On the Selection of Item Scores or Composite Scores for Clinical Prediction

References

• Allen, M. J., & Yen, W. M. (2001). Introduction to measurement theory. Waveland Press.
   Google Scholar
• American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). https://doi.org/10.1176/appi.books.9780890425596
   Google Scholar
• Anastasi, A. (1950). The concept of validity in the interpretation of test scores. Educational and Psychological Measurement, 10(1), 67–78. https://doi.org/10.1177/001316445001000105
   Web of Science ®Google Scholar
• Ashton, M. C., Jackson, D. N., Paunonen, S. V., Helmes, E., & Rothstein, M. G. (1995). The Criterion Validity of Broad Factor Scales versus Specific Facet Scales. Journal of Research in Personality, 29(4), 432–442. https://doi.org/10.1006/jrpe.1995.1025
   Web of Science ®Google Scholar
• Beck, A. T., Brown, G., & Steer, R. A. (1996). Beck Depression Inventory II manual. The Psychological Corporation.
   Google Scholar
• Bollen, K. A. (1989). Structural equations with latent variables. John Wiley & Sons. https://doi.org/10.1002/9781118619179
   Google Scholar
• Bollen, K., & Lennox, R. (1991). Conventional wisdom on measurement: A structural equation perspective. Psychological Bulletin, 110(2), 305–314. https://doi.org/10.1037/0033-2909.110.2.305
   Web of Science ®Google Scholar
• Borsboom, D., & Cramer, A. O. J. (2013). Network analysis: An integrative approach to the structure of psychopathology. Annual Review of Clinical Psychology, 9(1), 91–121. https://doi.org/10.1146/annurev-clinpsy-050212-185608
   PubMed Web of Science ®Google Scholar
• Borsboom, D., Mellenbergh, G. J., & Heerden, J. V. (2003). The theoretical status of latent variables. Psychological Review, 110(2), 203–219. https://doi.org/10.1037/0033-295X.110.2.203
   PubMed Web of Science ®Google Scholar
• Boyle, G. J. (1991). Does item homogeneity indicate internal consistency or item redundancy in psychometric scales? Personality and Individual Differences, 12(3), 291–294. https://doi.org/10.1016/0191-8869(91)90115-R
   Web of Science ®Google Scholar
• Breiman, L. (2001). Statistical modeling: The two cultures (with comments and a rejoinder by the author). Statistical Science, 16(3), 199–231. https://doi.org/10.1214/ss/1009213726
   Web of Science ®Google Scholar
• Briggs, S. R., & Cheek, J. M. (1986). The role of factor analysis in the development and evaluation of personality scales. Journal of Personality, 54(1), 106–148. https://doi.org/10.1111/j.1467-6494.1986.tb00391.x
   Web of Science ®Google Scholar
• Brouwer, D., Meijer, R. R., & Zevalkink, J. (2013). On the factor structure of the Beck Depression Inventory–II: G is the key. Psychological Assessment, 25(1), 136–145. https://doi.org/10.1037/a0029228
   PubMed Web of Science ®Google Scholar
• Buhrmester, M., Kwang, T., & Gosling, S. D. (2011). Amazon’s mechanical turk: A new source of inexpensive, yet high-quality, data? Perspectives on Psychological Science: A Journal of the Association for Psychological Science, 6(1), 3–5. https://doi.org/10.1177/1745691610393980
   PubMedGoogle Scholar
• Burisch, M. (1984). Approaches to personality inventory construction: A comparison of merits. American Psychologist, 39(3), 214–227. https://doi.org/10.1037/0003-066X.39.3.214
   Web of Science ®Google Scholar
• Burman, P. (1989). A comparative study of ordinary cross-validation, r-fold cross-validation and the repeated learning-testing methods. Biometrika, 76(3), 503–514. https://doi.org/10.2307/2336116
   Web of Science ®Google Scholar
• Butcher, J. N., Dahlstrom, W. G., Graham, J. R., Tellegen, A., & Kaemmer, B. (1989). Manual for administration and scoring. Minnesota Multiphasic Personality Inventory—2: MMPI-2. University of Minnesota Press.
