A Review and Guide to Structural Equation Modeling in Counseling Psychology Research

Abstract

Structural equation modeling (SEM) is a sophisticated form of quantitative analysis that allows researchers to examine complex relationships between latent variables while accounting for measurement error. Prior reviews of SEM studies in psychology and its subdisciplines found numerous methodological problems in application that either prevented adequate evaluation of the study or brought its results into question. We examined the state of the research over the past 7 years (2017–2023) in the two flagship journals of counseling psychology (The Counseling Psychologist [TCP] and the Journal of Counseling Psychology [JCP]). We found evidence of numerous questionable research practices across 62 articles and 65 models, including that independent calculations of the degrees of freedom did not agree with what authors reported in 52% of measurement models and 58% of structural models in the articles reviewed. We conclude with a checklist for researchers, journal reviewers, and editors to reduce common errors in the application of SEM.

Keywords

structural equation modeling counseling psychology quantitative research replication study validity

Get full access to this article

View all access options for this article.

References

American Psychological Association (2020). Table 7: Reporting standards for studies using structural equation modeling. https://apastyle.apa.org/jars/quant-table-7.pdf

American Psychological Association (2024). Summary report of journal operations, 2023. American Psychologist, 79(5), 777–778. https://doi.org/10.1037/amp0001383

American Psychological Association (2025). Journal of Counseling Psychology. https://www.apa.org/pubs/journals/cou

Asparouhov

Muthén

. (2009). Exploratory structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal, 16(3), 397–438. https://doi.org/10.1080/10705510903008204

Bentler

P. M

. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238–246. https://doi.org/10.1037/0033-2909.107.2.238

Bollen

K. A.

Lilly

A. G.

Luo

. (2024). Selecting scaling indicators in structural equation models (SEMs). Psychological Methods, 29(5), 868–889. https://doi.org/10.1037/met0000530

Boomsma

. (2000). Reporting analyses of covariance structures. Structural Equation Modeling: A Multidisciplinary Journal, 7(3), 461–483. https://doi.org/10.1207/S15328007SEM0703_6

Boomsma

Hoyle

R. H.

Panter

A. T

. (2012). The structural equation modeling research report. In Hoyle

R. H.

(Ed.), Handbook of structural equation modeling (pp. 341–358). The Guilford Press.

Brown

S. D.

Lent

R. W

. (2011). Handbook of Counseling Psychology (4th ed.). Wiley.

10.

Byrne

B. M

. (2012). Structural equation modeling with Mplus: Basic concepts, applications, and programming. Routledge.

11.

Chen

Feng

Zhang

Ding

Pan

(2024). A tutorial on Bayesian structural equation modeling: Principles and applications. International Journal of Psychology, 59(6), 1326–1346. https://doi.org/10.1002/ijop.13258

12.

Cheung

G. W.

Rensvold

R. B

. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal, 9(2), 233–255. https://doi.org/10.1207/S15328007SEM0902_5

13.

Cortina

J. M.

Green

J. P.

Keeler

K. R.

Vandenberg

R. J

. (2017). Degrees of freedom in SEM: Are we testing the models we claim to test? Organizational Research Methods, 20(3), 350–378. https://doi.org/10.1177/1094428116676345

14.

Crede

Harms

. (2019). Questionable research practices when using confirmatory factor analysis. Journal of Managerial Psychology, 34(1), 18–30. https://doi.org/10.1108/JMP-06-2018-0272

15.

Enders

C. K

. (2025). Missing data: An update on the state of the art. Psychological Methods, 30(2), 322–339. https://doi.org/10.1037/met0000563

16.

Fan

Sivo

S. A

. (2009). Using goodness-of-fit indexes in assessing mean structure invariance. Structural Equation Modeling: A Multidisciplinary Journal, 16(1), 54–69. https://doi.org/10.1080/10705510802561311

17.

Fietzer

A. W.

Mitchell

Ponterotto

J. G

. (2018). Multicultural personality and multicultural counseling competency in counselor trainees. Counselor Education & Supervision, 57(2), 82–97. https://doi.org/10.1002/ceas.12095

18.

Furr

R. M

. (2021). Psychometrics: An introduction (4th ed). Sage.

19.

Gefen

Rigdon

E. E.

Straub

. (2011). Editor’s comments: An update and extension to SEM guidelines for administrative and social research. MIS Quarterly, 35(2), iii–xiv. https://doi.org/10.2307/23044042

20.

Hayduk

L. A

. (2014). Shame for disrespecting evidence: The personal consequences of insufficient respect for structural equation model test. BMC Medical Research Methodology, 14(124), 1–10. https://doi.org/10.1186/1471-2288-14-124

21.

Hoyle

R. H.

Isherwood

J. C

. (2013). Reporting results from structural equation modeling analyses in Archives of Scientific Psychology. Archives of Scientific Psychology, 1(1), 14–22. https://doi.org/10.1037/arc0000004

22.

L.-t.

Bentler

P. M

. (1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods, 3(4), 424–453. https://doi.org/10.1037/1082-989X.3.4.424

23.

L.-t.

Bentler

P. M

. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118

24.

Jobst

L. J.

Bader

Moshagen

. (2023). A tutorial on assessing statistical power and determining sample size for structural equation models. Psychological Methods, 28(1), 207–221. https://doi.org/10.1037/met0000423

25.

Kline

R. B

. (2023). Principles and practice of structural equation modeling (5th ed.). Guildford Press.

26.

Liang

Sterba

S. K

. (2024). Power analysis to detect misfit in SEMs with many items: Resolving unrecognized problems, relating old and new approaches, and “matching” power analysis approach to data analysis approach. Psychological Methods. Advance online publication. https://doi.org/10.1037/met0000684

27.

