Abstract
Several approaches exist to model interactions between latent variables. However, it is unclear how these perform when item scores are skewed and ordinal. Research on Type D personality serves as a good case study for that matter. In Study 1, we fitted a multivariate interaction model to predict depression and anxiety with Type D personality, operationalized as an interaction between its two subcomponents negative affectivity (NA) and social inhibition (SI). We constructed this interaction according to four approaches: (1) sum score product; (2) single product indicator; (3) matched product indicators; and (4) latent moderated structural equations (LMS). In Study 2, we compared these interaction models in a simulation study by assessing for each method the bias and precision of the estimated interaction effect under varying conditions. In Study 1, all methods showed a significant Type D effect on both depression and anxiety, although this effect diminished after including the NA and SI quadratic effects. Study 2 showed that the LMS approach performed best with respect to minimizing bias and maximizing power, even when item scores were ordinal and skewed. However, when latent traits were skewed LMS resulted in more false-positive conclusions, while the Matched PI approach adequately controlled the false-positive rate.
| Original language | English |
|---|---|
| Pages (from-to) | 637-665 |
| Journal | Multivariate Behavioral Research |
| Volume | 54 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - 2019 |
Keywords
- Latent prediction model
- structural equation modeling
- SEM
- latent interaction
- nonnormality
- Type D personality
- depression
- anxiety
- MAXIMUM-LIKELIHOOD-ESTIMATION
- COVARIANCE STRUCTURE-ANALYSIS
- CONFIRMATORY FACTOR-ANALYSIS
- CORONARY-HEART-DISEASE
- ITEM RESPONSE THEORY
- PREDICTIVE-VALUE
- PSYCHOLOGICAL DISTRESS
- CATEGORICAL VARIABLES
- NEGATIVE AFFECTIVITY
- EMPIRICAL-EVALUATION
Cite this
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Modeling interactions between latent variables in research on Type D personality : A Monte Carlo simulation and clinical study of depression and anxiety. / Lodder, Paul; Denollet, Johan; Emons, Wilco H. M.; Nefs, Giesje; Pouwer, Frans; Speight, Jane; Wicherts, Jelte M.
In: Multivariate Behavioral Research, Vol. 54, No. 5, 2019, p. 637-665.Research output: Contribution to journal › Article › Scientific › peer-review
TY - JOUR
T1 - Modeling interactions between latent variables in research on Type D personality
T2 - A Monte Carlo simulation and clinical study of depression and anxiety
AU - Lodder, Paul
AU - Denollet, Johan
AU - Emons, Wilco H. M.
AU - Nefs, Giesje
AU - Pouwer, Frans
AU - Speight, Jane
AU - Wicherts, Jelte M.
PY - 2019
Y1 - 2019
N2 - Several approaches exist to model interactions between latent variables. However, it is unclear how these perform when item scores are skewed and ordinal. Research on Type D personality serves as a good case study for that matter. In Study 1, we fitted a multivariate interaction model to predict depression and anxiety with Type D personality, operationalized as an interaction between its two subcomponents negative affectivity (NA) and social inhibition (SI). We constructed this interaction according to four approaches: (1) sum score product; (2) single product indicator; (3) matched product indicators; and (4) latent moderated structural equations (LMS). In Study 2, we compared these interaction models in a simulation study by assessing for each method the bias and precision of the estimated interaction effect under varying conditions. In Study 1, all methods showed a significant Type D effect on both depression and anxiety, although this effect diminished after including the NA and SI quadratic effects. Study 2 showed that the LMS approach performed best with respect to minimizing bias and maximizing power, even when item scores were ordinal and skewed. However, when latent traits were skewed LMS resulted in more false-positive conclusions, while the Matched PI approach adequately controlled the false-positive rate.
AB - Several approaches exist to model interactions between latent variables. However, it is unclear how these perform when item scores are skewed and ordinal. Research on Type D personality serves as a good case study for that matter. In Study 1, we fitted a multivariate interaction model to predict depression and anxiety with Type D personality, operationalized as an interaction between its two subcomponents negative affectivity (NA) and social inhibition (SI). We constructed this interaction according to four approaches: (1) sum score product; (2) single product indicator; (3) matched product indicators; and (4) latent moderated structural equations (LMS). In Study 2, we compared these interaction models in a simulation study by assessing for each method the bias and precision of the estimated interaction effect under varying conditions. In Study 1, all methods showed a significant Type D effect on both depression and anxiety, although this effect diminished after including the NA and SI quadratic effects. Study 2 showed that the LMS approach performed best with respect to minimizing bias and maximizing power, even when item scores were ordinal and skewed. However, when latent traits were skewed LMS resulted in more false-positive conclusions, while the Matched PI approach adequately controlled the false-positive rate.
KW - Latent prediction model
KW - structural equation modeling
KW - SEM
KW - latent interaction
KW - nonnormality
KW - Type D personality
KW - depression
KW - anxiety
KW - MAXIMUM-LIKELIHOOD-ESTIMATION
KW - COVARIANCE STRUCTURE-ANALYSIS
KW - CONFIRMATORY FACTOR-ANALYSIS
KW - CORONARY-HEART-DISEASE
KW - ITEM RESPONSE THEORY
KW - PREDICTIVE-VALUE
KW - PSYCHOLOGICAL DISTRESS
KW - CATEGORICAL VARIABLES
KW - NEGATIVE AFFECTIVITY
KW - EMPIRICAL-EVALUATION
UR - https://app-eu.readspeaker.com/cgi-bin/rsent?customerid=10118&lang=en_us&readclass=rs_readArea&url=https%3A%2F%2Fwww.tandfonline.com%2Fdoi%2Ffull%2F10.1080%2F00273171.2018.1562863
U2 - 10.1080/00273171.2018.1562863
DO - 10.1080/00273171.2018.1562863
M3 - Article
VL - 54
SP - 637
EP - 665
JO - Multivariate Behavioral Research
JF - Multivariate Behavioral Research
SN - 0027-3171
IS - 5
ER -