   Google Scholar
• Carroll, R. J., Ruppert, D., Stefanski, L. A., & Crainiceanu, C. M. (2006). Measurement error in nonlinear models: A modern perspective (2nd ed.). Chapman and Hall/CRC. https://doi.org/10.1201/9781420010138
   Google Scholar
• Cattell, R. B. (1960). The multiple abstract variance analysis equations and solutions: For nature-nurture research on continuous variables. Psychological Review, 67(6), 353–372. https://doi.org/10.1037/h0043487
   PubMed Web of Science ®Google Scholar
• Cattell, R. B. (1966). Handbook of multivariate experimental psychology. Chicago.
   Google Scholar
• Chandler, J., & Shapiro, D. (2016). Conducting clinical research using crowdsourced convenience samples. Annual Review of Clinical Psychology, 12(1), 53–81. https://doi.org/10.1146/annurev-clinpsy-021815-093623
   PubMed Web of Science ®Google Scholar
• Clark, L. A., & Watson, D. (2019). Constructing validity: New developments in creating objective measuring instruments. Psychological Assessment, 31(12), 1412–1427. https://doi.org/10.1037/pas0000626
   PubMed Web of Science ®Google Scholar
• Clark, L., & Watson, D. (1995). Constructing validity: Basic issues in objective scale development. Psychological Assessment, 7(3), 309–319. https://doi.org/10.1037/14805-012
   Web of Science ®Google Scholar
• Condon, D. M., Wood, D., Mõttus, R., Booth, T., Costantini, G., Greiff, S., Johnson, W., Lukaszewski, A., Murray, A., Revelle, W., Wright, A. G. C., Ziegler, M., & Zimmermann, J. (2020). Bottom up construction of a personality taxonomy. European Journal of Psychological Assessment, 36(6), 923–934. https://doi.org/10.1027/1015-5759/a000626
   Web of Science ®Google Scholar
• Cook, D. A., Brydges, R., Ginsburg, S., & Hatala, R. (2015). A contemporary approach to validity arguments: A practical guide to Kane’s framework. Medical Education, 49(6), 560–575. https://doi.org/10.1111/medu.12678
   PubMed Web of Science ®Google Scholar
• Cortina, J. M. (1993). What is coefficient alpha? An examination of theory and applications. Journal of Applied Psychology, 78(1), 98–104. https://doi.org/10.1037/0021-9010.78.1.98
   Web of Science ®Google Scholar
• Crocker, L., & Algina, J. (2008). Introduction to classical and modern test theory. Cengage Learning.
   Google Scholar
• Cronbach, L. J. (1988). Five perspectives on validity argument. In H. Wainer & H. Braun (Eds.), Test validity (pp. 3–17). Lawrence Erlbaum.
   Google Scholar
• Cronbach, L. J., & Meehl, P. E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52(4), 281–302. https://doi.org/10.1037/h0040957
   PubMed Web of Science ®Google Scholar
• Dawes, R. M. (1979). The robust beauty of improper linear models in decision making. American Psychologist, 34(7), 571–582. https://doi.org/10.1037/0003-066X.34.7.571
   Web of Science ®Google Scholar
• Dawes, R. M., Faust, D., & Meehl, P. E. (1993). Statistical prediction versus clinical prediction: Improving what works. In G. Keren & C. Lewis (Eds.), A handbook for data analysis in the behavioral sciences: Methodological issues (pp. 351–367). Lawrence Erlbaum.
