I generated a random-effects logistic regression model and included within-person predictors by person-mean-centering.

Not all individuals in my dataset have multiple data points, i.e. the individuals gave interviews, and while a large number of them did multiple interviews, many only did one interview.

My research question is whether interviewer characteristics affect how interviewers measure respondent characteristics. To answer the question, I analyze the within-person effects of interviewer characteristics.

**Now, my question is:**

Who (i.e. which interview respondents) are

*included in this analysis?*

**not**1. Obviously, respondents who only did one interview are not included, since there are literally no within-person comparisons to make.

2. Next, Williams (2018) (see link below) states that "for all subjects where the dependent variable [i.e. measured respondent characteristics) is a constant ... the case is dropped from the statistical analysis." This make sense.

**3.**However, what about the independent variable being constant, i.e. what about about respondents who were interviewed multiple times by interviewers whose characteristics are constant across all interviews? My intuition tells me that these individuals would also be excluded from the analysis, but I cannot fully wrap my head around it and I couldn't find any literature on that.

Any input would be greatly appreciated!

**Link to William (2018):**https://www.google.com/url?sa=t&rct...VsRandom.pdf&usg=AOvVaw2Uq3PAcGrUIML-wmaaA8tL