%202021%20-%20Statistical%20Psychological%20Services%20-%20All%20rights%20reserved.jpg)
Papers and Resources
A selection of papers including Mplus, SPSS, and MLwiN code examples for researchers and students.
Little, J. (2013). Multilevel confirmatory ordinal factor analysis of the Life Skills Profile–16. Psychological Assessment, 25, 810–825. doi:10.1037/a0032574 (this paper was awarded the Korten Prize at The Australian National University in 2013)
Metcalf, O., Little, J., Cowlishaw, S., Varker, T., Arjmand, H. A., O’Donnell, M. L., . . . Forbes, D. (2021). Modelling the relationship between poor sleep and problem anger in veterans: A dynamic structural equation modelling approach. Journal of Psychosomatic Research,150, 110615. doi: 10.1016/j.jpsychores.2021.110615
Gibson, K. E., Little, J., Cowlishaw, S., Toromon, T., & O’Donnell, M. L. (2021). Piloting a scalable, post-trauma psychosocial intervention in Tuvalu: The Skills fOr Life Adjustment and Resilience (SOLAR) Program. European Journal of Psychotraumatology, 12 (1), 1948253. doi: 10.1080/20008198.2021.1948253
Havighurst, S. S., Mangelsdorf, S. N., Boswell, N., Little, J., Zhang, A., Gleeson, K., … Kehoe, C. (2024, March 15). An online emotion socialization intervention for parents of children with challenging behavior: Tuning in to Kids OnLine (TIKOL). doi.org/10.31234/osf.io/5432d
Cowlishaw, S., Metcalf, O., Little, J., Hinton, M., Forbes, D., Varker, T. . .O’Donnell, M. L. (2021). Cross-lagged analyses of anger and PTSD among veterans in treatment. Psychological Trauma: Theory, Research, Practice, and Policy, Aug 26. doi:10.1037/tra0001084 Epub ahead of print.
​
Cousins, A., Albrecht, S., & Little, J. (2015). Feedback quality influence on trust in supervisor, work engagement, job satisfaction, organisational commitment and feedback seeking: Test of a model. Paper presented to the APS 11th Industrial and Organisational Psychology Conference. Melbourne, Victoria.
​
​
Little, J. (2007). Confirmatory and exploratory factor analysis of the Westerman Aboriginal Symptom Checklist - Youth Form. In T. G. Westerman (Ed). The development of a comprehensive assessment process for Aboriginal youth (aged 13 to 17 years) at risk of depression, suicidal behaviours and anxiety: The Westerman Aboriginal Symptom Checklist for Youth Manual (2nd ed.). Victoria Park, Western Australia: Indigenous Psychological Services, pp. 326–347.
Preston, N. J., & Little, J. (2009). Predictive and construct validity evaluation of the Zero Scale: Examining a measure of employee suitability in safety critical environments (Technical Report for Sentis Australia). West Leederville, Western Australia: PsyOpus.
​
​
and many others . . .
O’Donnell, M. L., Lau, W., Chisholm, K., Agathos, J., Little, J., Terhaag, S.,. . .Gallagher, M. W. (2021). A pilot study of the efficacy of the unified protocol for transdiagnostic treatment of emotional disorders in treating posttraumatic psychopathology: A randomized controlled trial. Journal of Traumatic Stress. doi:10.1002/jts.22650
Miscellaneous programs and calculators
A selection of handy programs and calculators for researchers and students - free for non-commercial use with acknowledgement. These programs and calculators are licensed under an Attribution-NonCommercial-ShareAlike 4.0 International.
JRule for Mplus & EQS - SPSS Program - Version 2.1 (2024). Power test for Modification Index (MI) and Expected Parameter Change Statistic (EPC). Prioritizes parameters for modification in covariance structure analysis. Also applies Hancock's Scheffe-type I error adjustment and a Holm-Bonferroni sequential type adjustment. Works with Mplus output. Based on the work of
​
Saris, W., E., Satorra, A., & van der Veld, W. M. (2009). Testing structural equation models or detection of misspecifications? Structural Equation Modeling: A Multidisciplinary Journal, 16, 561–582. doi:
​
Van der Veld, W., Saris, W., & Satorra, A. (2008). JRule 3.0: User's Guide.
​
Hancock, G. R. (1999). A sequential Scheffe-type respecification procedure for controlling type I error in exploratory structural equation model modification. Structural Equation Modeling: A Multidisciplinary Journal, 6, 158–168. doi: 10.1080/10705519909540126
​
Degrees of freedom calculator based on the work of
​
Ridgeon, E. (1994). Calculating degrees of freedom for a structural model. Structural Equation Modeling: A Multidisciplinary Journal, 1, 274–278.
Missing data handling: Pooled likelihood ratio test for multiple imputation for spss based on the work of
​
Li, K. H., Meng, X. L., Raghunathan, T. E., & Rubin, D. B. (1991). Significance levels from repeated p-values and multiply imputed data. Statistica Sinica, 1, 65–92. doi: 10.2307/2290525
Missing data handling: Pooling multiple imputed datasets from Mplus, Blimp etc and other programs into single stacked spss datafile.