   Google Scholar
• de Rooij, M., & Weeda, W. (2020). Cross-validation: A method every psychologist should know. Advances in Methods and Practices in Psychological Science, 3(2), 248–263. https://doi.org/10.1177/2515245919898466
   Google Scholar
• Dudley, N. M., Orvis, K. A., Lebiecki, J. E., & Cortina, J. M. (2006). A meta-analytic investigation of conscientiousness in the prediction of job performance: Examining the intercorrelations and the incremental validity of narrow traits. The Journal of Applied Psychology, 91(1), 40–57. https://doi.org/10.1037/0021-9010.91.1.40
   PubMed Web of Science ®Google Scholar
• Dwyer, D. B., Falkai, P., & Koutsouleris, N. (2018). Machine learning approaches for clinical psychology and psychiatry. Annual Review of Clinical Psychology, 14(1), 91–118. https://doi.org/10.1146/annurev-clinpsy-032816-045037
   PubMed Web of Science ®Google Scholar
• Edwards, J. R., & Bagozzi, R. P. (2000). On the nature and direction of relationships between constructs and measures. Psychological Methods, 5(2), 155–174. https://doi.org/10.1037/1082-989X.5.2.155
   PubMed Web of Science ®Google Scholar
• Ekehammar, B., & Akrami, N. (2007). Personality and prejudice: From big five personality factors to facets. Journal of Personality, 75(5), 899–925. https://doi.org/10.1111/j.1467-6494.2007.00460.x
   PubMed Web of Science ®Google Scholar
• Ellis, J. L., & Junker, B. W. (1997). Tail-measurability in monotone latent variable models. Psychometrika, 62(4), 495–523. https://doi.org/10.1007/BF02294640
   Web of Science ®Google Scholar
• Forkmann, T., Glaesmer, H., Paashaus, L., Rath, D., Schönfelder, A., Stengler, K., Juckel, G., Assion, H.-J., & Teismann, T. (2020). Interpersonal theory of suicide: Prospective examination. BJPsych Open, 6(5), e113. https://doi.org/10.1192/bjo.2020.93
   PubMed Web of Science ®Google Scholar
• Fried, E. I., & Nesse, R. M. (2015). Depression sum-scores don’t add up: Why analyzing specific depression symptoms is essential. BMC Medicine, 13(1), 72. https://doi.org/10.1186/s12916-015-0325-4
   PubMed Web of Science ®Google Scholar
• Fried, E. I., Nesse, R. M., Zivin, K., Guille, C., & Sen, S. (2014). Depression is more than the sum-score of its parts: Individual DSM symptoms have different risk factors. Psychological Medicine, 44(10), 2067–2076. https://doi.org/10.1017/S0033291713002900
   PubMed Web of Science ®Google Scholar
• Fried, E. I., Stockert, S., von, Haslbeck, J. M. B., Lamers, F., Schoevers, R. A., & Penninx, B. W. J. H. (2020). Using network analysis to examine links between individual depressive symptoms, inflammatory markers, and covariates. Psychological Medicine, 50(16), 2682–2690. https://doi.org/10.1017/S0033291719002770
   PubMed Web of Science ®Google Scholar
• Fuller, W. A. (1987). Measurement error models. John Wiley & Sons.
   Google Scholar
• Golino, H., & Christensen, A. P. (2022). EGAnet: Exploratory Graph Analysis – A framework for estimating the number of dimensions in multivarte data using network psychometrics. R package version 1.01.
   Google Scholar
• Golino, H. F., & Epskamp, S. (2017). Exploratory graph analysis: A new approach for estimating the number of dimensions in psychological research. PloS One, 12(6), e0174035. https://doi.org/10.1371/journal.pone.0174035
   PubMed Web of Science ®Google Scholar
• Green, S. B., Lissitz, R. W., & Mulaik, S. A. (1977). Limitations of coefficient alpha as an Index of test unidimensionality. Educational and Psychological Measurement, 37(4), 827–838. https://doi.org/10.1177/001316447703700403
   Web of Science ®Google Scholar
• Gutierrez, P. M., Joiner, T., Hanson, J., Stanley, I. H., Silva, C., & Rogers, M. L. (2019). Psychometric properties of four commonly used suicide risk assessment measures: Applicability to military treatment settings. Military Behavioral Health, 7(2), 177–184. https://doi.org/10.1080/21635781.2018.1562390
   Google Scholar
• Haig, B. D. (2014). Investigating the psychological world: Scientific method in the behavioral sciences. MIT Press.
