Crosstabulations and Measures of Association Correlation

Crosstabulations and Measures of Association Correlation

In this week, we take a look at cross-tabulation or contingency tables, which deals with analysis of tabular data. This implies analysis of categorical/nominal variables. Moreover, chi-square and other measures of association are discussed, too.

Crosstabulation or contingency tables deals with analysis of tabular data, which implies analysis of categorical/nominal variables. In this module, chi-square and other measures of association are discussed.

Upon successful completion of this module, you will be able to:

• Develop research questions and testable hypotheses linked to existing theory or research.
• Develop hypotheses, choose appropriate statistics to test them, and describe the results correctly in a short research paper.
• Develop research questions and testable hypotheses linked to existing theory or research.
• Perform descriptive and inferential statistical analysis of public administrative datasets using IBM SPSS software.
• Interpret results from descriptive and inferential statistical analysis of public administrative datasets and place results in APA formatted text, tables, and figures.

Cross-tabulation is a statistical mechanism used to categorize data. Statistics entail crunching down different data types and making comparisons to determine their similarities, differences, and patterns that affect how the variables influence each other. Cross-tabulation or contingency tables are essential tools in analyzing categorical or nominal variables, which often take a tabular format. This paper reflects on the significance of cross-tabulation and hypothetical suppositions addressing the phenomenon of data collection and analysis.

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