Is This Grouping OK in SPSS? Best Practices for Analyzing Human Sexual Selection Data

When conducting research on human sexual selection, it's crucial to carefully choose the methods and analyses to ensure accurate and reliable results. In this case, Lucas is analyzing data from 104 lonely hearts advertisements to explore the differences in preferred age of potential mates between sexes. The data, however, did not show a normal distribution, so Kruskal-Wallis test was employed for initial analysis.

Lucas then desired to delve deeper into the interaction effects between gender and age of the person placing the advertisements on the preferred age of potential mates. Here are some guidelines and considerations to help determine whether the current grouping method in SPSS is appropriate.

Overview of the Data and Initial Analysis

Lucas collected 104 advertisements and grouped them based on gender and the age of the person placing the ad. The data was further categorized into preferred age of potential mates, where median values were calculated. Due to the abnormal distribution, Kruskal-Wallis test was performed to compare the median potential mate preferences across different gender groups. This test is non-parametric and suitable when the data does not follow a normal distribution.

Challenges with the Current Approach

The issue arises when trying to look at the interaction of gender and the age of the person placing the ad on the preferred age of potential mates. Kruskal-Wallis test only allows for one grouping variable, making it challenging to incorporate the complexity of the interaction.

To address this, Lucas attempted to attach a gender to each age category, creating two independent factors within one column. While this approach allows for pairwise comparisons, it may not be the most efficient or accurate way to handle the interaction effects.

Alternatives and Recommendations

To properly analyze the interaction between gender and age on the preferred age of potential mates, consider the following alternatives:

1. Separate Analysis

For each age group, perform a Kruskal-Wallis test to compare gender differences in preferred age. This would allow for a more nuanced understanding of how age influences the preferences in each gender group.

This approach ensures that the analysis takes into account the interaction effects and provides a clearer understanding of the relationships in the data.

2. Non-Parametric Interaction Test

Given the non-normal distribution, non-parametric methods like Friedman#39;s test or Quade test can be suitable for analyzing the interaction between the two factors (gender and age). These tests are designed to handle such complexities without the usual assumptions of normality.

3. Mixed-Effects Models

For a more robust analysis, consider using mixed-effects models. These models can handle the repeated measures aspect of the data and allow for the inclusion of both fixed and random effects. By including age as a fixed effect and gender as a random effect, the model can account for the interaction between these factors.

Conclusion and Final Recommendations

When working with data from lonely hearts advertisements and analyzing the preferences of potential mating partners in relation to sex and age, it's essential to choose appropriate statistical methods that can handle the complexities of the research question.

Based on the current approach, it is not ideal to group two factors in the way Lucas did. Instead, consider separating the analysis by age groups and using non-parametric interaction tests or mixed-effects models to gain a clear and reliable understanding of the interaction effects.

By adopting these methods, Lucas can ensure that the analysis is both accurate and comprehensive, leading to more meaningful insights into human sexual selection.

Keywords: Kruskal-Wallis test, SPSS, sexual selection data, statistical analysis, gender interaction