   Google Scholar
• Hamaker, E. L., Mulder, J. D., & van IJzendoorn, M. H. (2020). Description, prediction and causation: Methodological challenges of studying child and adolescent development. Developmental Cognitive Neuroscience, 46, 100867. https://doi.org/10.1016/j.dcn.2020.100867
   PubMed Web of Science ®Google Scholar
• Hastie, T. (2020). Ridge regularization: An essential concept in data science. Technometrics: A Journal of Statistics for the Physical, Chemical, and Engineering Sciences, 62(4), 426–433. https://doi.org/10.1080/00401706.2020.1791959
   PubMed Web of Science ®Google Scholar
• Hastie, T., Tibshirani, R., & Friedman, F. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.) Springer-Verlag.
   Google Scholar
• Hathaway, S. R., & Mckinley, J. C. (1940). A Multiphasic Personality Schedule (Minnesota): I. Construction of the schedule. The Journal of Psychology, 10(2), 249–254. https://doi.org/10.1080/00223980.1940.9917000
   Google Scholar
• Hauser, D. J., & Schwarz, N. (2016). Attentive Turkers: MTurk participants perform better on online attention checks than do subject pool participants. Behavior Research Methods, 48(1), 400–407. https://doi.org/10.3758/s13428-015-0578-z
   PubMed Web of Science ®Google Scholar
• Hernán, M. A., Hsu, J., & Healy, B. (2019). A second chance to get causal inference right: A classification of data science tasks. CHANCE, 32(1), 42–49. https://doi.org/10.1080/09332480.2019.1579578
   Google Scholar
• Horn, J. L. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 30(2), 179–185. https://doi.org/10.1007/BF02289447
   PubMed Web of Science ®Google Scholar
• Hu, L., & 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
   Web of Science ®Google Scholar
• Jacobucci, R., & Grimm, K. J. (2020). Machine learning and psychological research: The unexplored effect of measurement. Perspectives on Psychological Science: A Journal of the Association for Psychological Science, 15(3), 809–816. https://doi.org/10.1177/1745691620902467
   PubMedGoogle Scholar
• Jacobucci, R., Littlefield, A. K., Millner, A. J., Kleiman, E. M., & Steinley, D. (2021). Evidence of inflated prediction performance: A commentary on machine learning and suicide research. Clinical Psychological Science, 9(1), 129–134. https://doi.org/10.1177/2167702620954216
   Web of Science ®Google Scholar
• Joiner, T. E. (2005). Why people die by suicide. Harvard University Press.
