Angelina Hill, Associate Director of Academic Assessment 2009 Academic Assessment Workshop May 14 th & 15 th UNLV

2. Prior to Analysis What would you like to discover? Perceived competence Preferences, satisfaction Group differences Demographics What are your predictions?

3. Prior to Analysis Your goals drive the make-up of the survey and how it should be analyzed. Exploration can be informative, but with an analysis plan.

4. Prior to Analysis Survey design & layout Stylistic considerations are important because they increase response, validity, and reliability

5. Survey Design Good questions reduce error By increasing the respondent’s willingness to answer Increases reliability and validity. Less error = better data

6. Reliability & Validity Reliability – Is the survey measuring something consistently? Typically measured using Chronbach’s alpha Validity – Is the survey measuring what it’s supposed to be measuring? Typically measured using factor analysis

7. Construct Validity Does your measure correlate with a theorized concept of interest? Correlate measure with values that are known to be related to the construct.

8. Pilot Piloting the survey can inform: Question clarity Question format Variance in responses

9. Survey Analysis Using data from Paper Surveys SurveyMonkey SelectSurvey.Net

10. Survey Analysis Paper surveys Put data in spreadsheet format using excel or SPSS Columns represent variables Rows represent respondents

11. Survey Analysis Paper surveys Create a data matrix Variable name || Numeric Values || Numeric labels Summarize open-ended questions separately Response group || frequency

12. Survey Analysis SurveyMonkey Available under the analyze results tab Frequencies & crosstabs Download all responses for further analysis  Select Download responses from menu  Choose type of download – select all responses collected  Choose format – select condensed columns and numeric cells.

13. Survey Analysis SelectSurvey.NET Available under Analyze  Results Overview Frequencies Download all responses for further analysis  Select Export Data from Analyze page  Export Format – CSV (excel)  Data Format – SPSS Format Condensed

14. Data Cleaning Process of detecting, diagnosing, and editing faulty data Basic Issues: lack or excess of data outliers, including inconsistencies unexpected analysis results and other types of inferences and abstractions

15. Data Cleaning Inspect the data Frequency distributions Summary statistics Graphical exploration of distributions  Scatter plots, box plots, histograms

16. Data Cleansing Out of range Delete values and determine how to recode if possible Missing Values Refusals (question sensitivity) Don’t know responses (can’t remember) Not applicable Data processing errors Questionnaire programming errors Design factors Attrition

18. Missing Data Missing completely at random (MCAR) Cases with complete data are indistinguishable from cases with incomplete data. Missing at random (MAR) Cases with incomplete data differ from cases with complete data, but pattern of missingness is predicted from variables other than the missing variable. Nonignorable The pattern of data missingness is non-random and it is related to the missing variable.

19. Missing Data Listwise or casewise data deletion: If a record has missing data for any one variable used in a particular analysis, omit that entire record from the analysis. Default in most packages, including SPSS & SAS Pairwise data deletion: For bivariate correlations or covariances, compute statistics based upon the available pairwise data. Useful with small samples or when many values are missing Substitution techniques: Substitute a value based on available cases to fill in missing data values on the remaining cases. Mean Substitution, Regression methods, Hot deck imputation, Expectation Maximization (EM) approach, Raw maximum likelihood methods, Multiple imputation

20. Descriptive Statistics Frequency distribution

21. Descriptive Statistics Cross-tabs Excel Pivot tables Excel menu  Data  PivotTable and PivotChart PivotTable menu  Field setting  summarize by count  show data as % of row or column

22. Data Analysis Measurement scale determines how the data should be analyzed: Nominal, ordinal, interval, ratio Move from categorical information, to also knowing the order, to also knowing the exact distance between ratings, to also knowing that one measurement in twice as much as another.

23. Data Analysis Three instructors are evaluating preferences among three methods (lecture, discussion, activities) 1) Identify most, second, and least preferred. 2) Identify your favorite. 3) Rate each method on a 10-point scale, where 1 indicates not at all preferred and 10 indicates strongly preferred.

24. Data Analysis Nominal & ordinal variables are discrete Can be qualitative or quantitative Interval & ratio variables are continuous Grades Age

25. Data Analysis Charts Pie charts & bar charts used for categorical data Histograms used for continuous data Line graphs typically show trends over time

26. Data Analysis Other descriptive statistics Mean preferred, uses all of the data Median ordinal data open-ended scale outliers Mode nominal data

27. Data Analysis Other descriptive statistics Interquartile range Variability accompanying the median Standard deviation Variability accompanying the mean

28. Correlations Are the variables related? Determine variables that relate most to your item of interest Correlate Likert-scale questions with each other Correlate interval/ratio demographic information (e.g., age) to Likert-scale questions

29. Correlation Which correlation coefficient to use? Pearson’s r Used with interval and ratio data Spearman & Kendall’s tau-b Used with ordinal data Spearman used for linear relationship Kendall’s tau-b for any increasing or decreasing relationship

30. Mean Differences Are there meaningful differences between groups? class sections instructors on-line vs. off-line courses major vs. non-major

31. Mean Differences Which test to run? Interval and ratio data t-test when comparing 2 groups  Independent  Dependent (paired-samples in spss) ANOVA when comparing > 2 groups  Independent (One Way ANOVA in spss)  Dependent (general linear model-repeated measure in spss)

32. Presenting Results Describe the purpose of the survey List the factors that motivated you to conduct this research in the first place. Include the survey! On assessment reports When the survey is new/still being fine tuned How it was administered

33. Presenting Results Present the breakdown of results Tables and graphs should complement text Conclusions Explain findings, especially facts that were important or surprising Recommendations Describe an action plan based on concise concluding statements

34. Presenting Results Share results in formal venues Familiarize yourself with key findings so that you can mention results at every opportunity

35. Moving Forward Continuously improve the survey Delete, add, change questions Evaluate method of administration Compare results across semesters to look for improvements Compare with other assessment data for a broader picture