SPSS

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