Linear Mixed Model for evaluation of students

manpreet Best Answer 2 years ago

My dataset about students (n=74) contains one outcome variable (exam points/integer) and eight predictor variables:

2 categorical:

• 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)?