   Google Scholar
• Jung, Y. (2018). Multiple predicting K -fold cross-validation for model selection. Journal of Nonparametric Statistics, 30(1), 197–215. https://doi.org/10.1080/10485252.2017.1404598
   Web of Science ®Google Scholar
• Kane, M. (1992). An argument-based approach to validation. Psychological Bulletin, 112(3), 527–535. https://doi.org/10.1037/0033-2909.112.3.527
   Web of Science ®Google Scholar
• Kane, M. T. (2013). Validating the interpretations and uses of test scores. Journal of Educational Measurement, 50(1), 1–73. https://doi.org/10.1111/jedm.12000
   Web of Science ®Google Scholar
• Kendler, K. S. (2017). DSM disorders and their criteria: How should they inter-relate? Psychological Medicine, 47(12), 2054–2060. https://doi.org/10.1017/S0033291717000678
   PubMed Web of Science ®Google Scholar
• Kendler, K. S., Gardner, C. O., Neale, M. C., Aggen, S., Heath, A., Colodro-Conde, L., Couvyduchesne, B., Byrne, E. M., Martin, N. G., & Gillespie, N. A. (2019). Shared and specific genetic risk factors for lifetime major depression, depressive symptoms and neuroticism in three population-based twin samples. Psychological Medicine, 49(16), 2745–2753. https://doi.org/10.1017/S003329171800377X
   PubMed Web of Science ®Google Scholar
• Kennedy, L., & Gelman, A. (2021). Know your population and know your model: Using model-based regression and post-stratification to generalize findings beyond the observed sample. Psychological Methods, 26(5), 547–558. https://doi.org/10.1037/met0000362
   PubMed Web of Science ®Google Scholar
• Keum, B. T., Miller, M. J., & Inkelas, K. K. (2018). Testing the factor structure and measurement invariance of the PHQ-9 across racially diverse U.S. college students. Psychological Assessment, 30(8), 1096–1106. https://doi.org/10.1037/pas0000550
   PubMed Web of Science ®Google Scholar
• Kim, J.-H. (2009). Estimating classification error rate: Repeated cross-validation, repeated hold-out and bootstrap. Computational Statistics & Data Analysis, 53(11), 3735–3745. https://doi.org/10.1016/j.csda.2009.04.009
   Web of Science ®Google Scholar
• Kotov, R., Krueger, R. F., Watson, D., Achenbach, T. M., Althoff, R. R., Bagby, R. M., Brown, T. A., Carpenter, W. T., Caspi, A., Clark, L. A., Eaton, N. R., Forbes, M. K., Forbush, K. T., Goldberg, D., Hasin, D., Hyman, S. E., Ivanova, M. Y., Lynam, D. R., Markon, K., … Zimmerman, M. (2017). The Hierarchical Taxonomy of Psychopathology (HiTOP): A dimensional alternative to traditional nosologies. Journal of Abnormal Psychology, 126(4), 454–477. https://doi.org/10.1037/abn0000258
   PubMed Web of Science ®Google Scholar
• Kroenke, K., Spitzer, R. L., & Williams, J. B. W. (2001). The PHQ-9: Validity of a brief depression severity measure. Journal of General Internal Medicine, 16(9), 606–613. https://doi.org/10.1046/j.1525-1497.2001.016009606.x
   PubMed Web of Science ®Google Scholar
• Kroenke, K., Spitzer, R. L., Williams, J. B. W., & Löwe, B. (2010). The patient health questionnaire somatic, anxiety, and depressive symptom scales: A systematic review. General Hospital Psychiatry, 32(4), 345–359. https://doi.org/10.1016/j.genhosppsych.2010.03.006
   PubMed Web of Science ®Google Scholar
• Kuhn, M. (2022). caret: Classification and Regression Training. R package version 6.0-9.2. https://CRAN.R-project.org/package=caret
   Google Scholar
• Lawrence, H. R., Burke, T. A., Sheehan, A. E., Pastro, B., Levin, R. Y., Walsh, R. F. L., Bettis, A. H., & Liu, R. T. (2021). Prevalence and correlates of suicidal ideation and suicide attempts in preadolescent children: A US population-based study. Translational Psychiatry, 11(1), 489. https://doi.org/10.1038/s41398-021-01593-3
   PubMed Web of Science ®Google Scholar
• Littlefield, A. K., Cooke, J. T., Bagge, C. L., Glenn, C. R., Kleiman, E. M., Jacobucci, R., Millner, A. J., & Steinley, D. (2021). Machine learning to classify suicidal thoughts and behaviors: Implementation within the common data elements used by the military Suicide research consortium. Clinical Psychological Science, 9(3), 467–481. https://doi.org/10.1177/2167702620961067
   Web of Science ®Google Scholar
• Liu, R. T., Lawrence, H. R., Burke, T. A., Sanzari, C. M., Levin, R. Y., Maitlin, C., Paszek, C., & Zhu, X. (2021). Passive and active suicidal ideation among left‐behind children in rural China: An evaluation of intrapersonal and interpersonal vulnerability and resilience. Suicide & Life-Threatening Behavior, 51(6), 1213–1223. https://doi.org/10.1111/sltb.12802
   PubMed Web of Science ®Google Scholar
• Loevinger, J. (1957). Objective tests as instruments of psychological theory. Psychological Reports, 3(3), 635–694. https://doi.org/10.2466/pr0.1957.3.3.635
   Google Scholar
• Lord, F. M., Novick, M. R., & Birnbaum, A. (1968). Statistical theories of mental test scores. Addison-Wesley.
