My dataset about students (n=74) contains one outcome variable (exam points/integer) and eight predictor variables:
- gender [F,M]
- study years [1,2,3]
6 continuous variables:
- age [in years/integer]
- work experience [in years/integer]
- technology experience [score/double]
- technology usage [score/double]
- technology success [score/integer]
- technology acceptance [score/integer])
These variables have been measured on the same students throughout the study year (some variables at the beginning, some during, and some at the end of the study year). Now I want to check the relationships between these variables, especially with regards to the outcome variable.
It was recommended to go ahead (thanks @COOLSerdash) with a linear mixed model (R package: lme4). So I have been digging into linear mixed models, and I am struggling a bit with crossed vs. nested random effects. As I am understanding it now, my data should be modeled as crossed and nested. Currently, my dataset is as follows:
74 students have each a single response to 8 variables (gender, study years, age, work experience, technology experience, technology usage, technology success, technology acceptance), thus I would follow a crossed design as responses are clustered within students:
(1|gender/study years/age/work experience/technology experience/technology usage/technology success/technology acceptance)
Question 1): Does it make sense to add demographic details (gender, age, study years, work experience) following a crossed design? Where to best account for effects of gender, age, study years, work experience, technology experience?
- But there is also a nesting of the data, such as: female students
(group 1) nest into female students with low technology usage (group
2) thus I would model
(1|group1)+(1|group2). However, then I could have many nestings (female: low usage, mid usage, high usage; female: low technology acceptance, mid technology acceptance, high technology acceptance).
Question 2): What do I have to put in my model to account for the 8 variables that is the effect of gender, study years, age, work experience, technology experience, technology usage, technology success and technology acceptance on the exam performance (outcome variable)?