Construct reliability for higher-order scales under alternative factor identification rules. Based on the work of
​
Raykov, T., & Marcoulides, G. A. (2012). Evaluation of validity and reliability for hierarchical scales using latent variable modeling. Structural Equation Modeling: A Multidisciplinary Journal, 19 (3), 495–508.
SPSS calculator for power for testing of fit on the basis of the RMSEA for covariance structure models. Based on the work of:
​
MacCallum, R. C., Browne, M. W., Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1, 130–149.
Power that a given parameter is different from zero at a chosen sample size. Power plotted by a range of user defined sample sizes - SPSS program. Based on the Mplus guide for power: https://www.statmodel.com/power.shtml
SPSS calculator for conversion of logit to odd to probability
SPSS calculator (Advanced) for the efficacy of a screening test at a chosen prevalence. Using True Positive, True Negative, False Positive and False Negative case counts, Prevalence and Alpha level as user inputs this program calculates Sensitivity, Specificity, Diagnostic Accuracy, Positive Likelihood Ratio, Negative Likelihood Ratio, Positive Predictive Value, and Negative Predictive Value. Confidence intervals are also provided for most estimates. Based on the work of:
​
Mercaldo, N. D., Lau, K. F., & Zhou, X. H. (2007). Confidence intervals for predictive values with an emphasis to case–control studies. Statistics in Medicine, 26(10), 2170–2183. https://doi.org/10.1002/sim.2677.
​
Altman, D. G., Machin, D., Bryant, T. N., & Gardner, M. J. (Eds) (2000). Statistics with confidence, 2nd ed. BMJ Books.
SPSS calculator (Simplified) for the efficacy of a screening test at a chosen prevalence. Same as the Advanced version of this program but only uses Sensitivity, Specificity, and Prevalence as inputs to calculate Diagnostic Accuracy, Positive Likelihood Ratio, Negative Likelihood Ratio, Positive Predictive Value, and Negative Predictive Value. Based on the work of:
​
Mercaldo, N. D., Lau, K. F., & Zhou, X. H. (2007). Confidence intervals for predictive values with an emphasis to case–control studies. Statistics in Medicine, 26(10), 2170–2183. https://doi.org/10.1002/sim.2677.
SPSS calculator for the normalization of scores with rescaling for theoretical scale range. Normalization is a non-linear transformation and changes the shape of the score distribution - typically to a normal or bell shape. The following syntax is based on the work of that provided by Valentim R. Alferes (University of Coimbra, Portugal) valferes@fpce.uc.pt listed on www.spsstools.net. Based on the work of:
​
Guilford, J. P., & Fruchter, B. (1978). Fundamental statistics in psychology and education (6th ed.). New York: McGraw-Hill.
Scaled difference chi-square test statistic - SPSS Calculator. (Note that the robust chi-square difference test can sometimes produce a negative value. An alternative approach that avoids this is given in Satorra, A., & Bentler, P.M. (2010). Ensuring positiveness of the scaled difference chi-square test statistic. Psychometrika, 75, 243-248 and is explained on the Mplus website). The original test given below is based on the work of:
​
Satorra, A., & Bentler, P. M. (2001). A scaled difference chi-square test statistic and standard errors for the moment structure analysis. Psychometrica, 66, 507-514.
​
using an example taken from:
Bynre, B. M. (2006). Structural equation modeling with EQS: Basic concepts, applications, and
programming (2nd Ed). Mahwah, New Jersey: Lawrence Erlbaum.
Multilevel Modelling Design Effect Table and Calculator. The table shows the Design Effect for a range of ICC's BY Average Group Size and also includes a calculator. Based on the work of:
​
Muthén, B. O., & Satorra, A. (1995). Complex Sample Data in Structural Equation Modeling. Sociological Methodology, 25, 267–316. https://doi.org/10.2307/271070
​
Snijders, Tom & Bosker, Roel. (2012). Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling (2nd ed). Los Angles: Sage.
Adjust your p-values and stay fashionable. This spss program calculates the adjusted p value using the following methods: Bonferroni, Holm-Bonferroni, Benjamini–Hochberg, and the Holm-Sidak method.
Benjamini, Y., & Hochberg, Y. (1995). Controlling the False Discovery Rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B (Methodological), 57(1), 289–300.
https://doi.org/10.1111/j.2517-6161.1995.tb02031.x
Holm, S. (1979). A simple sequential rejective method procedure. Scandinavian Journal of Statistics, 6, 65–70.
Sidak, Z. (1967). Rectangular confidence regions for the means of multivariate normal distributions. Journal of the American Statistical Association, 62, 626–633.
MplusSummaryTable - Version 1.0 (2024). This R program takes output from Mplus files from a specified folder and saves these into an Excel workbook. Requires R libraries MplusAutomation and openxlsx.
MplusIRTgrapher - Version 1.0 (2025). These spss and python programs combine item characteristic curve data from Mplus plots, combines these into one data file using spss and then generates ICC plots by a chosen grid size using Phython. An extremely useful time saver as Mplus does not produce ICC plots for many items in the one graph.