#                                                                              #         
#              The Importance of Using an Optimal Cutoff Value for             # 
#                  the 10-Item Autism-Spectrum Quotient (AQ10)                 #           
#                                                                              #
#                         Psychological Assessment, 2024                       #
#                                                                              #
################################################################################         

                       **Analysis Information**      
+ Psychological_Assessment_Analysis.R:
  - Analysis script for mega-analysis dataset (Analysis_Data.csv). See script comments for further details. 

+ Analysis_Data.csv:

  + Structure:
    - 'data.frame'  : 18152 obs. of  10 variables:
    $ ppt           : int  Unique ppt ID number
    $ AQ10          : num  Total AQ10 score (0-10)
    $ DV_Raw        : num  Dependent Variable in original study units
    $ Sex           : int  0 = Female, 1 = Male, where available
    $ Age           : int  Age (years) where available
    $ Study         : chr  Study Identifier (Author_Year)
    $ Variable_Name : chr  Variable Identifier
    $ DV_Z          : num  Standardised Dependent Variable Score
    $ DV_Abs        : num  Absolute Standardised Variable Score
    $ Diagnosis     : int  0 = No Autism Diagnosis, 1 = Autism Diagnosis, where available

  + Summary Statistics:
                    | n       mean    sd     median  trimmed   mad    min     max    range   skew   kurtosis  se
    ------------------------------------------------------------------------------------------------------------------                   
    ppt             | 13692   --      --      --      --      --      --      --      --      --      --     --
    AQ10            | 13691   3.36    2.19    3.00    3.13    1.48    0.00    10.00   10.00   0.88    0.49   0.02
    Sex             | 6681    0.35    0.48    0.00    0.32    0.00    0.00    1.00    1.00    0.61   -1.63   0.01    
    Age             | 6162    34.52   14.52   31.00   32.96   14.83   13.00   83.00   70.00   0.74   -0.50   0.18
    Diagnosis       | 2425    0.31    0.46    0.00    0.27    0.00    0.00    1.00    1.00    0.80   -1.35   0.01
    Cut6            | 13691   0.16    0.36    0.00    0.07    0.00    0.00    1.00    1.00    1.90    1.62   0.00
    Cut7            | 13691   0.10    0.30    0.00    0.00    0.00    0.00    1.00    1.00    2.70    5.29   0.00


+ R sessionInfo:
    Analyses were conducted using the R Statistical language (version 4.1.0; R Core Team, 2021) on Windows 10 x64 (build 19045)
    
    Package   | Version | Reference
    ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
    boot      |  1.3.28 | Angelo Canty and Brian Ripley (2021). boot: Bootstrap R (S-Plus) Functions. R package version 1.3-28.
    caret     |  6.0.88 | Max Kuhn (2021). caret: Classification and Regression Training. R package version 6.0-88. https://CRAN.R-project.org/package=caret
    DescTools | 0.99.44 | Andri Signorell et mult. al. (2021). DescTools: Tools for descriptive statistics. R package version 0.99.44.
    dplyr     |   1.0.6 | Hadley Wickham, Romain François, Lionel Henry and Kirill Müller (2021). dplyr: A Grammar of Data Manipulation. R package version 1.0.6. https://CRAN.R-project.org/package=dplyr
    ggplot2   |   3.3.6 | H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.
    lattice   | 0.20.44 | Sarkar, Deepayan (2008) Lattice: Multivariate Data Visualization with R. Springer, New York. ISBN 978-0-387-75968-5
    lme4      |  1.1.27 | Douglas Bates, Martin Maechler, Ben Bolker, Steve Walker (2015). Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software, 67(1), 1-48. doi:10.18637/jss.v067.i01.
    Matrix    |   1.3.3 | Douglas Bates and Martin Maechler (2021). Matrix: Sparse and Dense Matrix Classes and Methods. R package version 1.3-3. https://CRAN.R-project.org/package=Matrix
    plyr      |   1.8.6 | Hadley Wickham (2011). The Split-Apply-Combine Strategy for Data Analysis. Journal of Statistical Software, 40(1), 1-29. URL http://www.jstatsoft.org/v40/i01/.
    psych     |   2.1.3 | Revelle, W. (2020) psych: Procedures for Personality and Psychological Research, Northwestern University, Evanston, Illinois, USA, https://CRAN.R-project.org/package=psych Version = 2.1.3,.
    R         |   4.1.0 | R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
    report    |   0.5.1 | Makowski, D., Ben-Shachar, M.S., Patil, I. & Lüdecke, D. (2020). Automated Results Reporting as a Practical Tool to Improve Reproducibility and Methodological Best Practices Adoption.
    Rmisc     |   1.5.1 | Ryan M. Hope (2022). Rmisc: Ryan Miscellaneous. R package version 1.5.1. https://CRAN.R-project.org/package=Rmisc
    rstatix   |   0.7.0 | Alboukadel Kassambara (2021). rstatix: Pipe-Friendly Framework for Basic Statistical Tests. R package version 0.7.0. https://CRAN.R-project.org/package=rstatix
    sjPlot    |  2.8.11 | Lüdecke D (2022). _sjPlot: Data Visualization for Statistics in SocialScience_. R package version 2.8.11, <URL:https://CRAN.R-project.org/package=sjPlot>.