Dataset Information

Flexible models of change: using structural equations to match statistical and theoretical models of multiple change processes.

ABSTRACT:

Objective

To introduce and illustrate recent advances in statistical approaches to simultaneous modeling of multiple change processes.

Methods

Provide a general overview of how to use structural equations to simultaneously model multiple change processes and illustrate the use of a theoretical model of change to guide selection of an appropriate specification from competing alternatives. The selected latent change score model is then fit to data collected during an adolescent weight-control treatment trial.

Results

A latent change score model is built starting with the foundation of repeated-measures analysis of variance and illustrated using graphical notation.

Conclusions

The assumptions behind using structural equations to model change are discussed as well as limitations of the approach. Practical guidance is provided on matching the statistical model to the theory underlying the observed change processes and the research question(s) being answered by the analyses.

SUBMITTER: Barker DH

PROVIDER: S-EPMC3936260 | biostudies-literature | 2014 Mar

REPOSITORIES: biostudies-literature

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Publications

Flexible models of change: using structural equations to match statistical and theoretical models of multiple change processes.

Barker David H DH Rancourt Diana D Jelalian Elissa E

Journal of pediatric psychology 20131104 2

<h4>Objective</h4>To introduce and illustrate recent advances in statistical approaches to simultaneous modeling of multiple change processes.<h4>Methods</h4>Provide a general overview of how to use structural equations to simultaneously model multiple change processes and illustrate the use of a theoretical model of change to guide selection of an appropriate specification from competing alternatives. The selected latent change score model is then fit to data collected during an adolescent weig ...[more]

PMID: 24190912

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Project description:BackgroundIn the field of implementation research, there is an increased interest in use of theory when designing implementation research studies involving behavior change. In 2003, we initiated a series of five studies to establish a scientific rationale for interventions to translate research findings into clinical practice by exploring the performance of a number of different, commonly used, overlapping behavioral theories and models. We reflect on the strengths and weaknesses of the methods, the performance of the theories, and consider where these methods sit alongside the range of methods for studying healthcare professional behavior change.MethodsThese were five studies of the theory-based cognitions and clinical behaviors (taking dental radiographs, performing dental restorations, placing fissure sealants, managing upper respiratory tract infections without prescribing antibiotics, managing low back pain without ordering lumbar spine x-rays) of random samples of primary care dentists and physicians. Measures were derived for the explanatory theoretical constructs in the Theory of Planned Behavior (TPB), Social Cognitive Theory (SCT), and Illness Representations specified by the Common Sense Self Regulation Model (CSSRM). We constructed self-report measures of two constructs from Learning Theory (LT), a measure of Implementation Intentions (II), and the Precaution Adoption Process. We collected data on theory-based cognitions (explanatory measures) and two interim outcome measures (stated behavioral intention and simulated behavior) by postal questionnaire survey during the 12-month period to which objective measures of behavior (collected from routine administrative sources) were related. Planned analyses explored the predictive value of theories in explaining variance in intention, behavioral simulation and behavior.ResultsResponse rates across the five surveys ranged from 21% to 48%; we achieved the target sample size for three of the five surveys. For the predictor variables, the mean construct scores were above the mid-point on the scale with median values across the five behaviors generally being above four out of seven and the range being from 1.53 to 6.01. Across all of the theories, the highest proportion of the variance explained was always for intention and the lowest was for behavior. The Knowledge-Attitudes-Behavior Model performed poorly across all behaviors and dependent variables; CSSRM also performed poorly. For TPB, SCT, II, and LT across the five behaviors, we predicted median R2 of 25% to 42.6% for intention, 6.2% to 16% for behavioral simulation, and 2.4% to 6.3% for behavior.ConclusionsWe operationalized multiple theories measuring across five behaviors. Continuing challenges that emerge from our work are: better specification of behaviors, better operationalization of theories; how best to appropriately extend the range of theories; further assessment of the value of theories in different settings and groups; exploring the implications of these methods for the management of chronic diseases; and moving to experimental designs to allow an understanding of behavior change.

Project description:We developed a model for estimating demographic rates and population abundance based on multiple data sets revealing information about population age- and sex structure. Such models have previously been described in the literature as change-in-ratio models, but we extend the applicability of the models by i) using time series data allowing the full temporal dynamics to be modelled, by ii) casting the model in an explicit hierarchical modelling framework, and by iii) estimating parameters based on Bayesian inference. Based on sensitivity analyses we conclude that the approach developed here is able to obtain estimates of demographic rate with high precision whenever unbiased data of population structure are available. Our simulations revealed that this was true also when data on population abundance are not available or not included in the modelling framework. Nevertheless, when data on population structure are biased due to different observability of different age- and sex categories this will affect estimates of all demographic rates. Estimates of population size is particularly sensitive to such biases, whereas demographic rates can be relatively precisely estimated even with biased observation data as long as the bias is not severe. We then use the models to estimate demographic rates and population abundance for two Norwegian reindeer (Rangifer tarandus) populations where age-sex data were available for all harvested animals, and where population structure surveys were carried out in early summer (after calving) and late fall (after hunting season), and population size is counted in winter. We found that demographic rates were similar regardless whether we include population count data in the modelling, but that the estimated population size is affected by this decision. This suggest that monitoring programs that focus on population age- and sex structure will benefit from collecting additional data that allow estimation of observability for different age- and sex classes. In addition, our sensitivity analysis suggests that focusing monitoring towards changes in demographic rates might be more feasible than monitoring abundance in many situations where data on population age- and sex structure can be collected.

Dataset Information

Flexible models of change: using structural equations to match statistical and theoretical models of multiple change processes.

Objective

Methods

Results

Conclusions

Publications

Flexible models of change: using structural equations to match statistical and theoretical models of multiple change processes.

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