Many important psychological phenomena (e.g., moods) appear to be influenced both by an individual's chronic level (trait) as well as temporary fluctuations from that chronic level (state). Latent trait-state models (Steyer, Ferring, & Schmitt, 1992; Steyer, Schmitt, & Eid, 1999; see Figure 21.2) partition each measure collected at each measurement occasion into three components. First is a component that represents the trait construct measured at a specific time point (denoted Time 1, Time 2, and Time 3 in Figure 21.2). This component is further partitioned into (a) a latent trait factor that characterizes the person's stable general level on the construct of conscientiousness (denoted as Consci in Figure 21.2) and (b) a latent state residual that characterizes temporary (state) effects on the person associated with each measurement wave. Second, the method factor represents the stable influence of the specific measure (here, the measure of each facet of conscientiousness, denoted Order, Decis, Reliab, Indust, respectively). Third, as in previous models, another component reflects random measurement error.

The latent state-trait model shows a good fit to the conscientiousness data, ^2(39) = 31.87, ns, RMSEA = .00). The clear partitioning of the observed scores on the measure into trait, state, measure, and error variance components provides a strong basis for predicting external criteria. For example, the relatively pure measure of the trait of conscientiousness that is estimated can be used to predict conscientiousness-related behaviors such as class attendance or worker productivity. The latent trait-state model can also partition the total amount of variance in the observed scores into trait, state, measurement method, and error variance components (see Steyer et al., 1992). In the present example, 42% of the variance in the observed scores is associated with the stable latent trait factor for conscientiousness.2 Or, if the researcher were interested in situational effects on conscientiousness (e.g., if midterm exams were given prior to the Week 2 measurement), the proportion of the total variance

2The instructions emphasized answering based only on the past week's behaviors.

in the observed scores associated with the latent state residuals could be computed. Steyer et al. (1992) discussed a variety of potential methods of partitioning the variance to produce estimates of several diverse forms of reliability and stability that may be useful in different research contexts. Steyer et al. (1992) and Kenny and Zautra (2001) compared several variants of the latent trait-state model.

Although the basic latent trait-state model has several important strengths, it also has three limita tions. First, like the autoregressive model, the basic state-trait model focuses only on the relative ordering of a set of individuals. Clear interpretation of findings requires there is not systematic growth or decline for each individual over time. Otherwise, more complex models that combine growth and trait-state components are required (Tisak & Tisak, 2002). Second,- the temporal ordering of the observations is not represented in the analysis. Otherwise stated, the data from any two time periods (e.g., 2 and 3) can be exchanged without affecting the fit or any important features of the model. Third, like multitrait-multimethod models (Eid, 2000; Kenny & Kashy, 1992), latent trait-state models can be difficult to fit with many data sets. Data sets with small state components or small method components can lead to improper solutions. In general, adding more time periods, more measures, and more participants appears to improve estimation. Steyer et al. (1999) present approaches that may be used when there are problems in estimation.

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