Using structural equation modeling to detect response shifts and true change

Oort, Frans J.

doi:10.1007/s11136-004-0830-y

Cited by 228 publications

(401 citation statements)

References 17 publications

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“…In line with previous papers (Fokkema, Smits, Kelderman, & Cuijpers, 2013;Oort, 2005;Vandenberg & Lance, 2000), we therefore allowed the residuals of each item to be correlated across time. All structural equation models (EFA, CFA, and ESEM) and tests of measurement invariance were estimated in Mplus 7.3 (Muthén & Muthén, 2012); all other analyses were conducted in R 3.1 (R Development Core Team, 2008).…”

Section: Discussionmentioning

confidence: 99%

Measuring depression over time . . . Or not? Lack of unidimensionality and longitudinal measurement invariance in four common rating scales of depression.

Fried

Borkulo

Epskamp

et al. 2016

Psychological Assessment

253

241

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In depression research, symptoms are routinely assessed via rating scales and added to construct sum-scores. These scores are used as a proxy for depression severity in cross-sectional research, and differences in sum-scores over time are taken to reflect changes in an underlying depression construct. To allow for such interpretations, rating scales must (a) measure a single construct, and (b) measure that construct in the same way across time. These requirements are referred to as unidimensionality and measurement invariance. We investigated these 2 requirements in 2 large prospective studies (combined n = 3,509) in which overall depression levels decrease, examining 4 common depression rating scales (1 self-report, 3 clinician-report) with different time intervals between assessments (between 6 weeks and 2 years). A consistent pattern of results emerged. For all instruments, neither unidimensionality nor measurement invariance appeared remotely tenable. At least 3 factors were required to describe each scale, and the factor structure changed over time. Typically, the structure became less multifactorial as depression severity decreased (without however reaching unidimensionality). The decrease in the sum-scores was accompanied by an increase in the variances of the sum-scores, and increases in internal consistency. These findings challenge the common interpretation of sum-scores and their changes as reflecting 1 underlying construct. The violations of common measurement requirements are sufficiently severe to suggest alternative interpretations of depression sum-scores as formative instead of reflective measures. We discuss the possible causes of these violations such as response shift bias, restriction of range, and regression to the mean.

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Section: Discussionmentioning

confidence: 99%

Measuring depression over time . . . Or not? Lack of unidimensionality and longitudinal measurement invariance in four common rating scales of depression.

Fried

Borkulo

Epskamp

et al. 2016

Psychological Assessment

253

241

View full text Add to dashboard Cite

show abstract

“…1) (Oort 2005). The hypothesis is tested by estimating the same model for each group simultaneously while allowing estimated parameters to differ.…”

Section: Measurement and Structural Invariancementioning

confidence: 99%

“…Measurement and structural invariance of the model can be interpreted using the response shift theory (Oort 2005;Sass 2011;de Beurs et al 2015). The response shift is defined as: ''a change in the meaning of one's self-evaluation of a target construct as a result of (a) a change in the respondent's internal standards of measurement (i.e., scale recalibration); (b) a change in the respondent's values (i.e., the importance of component domains constituting the target construct through reprioritization) or (c) a redefinition of the target construct (i.e., reconceptualization)'' (Schwartz and Sprangers 1999).…”

Section: Measurement and Structural Invariancementioning

confidence: 99%

Latent factor analysis for synthesized speech quality-of-experience assessment

Gupta

Falk

2017

Qual User Exp

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Text-to-speech (TTS) systems are evolving and making way into numerous commercial systems, such as smartphones and assistive technologies. Notwithstanding, their user perceived quality-of-experience (QoE) is still low compared to natural speech, with distortions arising across numerous perceptual dimensions, such as voice pleasantness, comprehension, and appropriateness of intonation, to name a few. Unfortunately, the effects of such perceptual dimensions on overall perceived QoE is still unknown, particularly across listeners of different genders, thus making it difficult for TTS developers to further improve system quality. To overcome this limitation, this study makes use of exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and model invariance tests to shed light on factors responsible for QoE perception across natural and synthesized speech, as well as male and female listeners. Experimental EFA/CFA results on a publicly available database of commercial TTS systems showed the emergence of two key perceptual dimensions responsible for TTS QoE, namely 'listening pleasure' and 'prosody'. Model invariance tests validated the reliability of the model across male and female listeners, as well as across natural and synthetic voices.

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“…One of the most frequently applied methods for detecting response shift over the past decade, the Oort SEM approach [5,12], has the advantage of being codified and interpretable using available software. In this special section, three research teams apply and demonstrate the application of the Oort SEM at both the item and subscale levels in distinct patient populations.…”

Section: Item-level Studies Using the Oort Sem Approachmentioning

confidence: 99%

“…We present the results of this survey briefly below, followed by a short introduction to the five papers included in this special section. This special section can be divided into method-based papers that utilize one of three response shift detection methods: (1) the retrospective pretest (i.e., then-test) [1][2][3][4], (2) the Oort Structural Equation Modeling (SEM) approach [5], and (3) the RespOnse Shift ALgorithm in Item response theory (ROSALI) [6].…”

mentioning

confidence: 99%

Introduction to special section on response shift at the item level

Schwartz

2016

Qual Life Res

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Using structural equation modeling to detect response shifts and true change

Cited by 228 publications

References 17 publications

Measuring depression over time . . . Or not? Lack of unidimensionality and longitudinal measurement invariance in four common rating scales of depression.

Measuring depression over time . . . Or not? Lack of unidimensionality and longitudinal measurement invariance in four common rating scales of depression.

Latent factor analysis for synthesized speech quality-of-experience assessment

Introduction to special section on response shift at the item level

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