Essec\Faculty\Model\Contribution {#2216
#_index: "academ_contributions"
#_id: "10728"
#_source: array:26 [
"id" => "10728"
"slug" => "from-data-to-causes-i-building-a-general-cross-lagged-panel-model-gclm"
"yearMonth" => "2020-05"
"year" => "2020"
"title" => "From Data to Causes I: Building A General Cross-Lagged Panel Model (GCLM)"
"description" => "ZYPHUR, M.J., ALLISON, P.D., TAY, L., VOELKLE, M.C., PREACHER, K., ZHANG, Z. ... DIENER, E. (2020). From Data to Causes I: Building A General Cross-Lagged Panel Model (GCLM). <i>Organizational Research Methods</i>, 23(4), pp. 651-687."
"authors" => array:11 [
0 => array:3 [
"name" => "SHAMSOLLAHI Ali"
"bid" => "B00767865"
"slug" => "shamsollahi-ali"
]
1 => array:1 [
"name" => "ZYPHUR M. J."
]
2 => array:1 [
"name" => "ALLISON P. D."
]
3 => array:1 [
"name" => "TAY L."
]
4 => array:1 [
"name" => "VOELKLE M. C."
]
5 => array:1 [
"name" => "PREACHER Kristopher"
]
6 => array:1 [
"name" => "ZHANG Zhen"
]
7 => array:1 [
"name" => "HAMAKER Ellen"
]
8 => array:1 [
"name" => "PIERIDES Dean"
]
9 => array:1 [
"name" => "KOVAL Peter"
]
10 => array:1 [
"name" => "DIENER Ed"
]
]
"ouvrage" => ""
"keywords" => array:1 [
0 => "panel data model, cross-lagged panel model, causal inference, Granger causality, structural equation model, vector autoregressive VAR model, autoregression, moving average, ARMA, VARMA, panel VAR"
]
"updatedAt" => "2021-09-24 10:33:27"
"publicationUrl" => "https://journals.sagepub.com/doi/full/10.1177/1094428119847278"
"publicationInfo" => array:3 [
"pages" => "651-687"
"volume" => "23"
"number" => "4"
]
"type" => array:2 [
"fr" => "Articles"
"en" => "Journal articles"
]
"support_type" => array:2 [
"fr" => "Revue scientifique"
"en" => "Scientific journal"
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => "This is the first paper in a series of two that synthesizes, compares, and extends methods for causal inference with longitudinal panel data in a structural equation modeling (SEM) framework. Starting with a cross-lagged approach, this paper builds a general cross-lagged panel model (GCLM) with parameters to account for stable factors while increasing the range of dynamic processes that can be modeled. We illustrate the GCLM by examining the relationship between national income and subjective well-being (SWB), showing how to examine hypotheses about short-run (via Granger-Sims tests) versus long-run effects (via impulse responses). When controlling for stable factors, we find no short-run or long-run effects among these variables, showing national SWB to be relatively stable, whereas income is less so. Our second paper addresses the differences between the GCLM and other methods. Online Supplementary Materials offer an Excel file automating GCLM input for Mplus (with an example also for Lavaan in R) and analyses using additional data sets and all program input/output. We also offer an introductory GCLM presentation at https://youtu.be/tHnnaRNPbXs. We conclude with a discussion of issues surrounding causal inference."
"en" => "This is the first paper in a series of two that synthesizes, compares, and extends methods for causal inference with longitudinal panel data in a structural equation modeling (SEM) framework. Starting with a cross-lagged approach, this paper builds a general cross-lagged panel model (GCLM) with parameters to account for stable factors while increasing the range of dynamic processes that can be modeled. We illustrate the GCLM by examining the relationship between national income and subjective well-being (SWB), showing how to examine hypotheses about short-run (via Granger-Sims tests) versus long-run effects (via impulse responses). When controlling for stable factors, we find no short-run or long-run effects among these variables, showing national SWB to be relatively stable, whereas income is less so. Our second paper addresses the differences between the GCLM and other methods. Online Supplementary Materials offer an Excel file automating GCLM input for Mplus (with an example also for Lavaan in R) and analyses using additional data sets and all program input/output. We also offer an introductory GCLM presentation at https://youtu.be/tHnnaRNPbXs. We conclude with a discussion of issues surrounding causal inference."
]
"authors_fields" => array:2 [
"fr" => "Marketing"
"en" => "Marketing"
]
"indexedAt" => "2024-11-22T04:21:50.000Z"
"docTitle" => "From Data to Causes I: Building A General Cross-Lagged Panel Model (GCLM)"
"docSurtitle" => "Journal articles"
"authorNames" => "<a href="/cv/shamsollahi-ali">SHAMSOLLAHI Ali</a>, ZYPHUR M. J., ALLISON P. D., TAY L., VOELKLE M. C., PREACHER Kristopher, ZHANG Zhen, HAMAKER Ellen, PIERIDES Dean, KOVAL Peter, DIENER Ed"
"docDescription" => "<span class="document-property-authors">SHAMSOLLAHI Ali, ZYPHUR M. J., ALLISON P. D., TAY L., VOELKLE M. C., PREACHER Kristopher, ZHANG Zhen, HAMAKER Ellen, PIERIDES Dean, KOVAL Peter, DIENER Ed</span><br><span class="document-property-authors_fields">Marketing</span> | <span class="document-property-year">2020</span>"
"keywordList" => "<a href="#">panel data model, cross-lagged panel model, causal inference, Granger causality, structural equation model, vector autoregressive VAR model, autoregression, moving average, ARMA, VARMA, panel VAR</a>"
"docPreview" => "<b>From Data to Causes I: Building A General Cross-Lagged Panel Model (GCLM)</b><br><span>2020-05 | Journal articles </span>"
"docType" => "research"
"publicationLink" => "<a href="https://journals.sagepub.com/doi/full/10.1177/1094428119847278" target="_blank">From Data to Causes I: Building A General Cross-Lagged Panel Model (GCLM)</a>"
]
+lang: "en"
+"_type": "_doc"
+"_score": 8.730755
+"parent": null
}