Little

T. D.

Cunningham

W. A.

Shahar

Widaman

K. F

. (2002). To parcel or not to parcel: Exploring the question, weighing the merits. Structural Equation Modeling: A Multidisciplinary Journal, 9(2), 151–173. https://doi.org/10.1207/S15328007SEM0902_1

28.

Little

T. D.

Rhemtulla

Gibson

Schoemann

A. M

. (2013). Why the items versus parcels controversy needn’t be one. Psychological Methods, 18(3), 285–300. https://doi.org/10.1037/a0033266

29.

Little

T. D.

Slegers

D. W.

Card

N. A

. (2006). A non-arbitrary method of identifying and scaling latent variables in SEM and MACS models. Structural Equation Modeling: A Multidisciplinary Journal, 13(1), 59–72. https://doi.org/10.1207/s15328007sem1301_3

30.

MacCallum

R. C.

Austin

J. T

. (2000). Applications of structural equation modeling in psychological research. Annual Review of Psychology, 51, 201–226. https://doi.org/10.1146/annurev.psych.51.1.201

31.

MacCallum

R. C.

Browne

M. W.

Sugawara

H. M

. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1(2), 130–149. https://doi.org/10.1037/1082-989X.1.2.130

32.

Marsh

H. W.

Hau

K.-T.

Wen

. (2004). In search of golden rules: Comment on hypothesis-testing approaches to setting cutoff values for fit indexes and dangers in overgeneralizing Hu and Bentler’s (1999). Findings. Structural Equation Modeling: A Multidisciplinary Journal, 11(3), 320–341. https://doi.org/10.1207/s15328007sem1103_2

33.

Martens

M. P

. (2005). The use of structural equation modeling in counseling psychology research. The Counseling Psychologist, 33(3), 269–298. https://doi.org/10.1177/0011000004272260

34.

McDonald

R. P.

M.-H. R

. (2002). Principles and practice in reporting structural equation analyses. Psychological Methods, 7(1), 64–82. https://doi.org/10.1037//1082-989X.7.1.64

35.

Muthén

B. O.

(2008, May 16). Mplus Discussion. Skewness. https://www.statmodel.com/discussion/messages/9/352.html

36.

Ord

A. S.

Ripley

J. S.

Hook

Erspamer

. (2016). Teaching statistics in APA-accredited doctoral programs in clinical and counseling psychology: A syllabi review. Teaching of Psychology, 43(3), 221. https://doi.org/10.1177/0098628316649478

37.

Parent

M. C

. (2013). Handling item-level missing data: Simpler is just as good. The Counseling Psychologist, 41(4), 568–600. https://doi.org/10.1177/0011000012445176

38.

Pashler

Wagenmakers

E.-J

. (2012). Editors’ introduction to the special section on replicability in psychological science: A crisis of confidence? Perspectives on Psychological Science, 7(6), 528–530. https://doi.org/10.1177/1745691612465253

39.

Rossen

Oakland

. (2008). Graduate preparation in research methods: The current status of APA-accredited professional programs in psychology. Training and Education in Professional Psychology, 2(1), 42–49. https://doi.org/10.1037/1931-3918.2.1.42

40.

Schlomer

G. L.

Bauman

Card

N. A.

(2010). Best practices for missing data management in counseling psychology. Journal of Counseling Psychology, 57(1), 1–10. https://doi.org/10.1037/a0018082

41.

Schreiber

J. B.

Nora

Stage

F. K.

King

Barlow

E. A.

(2006). Reporting structural equation modeling and confirmatory factor analysis results: A review. The Journal of Educational Research, 99(6), 323–338. https://doi.org/10.3200/JOER.99.6.323-338

42.

Skorupiński

P. M

. (2014). American Educational Research Association, American Psychological Association, & National Council on Measurement in Education. Standards for educational and psychological testing. https://www.ceeol.com/search/article-detail?id=409541

43.

StataCorp . (1985–2015). Stata (version 14.2). StataCorp.

44.

Steiger

J. H

. (2001). Driving fast in reverse. Journal of the American Statistical Association, 96(453), 331–338. https://doi.org/10.1198/016214501750332893

45.

** Steiger

J. H.

Lind

J. C.

(1980, May). Statistically based tests for the number of common factors. Paper presented at the annual meeting of the Psychometric Society, Iowa City, IA.

46.

The Counseling Psychologist . (2025). Sage Publications. https://journals-sagepub-com-s.web.bisu.edu.cn/overview-metric/TCP

47.

Thelwall

Wilson

. (2015). Does research with statistics have more impact? The citation rank advantage of structural equation modeling. Journal of the Association for Information Science and Technology, 67(5), 1233–1244. https://doi.org/10.1002/asi.23474

48.

Tucker

L. R.

Lewis

. (1973). A reliability coefficient for maximum likelihood factor analysis. Psychometrika, 38(1), 1–10. https://doi.org/10.1007/BF02291170

49.

Tyson

Kennedy

. (2024, November 14). Public trust in scientists and views on their role in policymaking. Pew Research Center. https://www.pewresearch.org/science/2024/11/14/public-trust-in-scientists-and-views-on-their-role-in-policymaking/

50.

Weller

B. E.

Bowen

N. K.

Faubert

S. J

. (2020). Latent class analysis: A guide to best practice. Journal of Black Psychology, 46(4), 287–311. https://doi.org/10.1177/0095798420930932

51.

Weston

Gore

P. A.

Jr . (2006). A brief guide to structural equation modeling. The Counseling Psychologist, 34(5), 719–751. https://doi.org/10.1177/0011000006286345

52.

Worthington

R. L.

Whittaker

T. A

. (2006). Scale development research: A content analysis and recommendations for best practices. The Counseling Psychologist, 34(6), 806–838. https://doi.org/10.1177/0011000006288127

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.62 MB