Notes to researchers

SCORING: I use expert scoring key because it is easier to theoretically justify. The Manual suggests general consensus.

RESEARCH USE: Click HERE for info.

BIAS SCORE: I’ve not seen researchers use this score and I am very interested in seeing people use it. The variable name is SS_PosNeg.

RELIABILITY: Several researchers have reported obtaining lower reliabilities for MSCEIT ability and task scores than expected.  We have found a few procedural problems that have resulted in these findings.  First, as we note, while task reliabilities are computed with coefficient alpha, the four ability scores and total score are computed using split-half reliabilities, not alphas, as there is a great deal of item heterogeneity.  Second, some researchers have not used the Spearman-Brown correction.  Third, if you use SPSS and other stat packages, these procedures take the first half of items and compare them against the second half.  This is not going to work for the MSCEIT, since there are several different item types.  We used an odd-even split half method — which may require you to compute split-halves without use of the SPSS Reliability function. Fourth, 19 items are deleted from MSCEIT scores and you should not include these in your reliability analyses! We recently re-analyzed our data, wondering whether we had a problem, such as mis-coding the SPSS analysis file. We found the same results — pretty decent reliability. We wonder whether others’ results are due to less range (our normative sample was fairly representative) and smaller sample size (we had 5,000 people).

How To Compute MSCEIT Reliability – Use the method below to compute reliability for Total, Area (Experiential and Strategic), and Branch (Perceiving, Using, Understanding, Managing) level scores.

Example for Perceiving Emotions

Step 1: Make a composite (additive) score of all the odd items for Faces and Pictures together
Step 2: Make a composite (additive) score of all the even items for Faces and Pictures together
Step 3.  Intercorrelate the two composite scores.  This represents the reliability of a scale of half the length as the actual scale.
Step 4.  Apply the Spearman Brown correction to obtain the estimated reliability. The Spearman Brown correction states that: The reliability for the scale under study = two times the reliability of the half scale (which is represented by the intercorrelation of the two composite scales, as calculated above), divided by 1 plus the reliability of the half scale.