Dataset Information

Modeling Measurement as a Sequential Process: Autoregressive Confirmatory Factor Analysis (AR-CFA).

ABSTRACT: To model data from multi-item scales, many researchers default to a confirmatory factor analysis (CFA) approach that restricts cross-loadings and residual correlations to zero. This often leads to problems of measurement-model misfit while also ignoring theoretically relevant alternatives. Existing research mostly offers solutions by relaxing assumptions about cross-loadings and allowing residual correlations. However, such approaches are critiqued as being weak on theory and/or indicative of problematic measurement scales. We offer a theoretically-grounded alternative to modeling survey data called an autoregressive confirmatory factor analysis (AR-CFA), which is motivated by recognizing that responding to survey items is a sequential process that may create temporal dependencies among scale items. We compare an AR-CFA to other common approaches using a sample of 8,569 people measured along five common personality factors, showing how the AR-CFA can improve model fit and offer evidence of increased construct validity. We then introduce methods for testing AR-CFA hypotheses, including cross-level moderation effects using latent interactions among stable factors and time-varying residuals. We recommend considering the AR-CFA as a useful complement to other existing approaches and treat AR-CFA limitations.

SUBMITTER: Ozkok O

PROVIDER: S-EPMC6763968 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Modeling Measurement as a Sequential Process: Autoregressive Confirmatory Factor Analysis (AR-CFA).

Ozkok Ozlem O Zyphur Michael J MJ Barsky Adam P AP Theilacker Max M Donnellan M Brent MB Oswald Frederick L FL

Frontiers in psychology 20190920

To model data from multi-item scales, many researchers default to a confirmatory factor analysis (CFA) approach that restricts cross-loadings and residual correlations to zero. This often leads to problems of measurement-model misfit while also ignoring theoretically relevant alternatives. Existing research mostly offers solutions by relaxing assumptions about cross-loadings and allowing residual correlations. However, such approaches are critiqued as being weak on theory and/or indicative of pr ...[more]

PMID: 31616338

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Project description:Reading is a complex cognitive task with the ultimate goal of comprehending the written input. For longer, connected text, readers generate a mental representation that serves as its basis. Due to limited cognitive resources, common models of discourse representation assume distinct processing levels, each relying on different processing mechanisms. However, only little research addresses distinct representational levels when text comprehension is assessed, analyzed or modelled. Moreover, current studies that tried to relate process measures of reading (e.g., reading times, eye movements) to comprehension did not consider comprehension as a multi-faceted, but rather a uni-dimensional construct, usually assessed with one-shot items. Thus, the first aim of this paper is to use confirmatory factor analysis (CFA) to test whether comprehension can be modelled as a uni-or multi-dimensional concept. The second aim is to investigate how well widely used one-shot items can be used to capture comprehension. 400 participants read one of three short stories of comparable length, linguistic characteristics, and complexity. Based on the evaluation of three independent raters per story, 16 wh-questions and 60 yes/no-statements were compiled in order to retrieve information at micro and inference level, and 16 main contents were extracted to capture information at the macro level in participants' summaries. Still, only a fraction of these items showed satisfactory psychometric properties and factor loadings - a blatant result considering the common practice for item selection. For CFA, two models were set up that address text comprehension as either a one-dimensional construct (a uni-factor model with a single comprehension factor), or a three-dimensional construct reflecting the three distinct representational levels (three correlated first-order factors). Across stories and item types, model fit was consistently better for the three-factor model providing evidence for a multi-dimensional construct of text comprehension. Our results provide concrete guidance for the preparation of comprehension measurements in studies investigating the reading process.

Project description:Background. A portion of the Functional Assessment of Cancer Therapy-Lung (FACT-L) instrument contributed to a previously published utility index, the FACT Lung Utility Index or FACT-LUI. Six FACT items representing lung cancer quality of life covered fatigue, pain, dyspnea, cough, anxiety, and depression. Two FACT items had been previously combined by the index authors into one for nausea and/or appetite loss, resulting in 7 final domains. Methods. The objective was to perform measurement invariance testing within a confirmatory factor analysis (CFA) framework to support the feasibility of using the FACT-LUI for non-preference-based psychometric applications. The original index patients comprised group 1, and similar FACT patient data (n = 249) from another published study comprised group 2. One 2-factor model and two 1-factor CFA models were evaluated to assess measurement invariance across groups, using varying degrees of item parceling and a small number of residual covariances, all justified by the literature. Results. The 1-factor models were most optimal. A 1-factor model with 1 pair of items parceled showed invariance to the partial scalar level using usual fit criteria across groups, requiring 2 unconstrained intercepts. A 1-factor model with 3 pairs of justified parcels showed full configural, metric, and scalar invariance across groups. Conclusions. The FACT-LUI items fit a partially to fully invariant 1-factor model, suggesting feasibility for non-preference-based applications. Implications. Results suggest useful incorporation of the FACT-LUI into clinical trials with no substantial increased respondent burden, allowing preference-based and other psychometric applications from the same index items.HighlightsThis work suggests that in addition to being originally designed for use as a utility index, the 7 FACT-LUI items together also fit simple CFA and measurement invariance models. This less expected result indicates that these items as a group are also potentially useful in non-preference-based applications.Clinical trials can make for challenging decisions concerning which patient-reported outcome measures to include without being burdensome. However, the literature suggests a need for improved reporting of quality of life in lung cancer in particular as well as cancer in general. Inclusion of more disease-specific items such as the FACT-LUI may allow for information gathering of both preference-based and non-preference-based data with less demand on patients, similar to what has been done with some generic instruments.

Dataset Information

Modeling Measurement as a Sequential Process: Autoregressive Confirmatory Factor Analysis (AR-CFA).

Publications

Modeling Measurement as a Sequential Process: Autoregressive Confirmatory Factor Analysis (AR-CFA).

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