Research Methods in
Human-Computer Interaction
Chapter 4Statistical Analysis
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Chapter 4
Overview
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Preparing data for statistical analysis
Descriptive statistics
Comparing means
T tests
Analysis of variance (ANOVA)
Assumptions of t tests and F tests
Identifying relationships
Regression
Nonparametric statistical tests
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Chapter 4
Preparing data for analysis
• Cleaning up data
– Detect errors
– Formatting
• Coding
– Types of data that need to be coded
– Be consistent
• Organizing the data
– Accommodate to the requirements of statistical software
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Chapter 4
Descriptive statistics
• Measures of central tendency
– Mean
– Median
– Mode
• Measures of spread
– Range
– Variance
– Standard deviations
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Chapter 4
Comparing means
• Summary of methods
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Chapter 4
Comparing 2 means: T tests
• Independent-samples t test: between-group design
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Chapter 4
Comparing 2 means: T tests
• Paired-sample t test: within-group design
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Chapter 4
Comparing 2 or more means:
Analysis of variance (ANOVA)
• Also called F tests
• One-way ANOVA: for between-group design
• Data layout: Table 4.6
• Results summary:
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Chapter 4
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Chapter 4
Factorial ANOVA
• For between-group design
• 2 or more independent variables involved
• Data layout: table 4.9
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Chapter 4
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Chapter 4
Factorial ANOVA
• Summary results
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Chapter 4
Repeated measures ANOVA
• For within-group design
• Can investigate one or more variables
• One-way ANOVA
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Chapter 4
Repeated measures ANOVA
• One way ANOVA summary report:
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Chapter 4
Repeated measures ANOVA
• Two way ANOVA experiment design:
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Chapter 4
Repeated measures ANOVA
Two way ANOVA data layout
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Chapter 4
Repeated measures ANOVA
• Two way ANOVA summary report:
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Chapter 4
Split-plot ANOVA
• Involves both between-group and within-group
factors
• Experiment design
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Chapter 4
Split-plot ANOVA data layout
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Chapter 4
Split-plot ANOVA summary report
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Chapter 4
Assumptions of t tests and F tests
• Errors should be independent of each other
• Errors should be identically distributed
• Errors should be normally distributed
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Chapter 4
Identify relationships
• Correlation: Two factors are correlated if there is a
relationship between them
• Most commonly used test for correlation is the
Pearson’s product moment correlation coefficient
test
• Pearson’s r: ranges between -1 to 1
• Pearson’s r square represents the proportion of the
variance shared by the two variables
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Chapter 4
Identify relationships
• Correlation does not imply causal relationship
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Chapter 4
Identify relationships
• Regression: can investigate the relationship
between one DV and multiple IVs
• Regression is used for 2 purposes:
– Model construction
– Prediction
• Different regression procedures
– Simultaneous
– Hierarchical
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Chapter 4
Non-parametric tests
• Non-parametric tests are used when:
– The error is not normally distributed
– The distances between any two data units are not
equal
– The variance of error is not equal
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Chapter 4
Non-parametric tests
• CHI-square test
– Used to analyze categorical data
– Table of counts (contingency table)
– Assumptions of the test
• Data points need to be independent
• The sample size should not be too small
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Chapter 4
Non-parametric tests
• Two groups of data
– For between-group design: Mann–Whitney U test
or the Wald–Wolfowitz runs test
– For within-group design: Wilcoxon signed ranks
test
• Three or more groups of data
– For between-group design: Kruskal–Wallis oneway analysis of variance by ranks
– For within-group design: Friedman’s two-way
analysis of variance test
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Chapter 4
End-of-chapter
• Summary
• Discussion questions
• Research design exercise
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Chapter 4