P300 amplitude will be larger in the intervention group than in the control group
The effects of our intervention on belonging will be stronger among those with greater sense of purpose
Students' ratings of well-being at the end of the semester will be higher than their ratings of well-being at the beginning of the semester
Participants who perceive authenticity in hookup culture will report more relationship satisfaction, even after controlling for overall life satisfaction
People who rate ChatGPT as easy to use are more likely to rate it as useful
Think about your hypotheses
Some hypotheses require multivariate statistics. These include hypotheses involving...
Multiple dependent variables
Multiple relationships among different variables
Dimensions underlying the variables in your dataset
Clusters of people in your dataset
Some hypotheses require statistics that aren't multivariate, per se, but you haven't learned about them yet. This includes hypotheses involving...
Categorical dependent variables
"Nested" observations
We'll get into what this means later
Other hypotheses you can test
Example: We have multiple measures of academic and social functioning among college students - e.g., belonging, academic self-concept, school connectedness. We hypothesize that all of these, in the aggregate, will be higher among those in our intervention group than those in a control group.
Analysis: MANOVA
We typically are not going to use MANOVA, because we will often have hypotheses about how these variables relate to one another. But you will see them sometimes and you should know how to interpret them!
Multiple dependent variables
Example: We predict that students who perceive authenticity in hookup culture will choose partners whose values are similar to their own and who are high in honesty. This partner choice will lead to high relationship satisfaction.
Analysis: Structural equation modeling
Relationships among variables
Example: We administer 10 different items about well-being to a group of college students. We find that their responses reflect 3 broad dimensions: academic confidence, social connectedness, and sense of purpose
Analysis: Factor analysis (exploratory or confirmatory)
Dimensions underlying your variables
Example: We administer 10 different items about ChatGPT to our participants. We predict that there will be 3 groups of people in our dataset: people who are generally wary of ChatGPT (40%), people who are generally excited about ChatGPT (40%), and people who feel excited about ChatGPT's usefulness but wary of generative AI's potential misuse (20%)
Analysis: Mixture modeling
Clusters of people in your dataset
Example: We are interested in whether ADHD diagnosis is predicted by P300 amplitude, even after controlling for a variety of other physiological variables.
Analysis: Logistic regression
Categorical dependent variables
Example: We predict that the relationship between daily ratins of well-being and belonging will strengthen over the course of the semester
Analysis: Multilevel modeling
Nested dependent variables
Example: We predict that the relationship between sense of purpose and belonging will be stronger among students in schools with higher overall levels of achievement.