   Google Scholar
• Lovibond, P. F., & Lovibond, S. H. (1995). The structure of negative emotional states: Comparison of the Depression Anxiety Stress Scales (DASS) with the Beck Depression and Anxiety Inventories. Behaviour Research and Therapy, 33(3), 335–343. https://doi.org/10.1016/0005-7967(94)00075-U
   PubMed Web of Science ®Google Scholar
• Manian, N., Schmidt, E., Bornstein, M. H., & Martinez, P. (2013). Factor structure and clinical utility of BDI-II factor scores in postpartum women. Journal of Affective Disorders, 149(1–3), 259–268. https://doi.org/10.1016/j.jad.2013.01.039
   PubMed Web of Science ®Google Scholar
• McCrae, R. R. (2015). A more nuanced view of reliability: Specificity in the trait hierarchy. Personality and Social Psychology Review, 19(2), 97–112. https://doi.org/10.1177/1088868314541857
   PubMed Web of Science ®Google Scholar
• McDonald, R. P. (1996). Path analysis with composite variables. Multivariate Behavioral Research, 31(2), 239–270. https://doi.org/10.1207/s15327906mbr3102_5
   PubMed Web of Science ®Google Scholar
• McNally, R. J. (2021). Network analysis of psychopathology: Controversies and challenges. Annual Review of Clinical Psychology, 17(1), 31–53. https://doi.org/10.1146/annurev-clinpsy-081219-092850
   PubMedGoogle Scholar
• McNamara, M. E., Zisser, M., Beevers, C. G., & Shumake, J. (2022). Not just “big” data: Importance of sample size, measurement error, and uninformative predictors for developing prognostic models for digital interventions. Behaviour Research and Therapy, 153, 104086. https://doi.org/10.1016/j.brat.2022.104086
   PubMed Web of Science ®Google Scholar
• Meehl, P. E. (1954). Clinical versus statistical prediction: A theoretical analysis and a review of the evidence. University of Minnesota Press. https://doi.org/10.1037/11281-000
   Google Scholar
• Mershon, B., & Gorsuch, R. L. (1988). Number of factors in the personality sphere: Does increase in factors increase predictability of real-life criteria? Journal of Personality and Social Psychology, 55(4), 675–680. https://doi.org/10.1037/0022-3514.55.4.675
   Web of Science ®Google Scholar
• Messick, S. (1989). Meaning and values in test validation: The science and ethics of assessment. Educational Researcher, 18(2), 5–11. https://doi.org/10.3102/0013189X018002005
   Google Scholar
• Mitchell, S. M., Brown, S. L., Roush, J. F., Tucker, R. P., Cukrowicz, K. C., & Joiner, T. E. (2020). The Interpersonal Needs Questionnaire: Statistical considerations for improved clinical application. Assessment, 27(3), 621–637. https://doi.org/10.1177/1073191118824660
   PubMed Web of Science ®Google Scholar
• Mõttus, R., Kandler, C., Bleidorn, W., Riemann, R., & McCrae, R. R. (2016). Personality traits below facets: The consensual validity, longitudinal stability, heritability, and utility of personality nuances. Journal of Personality and Social Psychology, 112(3), 474–490. https://doi.org/10.1037/pspp0000100
   PubMed Web of Science ®Google Scholar
• Mõttus, R., & Rozgonjuk, D. (2019). Development is in the details: Age differences in the Big Five domains, facets, and nuances. Journal of Personality and Social Psychology, 120(4), 1035–1048. https://doi.org/10.1037/pspp0000276
   PubMed Web of Science ®Google Scholar
