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. 2022 Nov 7:1–21. Online ahead of print. doi: 10.1007/s12144-022-03893-3

Table 3.

Parameter estimates for the eight main models

Standardized Coeff Unstandardized Coeff SE Standardized Coeff Unstandardized Coeff SE
Model 1. Ex – A (W1-W3) and Insp (W1-W3) Model 2. Ex – A (W1-W3) and Envy (W1-W3)
Within Ex – A (W1) ➔ Ex – A (W2) .195** .195** .070 Within Ex – A (W1) ➔ Ex – A (W2) .185** .184* .072
Autoregressive paths Ex – A (W2) ➔ Ex – A (W3) .116 .105 .074 Autoregressive paths Ex – A (W2) ➔ Ex – A (W3) .135 .122 .075
Insp (W1) ➔ Insp (W2) .120 .120 .078 Envy (W1) ➔ Envy (W2) .147 .155 .092
Insp (W2) ➔ Insp (W3) .095 .082 .077 Envy (W2) ➔ Envy (W3) .126 .118 .085
Within Ex – A (W1) ➔ Insp (W2) .075 .079 .068 Within Ex – A (W1) ➔ Envy (W2) .009 .013 .099
Cross-lagged paths Ex – A (W2) ➔ Insp (W3) .004 .003 .068 Cross-lagged paths Ex – A (W2) ➔ Envy (W3) -.044 -.062 .108
Insp (W1) ➔ Ex – A (W2) .136* .130* .060 Envy (W1) ➔ Ex – A (W2) .002 .001 .050
Insp (W2) ➔ Ex – A (W3) .168* .145* .064 Envy (W2) ➔ Ex – A (W3) -.002 -.001 .048

Within

W1 correlation

Ex – A (W1) with Insp (W1) -.015 -.008 .034

Within

W1 correlation

Ex – A (W1) with Envy (W1) .036 .025 .049
Within Ex – A (W2) .056 / / Within Ex – A (W2) .034 / /
R-square Ex – A (W3) .048 / / R-square Ex – A (W3) .018 / /
Insp (W2) .020 / / Envy (W2) .022 / /
Insp (W3) .009 / / Envy (W3) .017 / /
Between RI – Ex A with RI – Insp .375*** .125*** .030 Between RI – Ex A with RI – Envy .153* .094* .047
Model 3. Ex – PL (W1-W3) and Insp (W1-W3) Model 4. Ex – PL (W1-W3) and Envy (W1-W3)
Within Ex – PL (W1) ➔ Ex – PL (W2) .055 .064 .101 Within Ex – PL (W1) ➔ Ex – PL (W2) .025 .029 .109
Autoregressive paths Ex – PL (W2) ➔ Ex – PL (W3) .209** .193** .068 Autoregressive paths Ex – PL (W2) ➔ Ex – PL (W3) .223** .206** .068
Insp (W1) ➔ Insp (W2) .120 .121 .079 Envy (W1) ➔ Envy (W2) .155 .164 .089
Insp (W2) ➔ Insp (W3) .130 .112 .078 Envy (W2) ➔ Envy (W3) .123 .115 .084
Within Ex – PL (W1) ➔ Insp (W2) .181* .294* .117 Within Ex – PL (W1) ➔ Envy (W2) .038 .089 .175
Cross-lagged paths Ex – PL (W2) ➔ Insp (W3) -.067 -.082 .087 Cross-lagged paths Ex – PL (W2) ➔ Envy (W3) .072 .135 .138
Insp (W1) ➔ Ex – PL (W2) .050 .036 .048 Envy (W1) ➔ Ex – PL (W2) .062 .033 .041
Insp (W2) ➔ Ex – PL (W3) .114 .076 .047 Envy (W2) ➔ Ex – PL (W3) -.063 -.029 .035

