Hello,
I’m analyzing how multiple emojis change the perception of messages. In the questionnaire, the participants had to rate neutral sentences and the same neutral sentences enriched with one positive, negative or neutral emoji or the same emoji twice or three times on a 7-point Likert-scale from very negative to very positive (DV). The IV in this case is the number of emojis used in the sentences (0, 1, 2 and 3). I also want to control, how adding emojis to the sentences influence the perception of the sentences for gender and digital generations.
To analyze the effect, I conducted an ordinal logistic regression, because the DV is ordinal. I also split the IV, to calculate different models with the IV’s 0 or 1 emoji, 1 or 2 emojis and 2 or 3 emojis. Now I want to control this effect for gender and digital generations.
- Is there another way than splitting the data set e.g. in different genders and calculating the regression again for different data sets, one containing only men, one only women and one only other genders?
- Is there another method than ordinal logistic regression, to check whether the effects derived from the collected data are significant?
I'm not sure I understand the question. Why not just add gender or other variables as predictors? Doing subgroup analyses is generally a bad idea because an effect in one subgroup and no significant effect in another doesn't imply that the effect is different in the two subgroups. For example it could be near identical in both subgroups and p < .05 in one and p > .05 because of different sample sizes or different degrees of collinearity with other predictors. To test if the effect is bigger for males than females you'd want to include interaction effects - though this can be difficult to interpret - especially in an ordinal logistic regression. Always get a plot of the predicted probabilities for the ranges of predictors of interest to aid interpretation. (The predicted probabilities are not a linear function of the coefficients so you can get potentially surprising patterns depending on the ranges of predicted probabilities).
Thom Baguley thanks for your answer! I did not add gender or generation as an IV, because I don't want to check their influence on the sentiment. I only want to control how they influence the effect of the number of emojis on the sentiment. Do you have an idea, besides calculating interaction effects, how I can control the influence of gender or generation on the effect of the number of emojis on the sentiment?
I don't like the word "control" here. While it is the word that we were taught, including a variable in a regression does not in any real sense control it. But, back to your question. Thom Baguley 's advise allows both of these to be looked at. Frauke Neugebauer , any reason why you don't want to estimate interaction effects?
Daniel Wright I am not familiar with the interpretation of interaction effects in ordinal logistic regressions and I've heard that it isn't that easy. Because of that, I'm afraid to misinterpret the estimates.
Thom Baguley 's advice to plot the predicted probabilities will help you interpreting interaction.
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