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A longitudinal multilevel CFA-MTMM model for interchangeable and structurally different methods.

ABSTRACT: One of the key interests in the social sciences is the investigation of change and stability of a given attribute. Although numerous models have been proposed in the past for analyzing longitudinal data including multilevel and/or latent variable modeling approaches, only few modeling approaches have been developed for studying the construct validity in longitudinal multitrait-multimethod (MTMM) measurement designs. The aim of the present study was to extend the spectrum of current longitudinal modeling approaches for MTMM analysis. Specifically, a new longitudinal multilevel CFA-MTMM model for measurement designs with structurally different and interchangeable methods (called Latent-State-Combination-Of-Methods model, LS-COM) is presented. Interchangeable methods are methods that are randomly sampled from a set of equivalent methods (e.g., multiple student ratings for teaching quality), whereas structurally different methods are methods that cannot be easily replaced by one another (e.g., teacher, self-ratings, principle ratings). Results of a simulation study indicate that the parameters and standard errors in the LS-COM model are well recovered even in conditions with only five observations per estimated model parameter. The advantages and limitations of the LS-COM model relative to other longitudinal MTMM modeling approaches are discussed.

SUBMITTER: Koch T

PROVIDER: S-EPMC4029009 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:IntroductionThe heart rate (HR) method is a promising approach for evaluating oxygen uptake ([Formula: see text]), energy demands and exercise intensities in different forms of physical activities. It would be valuable if the HR method, established on ergometer cycling, is interchangeable with other regular activities, such as level walking. This study therefore aimed to examine the interchangeability of the HR method when estimating [Formula: see text] for ergometer cycling and level treadmill walking in submaximal conditions.MethodsTwo models of [Formula: see text] regression equations for cycle ergometer exercise (CEE) and treadmill exercise (TE) were established with 34 active commuters. Model 1 consisted of three submaximal intensities of ergometer cycling or level walking, model 2 included also one additional workload of maximal ergometer cycling or running. The regression equations were used for estimating [Formula: see text] with seven individual HR values based on 25-85% of HR reserve (HRR). The [Formula: see text] estimations were compared between CEE and TE, within and between each model.ResultsOnly minor, and in most cases non-significant, average differences were observed when comparing the estimated [Formula: see text] levels between CEE and TE. Model 1 ranged from -0.4 to 4.8% (n.s.) between 25-85%HRR. In model 2, the differences between 25-65%HRR ranged from 1.3 to -2.7% (n.s.). At the two highest intensities, 75 and 85%HRR, [Formula: see text] was slightly lower (3.7%, 4.4%; P < 0.05), for CEE than TE. The inclusion of maximal exercise in the [Formula: see text] relationships reduced the individual [Formula: see text] variations between the two exercise modalities.ConclusionThe HR methods, based on submaximal ergometer cycling and level walking, are interchangeable for estimating mean [Formula: see text] levels between 25-85% of HRR. Essentially, the same applies when adding maximal exercise in the [Formula: see text] relationships. The inter-individual [Formula: see text] variation between ergometer cycling and treadmill exercise is reduced when using the HR method based on both submaximal and maximal workloads.

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A longitudinal multilevel CFA-MTMM model for interchangeable and structurally different methods.

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