• Mõttus, R., Wood, D., Condon, D. M., Back, M. D., Baumert, A., Costantini, G., Epskamp, S., Greiff, S., Johnson, W., Lukaszewski, A., Murray, A., Revelle, W., Wright, A. G. C., Yarkoni, T., Ziegler, M., & Zimmermann, J. (2020). Descriptive, predictive and explanatory personality research: Different goals, different approaches, but a shared Need to move beyond the Big Few traits. European Journal of Personality, 34(6), 1175–1201. https://doi.org/10.1002/per.2311
   Web of Science ®Google Scholar
• Nock, M. K., Hwang, I., Sampson, N. A., & Kessler, R. C. (2010). Mental disorders, comorbidity and suicidal behavior: Results from the National Comorbidity Survey Replication. Molecular Psychiatry, 15(8), 868–876. https://doi.org/10.1038/mp.2009.29
   PubMed Web of Science ®Google Scholar
• Paunonen, S. V., & Ashton, M. C. (2001). Big Five factors and facets and the prediction of behavior. Journal of Personality and Social Psychology, 81(3), 524–539. https://doi.org/10.1037/0022-3514.81.3.524
   PubMed Web of Science ®Google Scholar
• Pearl, J., & Bareinboim, E. (2014). External validity: From do-calculus to transportability across population. Statistical Science, 29(4), 579–595. https://doi.org/10.1145/3501714.3501741
   Web of Science ®Google Scholar
• Preacher, K. J., & Yaremych, H. E. (2023). Model selection in structural equation modeling. In R. H. Hoyle (Ed.), Handbook of structural equation modeling (2nd ed., pp. 206–222). Guilford Press.
   Google Scholar
• R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/
   Google Scholar
• Raftery, A. E. (1995). Bayesian model selection in social research. Sociological Methodology, 25, 111–163. https://doi.org/10.2307/271063
   Web of Science ®Google Scholar
• Revelle, W. (2022). psych: Procedures for Personality and Psychological Research, Northwestern University, Evanston, Illinois, USA, https://CRAN.R-project.org/package=psych
   Google Scholar
• Robinaugh, D. J., Hoekstra, R. H. A., Toner, E. R., & Borsboom, D. (2020). The network approach to psychopathology: A review of the literature 2008–2018 and an agenda for future research. Psychological Medicine, 50(3), 353–366. https://doi.org/10.1017/S0033291719003404
   PubMed Web of Science ®Google Scholar
• Rogers, M. L., & Joiner, T. E. (2019). Exploring the temporal dynamics of the interpersonal theory of suicide constructs: A dynamic systems modeling approach. Journal of Consulting and Clinical Psychology, 87(1), 56–66. https://doi.org/10.1037/ccp0000373
   PubMed Web of Science ®Google Scholar
• Rosseel, Y. (2012). lavaan: An R package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1–36. https://doi.org/10.18637/jss.v048.i02
   Web of Science ®Google Scholar
• Seeboth, A., & Mõttus, R. (2018). Successful explanations start with accurate descriptions: Questionnaire items as personality markers for more accurate predictions. European Journal of Personality, 32(3), 186–201. https://doi.org/10.1002/per.2147
   Web of Science ®Google Scholar
• Shapiro, D. N., Chandler, J., & Mueller, P. A. (2013). Using Mechanical Turk to study clinical populations. Clinical Psychological Science, 1(2), 213–220. https://doi.org/10.1177/2167702612469015
   Google Scholar
• Shmueli, G. (2010). To explain or to predict? Statistical Science, 25(3), 289–310. https://doi.org/10.1214/10-STS330
   Web of Science ®Google Scholar
• St. John, C. H., & Roth, P. L. (1999). The impact of cross-validation adjustments on estimates of effect size in business policy and strategy research. Organizational Research Methods, 2(2), 157–174. https://doi.org/10.1177/109442819922003
