Essec\Faculty\Model\Contribution {#2216
#_index: "academ_contributions"
#_id: "10729"
#_source: array:26 [
"id" => "10729"
"slug" => "from-data-to-causes-ii-comparing-approaches-to-panel-data-analysis"
"yearMonth" => "2020-05"
"year" => "2020"
"title" => "From Data to Causes II: Comparing Approaches to Panel Data Analysis"
"description" => "ZYPHUR, M.J., VOELKLE, M.C., TAY, L., ALLISON, P.D., PREACHER, K., ZHANG, Z. ... DIENER, E. (2020). From Data to Causes II: Comparing Approaches to Panel Data Analysis. <i>Organizational Research Methods</i>, 23(4), pp. 688-716."
"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" => "VOELKLE M. C."
]
3 => array:1 [
"name" => "TAY L."
]
4 => array:1 [
"name" => "ALLISON P. D."
]
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:9 [
0 => "panel data model"
1 => "causal inference"
2 => "cross-lagged model"
3 => "Granger causality"
4 => "structural equation model"
5 => "multilevel model"
6 => "latent curve model"
7 => "latent growth model"
8 => "Arellano-Bond methods"
]
"updatedAt" => "2023-01-05 13:32:40"
"publicationUrl" => "https://journals.sagepub.com/doi/full/10.1177/1094428119847280"
"publicationInfo" => array:3 [
"pages" => "688-716"
"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 article compares a general cross-lagged model (GCLM) to other panel data methods based on their coherence with a causal logic and pragmatic concerns regarding modeled dynamics and hypothesis testing. We examine three “static” models that do not incorporate temporal dynamics: random- and fixed-effects models that estimate contemporaneous relationships; and latent curve models. We then describe “dynamic” models that incorporate temporal dynamics in the form of lagged effects: cross-lagged models estimated in a structural equation model (SEM) or multilevel model (MLM) framework; Arellano-Bond dynamic panel data methods; and autoregressive latent trajectory models. We describe the implications of overlooking temporal dynamics in static models and show how even popular cross-lagged models fail to control for stable factors over time. We also show that Arellano-Bond and autoregressive latent trajectory models have various shortcomings. By contrasting these approaches, we clarify the benefits and drawbacks of common methods for modeling panel data, including the GCLM approach we propose. We conclude with a discussion of issues regarding causal inference, including difficulties in separating different types of time-invariant and time-varying effects over time."
"en" => "This article compares a general cross-lagged model (GCLM) to other panel data methods based on their coherence with a causal logic and pragmatic concerns regarding modeled dynamics and hypothesis testing. We examine three “static” models that do not incorporate temporal dynamics: random- and fixed-effects models that estimate contemporaneous relationships; and latent curve models. We then describe “dynamic” models that incorporate temporal dynamics in the form of lagged effects: cross-lagged models estimated in a structural equation model (SEM) or multilevel model (MLM) framework; Arellano-Bond dynamic panel data methods; and autoregressive latent trajectory models. We describe the implications of overlooking temporal dynamics in static models and show how even popular cross-lagged models fail to control for stable factors over time. We also show that Arellano-Bond and autoregressive latent trajectory models have various shortcomings. By contrasting these approaches, we clarify the benefits and drawbacks of common methods for modeling panel data, including the GCLM approach we propose. We conclude with a discussion of issues regarding causal inference, including difficulties in separating different types of time-invariant and time-varying effects over time."
]
"authors_fields" => array:2 [
"fr" => "Marketing"
"en" => "Marketing"
]
"indexedAt" => "2024-12-22T12:21:42.000Z"
"docTitle" => "From Data to Causes II: Comparing Approaches to Panel Data Analysis"
"docSurtitle" => "Journal articles"
"authorNames" => "<a href="/cv/shamsollahi-ali">SHAMSOLLAHI Ali</a>, ZYPHUR M. J., VOELKLE M. C., TAY L., ALLISON P. D., PREACHER Kristopher, ZHANG Zhen, HAMAKER Ellen, PIERIDES Dean, KOVAL Peter, DIENER Ed"
"docDescription" => "<span class="document-property-authors">SHAMSOLLAHI Ali, ZYPHUR M. J., VOELKLE M. C., TAY L., ALLISON P. D., 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</a>, <a href="#">causal inference</a>, <a href="#">cross-lagged model</a>, <a href="#">Granger causality</a>, <a href="#">structural equation model</a>, <a href="#">multilevel model</a>, <a href="#">latent curve model</a>, <a href="#">latent growth model</a>, <a href="#">Arellano-Bond methods</a>"
"docPreview" => "<b>From Data to Causes II: Comparing Approaches to Panel Data Analysis</b><br><span>2020-05 | Journal articles </span>"
"docType" => "research"
"publicationLink" => "<a href="https://journals.sagepub.com/doi/full/10.1177/1094428119847280" target="_blank">From Data to Causes II: Comparing Approaches to Panel Data Analysis</a>"
]
+lang: "en"
+"_type": "_doc"
+"_score": 9.029856
+"parent": null
}
