Chapter 10 Analyzing the Association Between Categorical Variables
2 Categorical Variables (frequencies)
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2 Categorical Variables (frequencies) Testing mean differences of a continuous variable between groups (categorical variable) 2 Continuous Variables Levels of SES Treatment Conditions + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + ControlEmotion FocusEmpowrmnt 4 6 8 10 12 14 Self-Esteem Treatment Condition 5 10 15 20 4 6 8 10 12 14 Resilience) Self-Esteem 2 CATEGORICAL MEAN DIFFERENCES 2 CONTINUOUS
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Transcript of 2 Categorical Variables (frequencies)
2 Categorical Variables (frequencies)
Testing mean differences of a continuous variable between groups (categorical variable)
2 Continuous Variables
Levels of SES
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Control Emotion Focus Empowrmnt
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2 CATEGORICAL MEAN DIFFERENCES 2 CONTINUOUS
2 Categorical Variables
Describe
Frequencies
Crosstabs
Graphs
Bars or pie
Clustered Bars
Analyze
Chi-square
2 CATEGORICAL MEAN DIFFERENCES 2 CONTINUOUS
Describing Categorical Variables: Frequencies
Analyze> Descriptive Statistics>
Frequencies
Charts... button
Drop categorical variables into the Variable(s) box
Choose either Bar charts or Pie charts to graph categorical variables.
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Describing Categorical Variables: Crosstabulation
Analyze> Descriptive Statistics>
CrosstabsCells... button
Use the Crosstabs to report frequencies between two variables and create clustered bar charts. For descriptives: choose to display Percentages in each cell.
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Analyzing Two Categorical Variables: Chi-square statistics
Crosstabs> Statistics... button Cells... button
Request Chi-square to test the independence between the variables and Phi and Cramer’s V as effect sizes.
To understand the relationship between the two categorical variables request Standardized Residuals and Expected Values
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Compare Mean Differences
Describe
Frequencies or
Descriptives
Explore
Graphs
Bars, Lines or Boxplots
Analyze
Two groups: t-test
+2 groups: one-way
anova
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Descriptives for Mean ComparisonsAnalyze>Descriptive Statistics > Frequencies Statistics... button
You can also use Frequencies to get statistics for continuous variables.
2 CATEGORICAL MEAN DIFFERENCES 2 CONTINUOUS
Descriptives for Mean ComparisonsAnalyze>Descriptive Statistics > Frequencies Charts... button
You can also use Frequencies to get statistics for continuous variables.
...and also to request the histogram with the normal curve.
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Descriptives for Mean ComparisonsAnalyze>
Descriptive Statistics >Descriptives
Options... button
Alternatively you can use Descriptives, but you will have to use Explore (next slide) without a Factor List variable to get the histograms, boxplots and tests of normality for the whole sample
2 CATEGORICAL MEAN DIFFERENCES 2 CONTINUOUS
Descriptives for Mean ComparisonsAnalyze>
Descriptive Statistics >Explore
Plots... button
Use Explore to produce the descriptives of a continuous variable at levels of a categorical variable. On the Plots button change the Stem-and Leaf for a Histogram and choose Normality plots with tests. You may disregard the normality plots (except the boxplot). Normality tests are read: If significant, the variable is not normally distributed, if not significant the variable is normallly distributed.
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Tests for Mean Comparisons: t test for two groups
Analyze>Compare Means>
Independent Samples t-test
Define Groups... button
You may test at the same time different continuous variables for differences between two groups defined by a categorical variable. Remember to check for homogeneity of variances. If the test is significant read the “Equal Variances Not Assumed” row, the second. If the test is not significant read the “Equal Variances Assumed” row, the first row.
2 CATEGORICAL MEAN DIFFERENCES 2 CONTINUOUS
Tests for Mean Comparisons: One-way ANOVA for more than 2 groups
Analyze>Compare Means>
One Way ANOVA
Options... button
If your categorical variable separates your sample in more than two groups you have to use a one way ANOVA test.
Unlike the t-test, homogeneity of variance tests are not produced by default. You have to request them on the Options button
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Tests for Mean Comparisons: One-way ANOVA for more than 2 groups
Analyze>Compare Means>
One Way ANOVA
Post Hoc... button
If the ANOVA test is significant you have to check for the pairwise (between two groups) comparisons using the Post-Hoc button.
These are some of the most commonly used Post-hoc tests. You should request them in the same step as your ANOVA. They are somewhat equivalent to running multiple t-tests among the different group combinations.
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Descriptives for Mean ComparisonsAnalyze>
Descriptive Statistics >Descriptives
Options... button
Alternatively you can use Descriptives, but you will have to use Explore (next slide) without a Factor List variable to get the histograms, boxplots and tests of normality for the whole sample
2 CATEGORICAL MEAN DIFFERENCES 2 CONTINUOUS
Descriptives for Mean ComparisonsAnalyze>
Descriptive Statistics >Descriptives
Options... button
Alternatively you can use Descriptives, but you will have to use Explore (next slide) without a Factor List variable to get the histograms, boxplots and tests of normality for the whole sample
2 CATEGORICAL MEAN DIFFERENCES 2 CONTINUOUS
2 Continuous Variables
Describe
Frequencies or
Descriptives
Graphs
Scatterdot
Analyze
Correlations: Pearson or Spearman
Partial-Correlations
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Descriptives for 2 Continuous VariablesAnalyze>
Descriptive Statistics >Frequencies
Statistics and Charts... buttons
The same as with the continuous variables you want to compare means, you can use the Frequencies to get descriptive statistics and the histogram with the normal curve. You can also get similar information with the Descriptives and Explore as shown above.
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Correlations Between Two Continuous Variables
Analyze> Correlate> Bivariate• Choose either Pearson or
Spearman depending on the normality of the test.
• Pearson is the usual correlation on continuous variables
• Spearman runs the correlation on the ranked data and it is used with there are not many cases or there are outliers that will bias the correlation.
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Partial Correlation of Two Continuous Variables Controlling for a Third one
Analyze> Correlate> Partial• Cannot use it for Spearman
correlation• There can be more than one
“Controlling for” variables.• The “Controlling for” variables
can be either continuous or categorical with only two levels.
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Graphs for 2 Continuous VariablesGraphs> Chart Builder• After selecting Scatter/Dot drag
or double click the first one on the left top corner into the chart area.
• Then select the two continuous variables one to each of the axis and click ok.
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