Table 3 Recommended procedures

Recommended procedures:

• Merge raw data from multiple sources with minimal pre-processing;

• Check whether item responses are comparable across sources;

• Clean data to establish item comparability:

○ Ensure constant directionality/polarity:

■ Review content and response options;

■ Run correlation matrices, flag items with sizable negative correlations;

■ Reverse code as necessary.

○ Ensure consistency in scoring type and scales:

■ Review response options;

■ Cross-tabulate items across datasets to evaluate whether items have different minimum and maximum values by dataset;

■ Exclude summary scores and counts in favor of more granular data;

■ Truncate, collapse response categories as necessary.

○ Eliminate conditional dependency:

■ Review content and logic flows;

■ Perform parametric modeling, scrutinize output for residuals;

■ Exclude conditional items.

○ Address missingness/skewness:

■Tabulate frequency of each item being endorsed;

■ Filter out items with coded missingness;

■ Filter out items with same min and max within a dataset;

■ Truncate, collapse response categories as necessary;

■Exclude items with no variability.

• Establish configural invariance:

○ Estimate parametric models within each dataset;

○ Scrutinize output for residuals;

○ Include residual covariances for items having high covariance residuals.