of most social-personality scales/measures have reported estimates of ‘internal consistency’ such as alpha coeffi-
cients, we include these details in the chapters that follow.
Reliability of scores over time is best examined through test /C0retest methods, in terms of both the immediate
test/C0retest (dependability) coefficients and longer-term test /C0retest (stability) coefficients over days, weeks,
months, years, etc. (cf. Cattell, 1973 , pp. 352 /C0356). It would be expected that for both state and trait measures,
dependability coefficients would be high (say 0.8 or 0.9), that longer-term stability coefficients would remain rela-
tively high for trait measures (say 0.7 to 0.8), but would be considerably lower for state measures (say 0.3 or 0.4),
if they are truly sensitive to situational variability ( Boyle, 1985, 2008 ). To take just one illustrative example,
Borteyrou et al. (2008) , reported that for the French adaptation of Spielberger’s STAXI-2, over a two-month
test/C0retest interval ( N5139), stability coefficients were found to be .70 for the trait anger scale and .32 for the
state anger scale, respectively. Unlike more enduring trait dispositions, the scores on transient state constructs
are expected to vary across time due to contextual influences such as environmental stressors and circadian
rhythms ( Matthews et al., 2002 ). Thus, one must be careful not to expect high stability coefficients when they are
not theoretically or clinically relevant (such as for emotional state and mood state measures).
Standardization
Standardization is important for measures that will be used across persons so that the administration instruc-
tions, content format, and scoring procedures are predetermined and identical no matter who administers and/
or undertakes the scoring. Today, computerized scoring of many personality measures is readily available,
thereby standardizing scoring procedures and removing the possibility of scoring errors. When such measures
are normed, each person’s raw score is interpreted in relation to the most relevant normative group. The same
point applies to criterion referenced measures whereby an individual’s score is compared with a preset listing of
performance criteria that may be dichotomous (‘met or exceeded’ vs. ‘did not meet’). For example, emotional
intelligence (EI) measures based on an ability model most often assign meaning or at least address the question
of ‘how much’ of the characteristic a person has by a comparison with subjective ‘expert’ definitions of emotional
competency. Trait EI measures, on the other hand, rely on normative data from large groups or standardization
samples as the basis for individual score interpretation. Thus, methods for standardizing a scale/measure may
also impact on the interpretation of obtained scores.
Validity
The validity of a scale/measure and the test scores as they relate to meaningful information about the person
have traditionally been bound to content, criterion and construct validity. While classical test theory views of
validity have been criticized for being both limiting and not focusing on the validity of an individual’s scores,
these components still form much of the method of validating tests. Messick (1980, 1995) extended the view of
validity to include multitrait /C0multimethod comparisons as well as the social consequences of assessment. Given
the sophistication of current statistical techniques, validity can now be assessed beyond more static convergent
and discriminant validity coefficients. Both exploratory (EFA) and confirmatory (CFA) factor analysis, measure-
ment invariance statistics and especially structural equation modeling (SEM) have expanded the way a measure
of a construct or latent trait is understood (e.g., Cattell, 1978; Child, 2006; Comrey & Lee, 1992; Cuttance & Ecob,
1987; Gorsuch, 1983; Loehlin, 1998; McDonald, 1985 ). SEM has moved us from examining simple correlation pat-
terns between a predictor and criterion measure or from descriptions of static personality factors (e.g., FFM /C0see
Cattell, Boyle, & Chant, 2002 ), to testing competing structural models of trait constructs in dynamic patterns of
cause and effect and moderating and mediating relationships with other factors. Item response theory (IRT;
Embretson & Reise, 2000 ), based on formal modeling of the influence of person and item parameters on test
responses has also become increasingly influential in assessment.
Despite the increased popularity of CFA, many scales/measures have been constructed using less than optimal
factor analytic methods ( Costello & Osborne, 2005 ). While many EFAs have been based on item intercorrelations,
such item responses are notoriously unreliable. For this reason, intercorrelations of item-parcels (Cattell), or of
Factored Homogeneous Item Dimensions or FHIDs (Comrey), have been preferred as the starting point for reli-
able factor analysis (see Cattell, 1978, 1988 ;Comrey, 2008 ;Comrey & Lee, 1992 ). The use of sound EFA methodol-
ogy remains essential in valid test construction (e.g., use of an iterative maximum-likelihood procedure with
squared multiple correlations (SMCs) as initial communality estimates and number of factors determined by an8 1. CRITERIA FOR SELECTION AND EVALUATION OF SCALES AND MEASURES
I. INTRODUCTION: CORE ISSUES IN ASSESSMENT