Within

W1 correlation

Ex – PL (W1) with Insp (W1) .091 .031 .025

Within

W1 correlation

Ex – PL (W1) with Envy (W1) .112 .051 .035
Within Ex – PL (W2) .006 / / Within Ex – PL (W2) .005 / /
R-square Ex – PL (W3) .067 / / R-square Ex – PL (W3) .050 / /
Insp (W2) .051 / / Envy (W2) .027 / /
Insp (W3) .018 / / Envy (W3) .023 / /
Between RI – Ex PL with RI – Insp .330*** .091*** .023 Between RI – Ex PL with RI – Envy .162* .084* .035
Model 5. Ex – A (W1-W3) and SC (W1-W3) and Insp (W1-W3) Model 6. Ex – A (W1-W3) and SC (W1-W3) and Envy (W1-W3)
Within Ex – A (W1) ➔ Ex – A (W2) .198** .197** .070 Within Ex – A (W1) ➔ Ex – A (W2) .191** .190* .074
Autoregressive paths Ex – A (W2) ➔ Ex – A (W3) .116 .105 .086 Autoregressive paths Ex – A (W2) ➔ Ex – A (W3) .127 .115 .088
SC (W1) ➔ SC (W2) .163 .166 .086 SC (W1) ➔ SC (W2) .100 .103 .089
SC (W2) ➔ SC (W3) .058 .059 .106 SC (W2) ➔ SC (W3) .030 .031 .099
Insp (W1) ➔ Insp (W2) .083 .083 .100 Envy (W1) ➔ Envy (W2) .147 .153 .103
Insp (W2) ➔ Insp (W3) .088 .076 .096 Envy (W2) ➔ Envy (W3) .055 .051 .095
Within Ex – A (W1) ➔ Insp (W2) .075 .078 .069 Within Ex – A (W1) ➔ Envy (W2) .008 .012 .101
Cross-lagged paths Ex – A (W2) ➔ Insp (W3) .033 .030 .071 Cross-lagged paths Ex – A (W2) ➔ Envy (W3) -.090 -.126 .105
Ex – A (W1) ➔ SC (W2) -.006 -.011 .124 Ex – A (W1) ➔ SC (W2) -.022 -.040 .124
Ex – A (W2) ➔ SC (W3) .068 .129 .149 Ex – A (W2) ➔ SC (W3) .068 .130 .143
SC (W1) ➔ Insp (W2) .116 .066 .047 SC (W1) ➔ Envy (W2) .037 .031 .067
SC (W2) ➔ Insp (W3) -.138 -.068 .045 SC (W2) ➔ Envy (W3) .186* .143* .063
Insp (W1) ➔ Ex – A (W2) .121 .117 .063 Envy (W1) ➔ Ex – A (W2) -.029 -.020 .049
Insp (W2) ➔ Ex – A (W3) .169* .148* .075 Envy (W2) ➔ Ex – A (W3) -.012 -.007 .049
Insp (W1) ➔ SC (W2) .058 .102 .141 Envy (W1) ➔ SC (W2) .140 .117 .093
Insp (W2) ➔ SC (W3) .031 .056 .145 Envy (W2) ➔ SC (W3) .083 .106 .111
SC (W1) ➔ Ex – A (W2) .135* .075* .036 SC (W1) ➔ Ex – A (W2) .143* .081* .036
SC (W2) ➔ Ex – A (W3) -.001 .000 .038 SC (W2) ➔ Ex – A (W3) .015 .008 .038
Within Ex – A (W1) with Insp (W1) -.021 -.011 .033 Within Ex – A (W1) with Envy (W1) .047 .034 .051
W1 correlations Ex – A (W1) with SC (W1) -.005 -.004 .058 W1 correlations Ex – A (W1) with SC (W1) -.027 -.024 .056
Insp (W1) with SC (W1) .110 .102 .072 Envy (W1) with SC (W1) .255** .320** .113
Within Ex – A (W2) .074 / / Within Ex – A (W2) .054 / /
R-square Ex – A (W3) .049 / / R-square Ex – A (W3) .017 / /
Insp (W2) .028 / / Envy (W2) .026 / /
Insp (W3) .024 / / Envy (W3) .046 / /
SC (W2) .032 / / SC (W2) .037 / /
SC (W3) .011 / / SC (W3) .015 / /
Between RI – Ex A with RI – Insp .371*** .126*** .031 Between RI – Ex A with RI – SC .351*** .256*** .059
RI – Ex A with RI – SC .339*** .242*** .061 RI – Ex A with RI – Envy .164 .101* .050
RI – Insp with RI – SC .330*** .249*** .067 RI – SC with RI – Envy .829*** 1.135*** .115
Model 7. Ex – PL (W1-W3) and SC (W1-W3) and Insp (W1-W3) Model 8. Ex – PL (W1-W3) and SC (W1-W3) and Envy (W1-W3)
