©2010 john wiley and sons chapter 4 research methods in human-computer interaction chapter 4-...

©2010 John Wiley and Sons www.wileyeurope.com/college/lazar Chapter 4

Research Methods inHuman-Computer Interaction

Chapter 4-Statistical Analysis


Overview• 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


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


Descriptive statistics

• Measures of central tendency– Mean– Median– Mode

• Measures of spread– Range– Variance– Standard deviations


Comparing means

• Summary of methods


Comparing 2 means: T tests

• Independent-samples t test: between-group design


Comparing 2 means: T tests

• Paired-sample t test: within-group design


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:


Factorial ANOVA

• For between-group design• 2 or more independent variables involved• Data layout: table 4.9


Factorial ANOVA

• Summary results


Repeated measures ANOVA

• For within-group design• Can investigate one or more variables• One-way ANOVA



• One way ANOVA summary report:



• Two way ANOVA experiment design:


Two way ANOVA data layout




• Two way ANOVA summary report:


Split-plot ANOVA

• Involves both between-group and within-group factors

• Experiment design


Split-plot ANOVA data layout


Split-plot ANOVA summary report


Assumptions of t tests and F tests

• Errors should be independent of each other• Errors should be identically distributed• Errors should be normally distributed


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



• Correlation does not imply causal relationship



• 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


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



• 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



• 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 one-

way analysis of variance by ranks– For within-group design: Friedman’s two-way

analysis of variance test


End-of-chapter

• Summary• Discussion questions• Research design exercise

©2010 john wiley and sons chapter 4 research methods in human-computer interaction chapter 4-...

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