Question
First, participants were asked to rate a series of different words on their meaningfulness or pleasantness. Scores for both questionnaires were rated on a Likert
First, participants were asked to rate a series of different words on their meaningfulness or pleasantness. Scores for both questionnaires were rated on a Likert scale from 1 (not meaningful, not pleasant) to 5 (very meaningful, very pleasant). Once the ratings were obtained, the researchers grouped these words into sets based on previous research. This produced four sets of words: Education words, Goal words, Noun words, and Religion words. The data set provided contains the average ratings for the words by set. These same participants then completed a "purpose in life questionnaire" (PIL), where the scores on questions were totaled for each participant.
IV:
- Control variables: Age, gender (1=female, 2=male). Gender is nominal here.
- Experimental manipulation: priming type (1=meaningful, 2=pleasantness). Priming type is nominal here.
- Education words averaged (i.e., accomplish, College, Degree, Education, Grades, Graduate, School, Teacher, Undergrad, University)
- Goals words averaged (i.e., achieve, ambition, become, goals, progress, success)
- Nouns words averaged (i.e., everything, know, lot, many, mind, much, right, some, something, thing, time, what, when)
- Religion words averaged (i.e., serve, glorify)
- Nominal variables can be placed in covariate box but only factor box in JASP
DV: PIL total - sum of scores on the purpose in life questionnaire
Research Question:We would like to test whether word ratings predict scores on the PIL questionnaire, above and beyond control variables and experimental manipulation. HINT: To test this, you will need a regression model that includes demographics and priming type (the null model) as the sole predictors, as well as a second regression model that includes actual predictor variables in addition to the controls (the regression model).
What are the steps for this when it comes to multiple linear regression (simple steps):
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