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Analysing a single nominal variable

Part 4: Reporting

If we combine all the reporting bits from the example, the full report for this variable, might have looked something like:

The media often reports that people are getting less often married these days. Figure 1 shows the results of the marital status in the survey.


Figure 1. Results of marital status.

As can be seen Figure 1 almost 50% of the respondents is married and only a few were separated.

A chi-square test of goodness-of-fit was performed to determine whether the marital status were equally chosen. The marital status was not equally distributed in the population, χ²(4, N = 1941) = 1249.13, p < .001, with a relatively strong (Cramer's V = .40) effect size according to conventions for Cramer's V (Rea & Parker, 1992). The binomial test pairwise comparison with Bonferroni correction of marital status showed that all proportions were significantly different from each other (p < .003). The Relative Risks ranged from 1.11 (divorced vs. never married) to 1.85 (married vs. seperated).

The percentage of married people is therefor still significantly higher than any of the other categories, however this will need to be cross-checked against the age of the respondent to fully verify the media claims.

Note the final paragraph explains the some-what technical results into more understandable English, something many readers would often appreciate.

Single nominal variable

Introduction

Impression

Visualisation

Center & Dispersion

Test

Post-hoc

Effect size

Reporting

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