   Web of Science ®Google Scholar
• Stanton, K., McDonnell, C. G., Hayden, E. P., & Watson, D. (2020). Transdiagnostic approaches to psychopathology measurement: Recommendations for measure selection, data analysis, and participant recruitment. Journal of Abnormal Psychology, 129(1), 21–28. https://doi.org/10.1037/abn0000464
   PubMed Web of Science ®Google Scholar
• Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267–288. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x
   Web of Science ®Google Scholar
• Van Buuren, S., & Groothuis-Oudshoorn, K. (2011). mice: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software, 45(3), 1–67. https://doi.org/10.18637/jss.v045.i03
   Web of Science ®Google Scholar
• Van Orden, K. A., Cukrowicz, K. C., Witte, T. K., & Joiner, T. E. (2012). Thwarted belongingness and perceived burdensomeness: Construct validity and psychometric properties of the Interpersonal Needs Questionnaire. Psychological Assessment, 24(1), 197–215. https://doi.org/10.1037/a0025358
   PubMed Web of Science ®Google Scholar
• Van Orden, K. A., Witte, T. K., Cukrowicz, K. C., Braithwaite, S. R., Selby, E. A., & Joiner, T. E. (2010). The interpersonal theory of suicide. Psychological Review, 117(2), 575–600. https://doi.org/10.1037/a0018697
   PubMed Web of Science ®Google Scholar
• Vanheule, S., Desmet, M., Groenvynck, H., Rosseel, Y., & Fontaine, J. (2008). The factor structure of the Beck Depression Inventory–II: An evaluation. Assessment, 15(2), 177–187. https://doi.org/10.1177/1073191107311261
   PubMed Web of Science ®Google Scholar
• Webb, M. A., & Tangney, J. P. (2022). Too good to be true: Bots and bad data from Mechanical Turk. Perspectives on Psychological Science. https://doi.org/10.1177/17456916221120027
   Web of Science ®Google Scholar
• Wherry, R. J. (1931). A new formula for predicting the shrinkage of the coefficient of multiple correlation. The Annals of Mathematical Statistics, 2(4), 440–457. https://www.jstor.org/stable/2957681 https://doi.org/10.1214/aoms/1177732951
   Google Scholar
• Widaman, K. F., & Revelle, W. (2022). Thinking thrice about sum scores, and then some more about measurement and analysis. Behavior Research Methods, 55(2), 788–806. https://doi.org/10.3758/s13428-022-01849-w
   PubMed Web of Science ®Google Scholar
• Williams, J. M. G., Crane, C., Barnhofer, T., Van der Does, A. J. W., & Segal, Z. V. (2006). Recurrence of suicidal ideation across depressive episodes. Journal of Affective Disorders, 91(2-3), 189–194. https://doi.org/10.1016/j.jad.2006.01.002
   PubMed Web of Science ®Google Scholar
• World Health Organization. (2019). International statistical classification of diseases and related health problems (11th ed.). https://icd.who.int/
   Google Scholar
• Yarkoni, T. (2022). The generalizability crisis. The Behavioral and Brain Sciences, 45, e1. https://doi.org/10.1017/S0140525X20001685
   Web of Science ®Google Scholar
• Yarkoni, T., & Westfall, J. (2017). Choosing prediction over explanation in psychology: Lessons from machine learning. Perspectives on Psychological Science: A Journal of the Association for Psychological Science, 12(6), 1100–1122. https://doi.org/10.1177/1745691617693393
   PubMedGoogle Scholar
• Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society Series B: Statistical Methodology, 67(2), 301–320. https://doi.org/10.1111/j.1467-9868.2005.00503.x
   Web of Science ®Google Scholar