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Who Benefits from Universal SEL Programming?: Assessment of Second Step© Using a Growth Mixture Modeling Approach

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Abstract

Outcomes from a large cluster-randomized study of Second Step©, a commonly adopted universal SEL program, have previously demonstrated small, teacher-reported effects on social–emotional competencies for subgroups of students. Given the size of investment in large RCT studies, and in SEL programming, replication attempts are warranted, ideally with diverse analytical strategies that can build a more convincing body of knowledge. The current manuscript builds upon previous study findings by utilizing a growth mixture modeling approach on a more limited set of outcomes. Intervention differences were significant for identified classes of hyperactivity, conduct problems, and emotional symptoms. Patterns suggest that Second Step© plays a predominantly mitigating role for those with modest levels of conduct problems and hyperactivity. Additionally, students in Second Step© schools were more likely to experience decreasing levels of emotional symptoms, as well as mitigation of escalation of symptoms. No academic effects were found, nor effects on prosocial skills. Findings are largely consistent with previous but different analytic approaches but extend to conduct problems and help in elucidating how universal SEL programming works. Implications for Second Step© implementation and future studies are briefly discussed.

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Notes

  1. Results for the emotional symptoms scale differ from Low (2015) due to a small error in the earlier report.

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Funding

For this project was provided by Committee for Children. No one working at Committee for Children was directly involved in the study activities, data collection, analyses, or dissemination.

Author information

Authors and Affiliations

Authors

Contributions

Both authors contributed to the study conception and design. Material preparation and data collection were performed under the leadership of Dr. Low and analyses were performed by Dr. Merrin. The first draft of the manuscript was written by both authors and both read and approved the final manuscript.

Corresponding author

Correspondence to Sabina Low.

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Conflict of interest

All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.

Ethical Approval

This study was approved by the appropriate institutional research ethics committees at Arizona State University and the University of Washington, and certified that the study was performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. Waiver of active consent was utilized for this study.

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Funding for this study was provided by Committee for Children, Seattle, WA.

Appendix

Appendix

Model Fit Indices

No. of classes

 − 2LL

Number of parameters

AIC

BIC

CAIC

AWE

LMRT

Adj LMRT

Entropy

Reading

1-class

232,239.178

13

232,265.178

232,357.242

232,370.242

232,514.305

2-class

227,949.694

17

227,983.694

228,104.085

228,121.085

228,309.476

0.001

0.001

0.909

3-class

225,999.974

21

226,041.974

226,190.692

226,211.692

226,444.411

0.090

0.094

0.905

Math

1-class

223,093.224

13

223,119.224

223,211.328

223,224.328

223,368.431

2-class

221,444.002

17

221,478.002

221,598.445

221,615.445

221,803.888

0.001

0.001

0.709

3-class

220,116.350

21

220,158.350

220,307.133

220,328.133

220,560.915

0.001

0.001

0.673

4-class

219,198.164

25

219,248.164

219,425.286

219,450.286

219,727.409

0.011

0.013

0.695

5-class

218,440.986

29

218,498.986

218,704.448

218,733.448

219,054.910

0.036

0.039

0.717

6-class

217,967.682

33

218,033.682

218,267.483

218,300.483

218,666.285

0.168

0.173

0.721

Conduct problems

1-class

9044.112

13

9070.112

9161.752

9174.752

9318.392

2-class

5139.080

17

5173.080

5292.917

5309.917

5497.754

0.033

0.036

0.927

3-class

2959.080

21

3001.080

3149.114

3170.114

3402.148

0.034

0.036

0.894

4-class

1581.724

25

1631.724

1807.955

1832.955

2109.186

0.040

0.044

0.891

5-class

366.098

29

424.098

628.526

657.526

977.953

0.050

0.054

0.884

Emotional symptoms

1-class

17,553.078

13

17,579.078

17,670.718

17,683.718

17,827.358

2-class

14,453.432

17

14,487.432

14,607.269

14,624.269

14,812.106

0.001

0.001

0.847

3-class

12,624.772

21

12,666.772

12,814.806

12,835.806

13,067.840

0.001

0.001

0.845

4-class

11,362.160

25

11,412.160

11,588.391

11,613.391

11,889.622

0.005

0.005

0.806

5-class

10,598.710

29

10,656.710

10,861.138

10,890.138

11,210.565

0.296

0.305

0.813

Hyperactivity

1-class

32,977.226

13

33,003.226

33,094.877

33,107.877

33,251.527

2-class

14,453.432

17

14,487.432

14,607.283

14,624.283

14,812.134

0.034

0.037

0.742

3-class

30,547.770

21

30,589.770

30,737.821

30,758.821

30,990.872

0.001

0.001

0.731

4-class

30,096.782

25

30,146.782

30,323.033

30,348.033

30,624.285

0.001

0.001

0.684

5-class

29,782.080

29

29,840.080

30,044.532

30,073.532

30,393.983

0.001

0.001

0.687

6-class

29,261.126

33

29,327.126

29,559.778

29,592.778

29,957.430

0.001

0.001

0.692

7-class

28,835.422

37

28,909.422

29,170.274

29,207.274

29,616.126

0.240

0.240

0.694

Prosocial

1-class

26,099.412

13

26,125.412

26,217.040

26,230.040

26,373.668

2-class

24,934.718

17

24,968.718

25,088.539

25,105.539

25,293.360

0.001

0.001

0.632

3-class

24,119.784

21

24,161.784

24,309.798

24,330.798

24,562.812

0.001

0.001

0.697

4-class

Non-convergence

        

Peer problems

1-class

9627.790

13

9653.790

9745.418

9758.418

9902.046

2-class

7720.862

17

7754.862

7874.683

7891.683

8079.504

0.003

0.004

0.805

3-class

6872.700

21

6914.700

7062.714

7083.714

7315.728

0.240

0.240

0.755

  1. Bold indicates significant differences at *p < ..5 or **p < ..01
  2.  − 2LL = Negative 2 log likelihood; AIC = Akaike information criteria; BIC = Bayesian information criteria; CAIC = consistent Akaike information criteria; AWE = approximate weight of evidence criterion; LMRT = Lo–Mendell–Rubin; Adj LMRT = adjusted Lo–Mendell–Rubin.

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Merrin, G.J., Low, S. Who Benefits from Universal SEL Programming?: Assessment of Second Step© Using a Growth Mixture Modeling Approach. School Mental Health 15, 177–189 (2023). https://doi.org/10.1007/s12310-022-09542-1

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