Within Ex – PL (W1) ➔ Ex – PL (W2) .032 .038 .127 Within Ex – PL (W1) ➔ Ex – PL (W2) .004 .004 .140
Autoregressive paths Ex – PL (W2) ➔ Ex – PL (W3) .214* .200* .085 Autoregressive paths Ex – PL (W2) ➔ Ex – PL (W3) .223* .209* .087
SC (W1) ➔ SC (W2) .162* .164 .085 SC (W1) ➔ SC (W2) .101 .103 .087
SC (W2) ➔ SC (W3) .047 .048 .109 SC (W2) ➔ SC (W3) .019 .020 .102
Insp (W1) ➔ Insp (W2) .089 .089 .098 Envy (W1) ➔ Envy (W2) .145 .151 .099
Insp (W2) ➔ Insp (W3) .123 .106 .097 Envy (W2) ➔ Envy (W3) .050 .047 .094
Within Ex – PL (W1) ➔ Insp (W2) .170* .276* .125 Within Ex – PL (W1) ➔ Envy (W2) .040 .094 .184
Cross-lagged paths Ex – PL (W2) ➔ Insp (W3) -.059 -.071 .104 Cross-lagged paths Ex – PL (W2) ➔ Envy (W3) .052 .098 .134
Ex – PL (W1) ➔ SC (W2) -.050 -.142 .230 Ex – PL (W1) ➔ SC (W2) -.066 -.187 .237
Ex – PL (W2) ➔ SC (W3) -.008 -.020 .217 Ex – PL (W2) ➔ SC (W3) -.017 -.043 .212
SC (W1) ➔ Insp (W2) .113 .066 .047 SC (W1) ➔ Envy (W2) .059 .050 .064
SC (W2) ➔ Insp (W3) -.128 -.064 .045 SC (W2) ➔ Envy (W3) .186* .144* .063
Insp (W1) ➔ Ex – PL (W2) .026 .019 .056 Envy (W1) ➔ Ex – PL (W2) .058 .030 .040
Insp (W2) ➔ Ex – PL (W3) .119 .080 .053 Envy (W2) ➔ Ex – PL (W3) -.032 -.015 .040
Insp (W1) ➔ SC (W2) .056 .098 .139 Envy (W1) ➔ SC (W2) .161* .203* .092
Insp (W2) ➔ SC (W3) .049 .087 .149 Envy (W2) ➔ SC (W3) .107 .134 .115
SC (W1) ➔ Ex—PL (W2) .097 .041 .032 SC (W1) ➔ Ex—PL (W2) .072 .030 .033
SC (W2) ➔ Ex—PL (W3) -.085 -.033 .029 SC (W2) ➔ Ex—PL (W3) -.071 -.027 .030
Within Ex – PL (W1) with Insp (W1) .066 .022 .026 Within Ex – PL (W1) with Envy (W1) .118 054 .038
W1 correlations Ex – PL (W1) with SC (W1) .030 .017 .047 W1 correlations Ex – PL (W1) with SC (W1) .018 .010 .047
Insp (W1) with SC (W1) .107 .100 .071 Envy (W1) with SC (W1) .275*** .348** .109
Within Ex – PL (W2) .012 / / Within Ex – PL (W2) .011 / /
R-square Ex – PL (W3) .070 / / R-square Ex – PL (W3) .051 / /
Insp (W2) .055 / / Envy (W2) .032 / /
Insp (W3) .029 / / Envy (W3) .049 / /
SC (W2) .033 / / SC (W2) .047 / /
SC (W3) .005 / / SC (W3) .013 / /
Between RI – Ex PL with RI – SC .318*** .196*** .047 Between RI Ex – PL with RI Envy .164* .086* .036
RI – Ex PL with RI – Insp .343*** .098*** .023 RI Ex – PL with RI SC .323*** .203*** .047
RI – SC with RI – Insp .329*** .245*** .067 RI Envy with RI SC .812*** 1.106*** .112

*** < .001; ** p < .01; * p < .05. For clarity, relations with control variables and correlations between error terms are not reported and significant relations are displayed in bold. For model 1 and 5, means were constrained. W1 = Wave 1, W2 = Wave 2, W3 = Wave 3. Ex—A = exposure to attractive appearances, Ex – PL = exposure to perfect lives, Insp = social media-induced inspiration, SC = social comparison

For meta-analyses on the within-person level with these data, please use the standardized coefficient estimates of the within-person cross-lagged paths for each of the models