Compared to the manually-derived model, the enumerated CTA model was 20% more parsimonious, 3.6% more accurate and 30% more efficient, and was more consistent with a priori hypotheses

Bryant, Fred B.

Holmbeck, Grayson

Millstein Coakley, Rachael

Yarnold, Paul R.

English

Loyola eCommons

Loyola University Chicago Loyola eCommons Psychology: Faculty Publications and Other Works Faculty Publications 9-17-2010 Manual vs. automated CTA: Psychosocial Adaptation in Young Adolescents with Spina Bifida Fred B. Bryant Loyola University Chicago, fbryant@luc.edu Rachael Millstein Coakley Harvard Medical School Grayson Holmbeck Loyola University Chicago, gholmbe@luc.edu Paul R. Yarnold Optimal Data Analysis, LLC Follow this and additional works at: https://ecommons.luc.edu/psychology_facpubs  Part of the Psychology Commons Recommended Citation Bryant, Fred B.; Millstein Coakley, Rachael; Holmbeck, Grayson; and Yarnold, Paul R.. Manual vs. automated CTA: Psychosocial Adaptation in Young Adolescents with Spina Bifida. Optimal Data Analysis, 1, : 55-58, 2010. Retrieved from Loyola eCommons, Psychology: Faculty Publications and Other Works, This Article is brought to you for free and open access by the Faculty Publications at Loyola eCommons. It has been accepted for inclusion in Psychology: Faculty Publications and Other Works by an authorized administrator of Loyola eCommons. For more information, please contact ecommons@luc.edu. This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License. ©data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVR42mNgYAAAAAMAASsJTYQAAAAASUVORK5CYII= Optimal Data Analysis LLC, 2010. Optimal Data Analysis    Copyright 2010 by Optimal Data Analysis, LLC 2010, Vol. 1, Release 1 (September 17, 2010) 2155-0182/10/$3.00 55 Manual vs. Automated CTA: Psychosocial Adaptation in Young Adolescents with Spina Bifida Rachael Millstein Coakley, Ph.D., Grayson N. Holmbeck, Ph.D.,   Children's Hospital, Boston / Harvard Medical School   Loyola University Chicago Fred B. Bryant, Ph.D., and Paul R. Yarnold, Ph.D.    Loyola University Chicago  Optimal Data Analysis, LLC Compared to the manually-derived model, the enumerated CTA model was 20% more parsimonious, 3.6% more accurate and 30% more efficient, and was more consistent with a priori hypotheses. A prospective study of how individual- and family-level multimethod, multi-informant attri-butes predict psychosocial adaptation (scholastic success, social acceptance, positive self-worth) in early adolescence was conducted for a sample of 68 families of children with spina bifida and 68 comparison families of healthy children.1Manually-derived CTA indicated that intrinsic motivation, estimated verbal IQ, behavioral con-duct, coping style, and physical appearance best predicted psychosocial adaptation in early ado-lescence: health status was not a factor in the model.  The model correctly classified 77.8% of the total sample, yielding ESS=55.0. An enumerated CTA model was obtain-ed by automated software for the same data used in manual analysis.2  To be consistent betweenanalyses, attributes were only  allowed to enter the model if their associated ESS was stable (did not diminish) in jackknife validity analysis.  The enumerated model is illustrated in Figure 1, and performance comparisons are given in Table 1. Behav ioralConduct:Mother ReportFamily -Lev elConf lict:Father ReportParent-ChildConf lict: ChildReportAttention:Mother,Father,TeacherReport< 3.75 > 3.75p < 0.007p < 0.006 p < 0.0001p < 0.036< 0.83 > 0.83 < 52.5 > 52.5< 1.92 > 1.92N=30N=21 N=13N=25 N=2023.1%PositiveAdaptation71.4%PositiveAdaptation88.0%PositiveAdaptation30.0%PositiveAdaptation16.7%PositiveAdaptation  Figure 1: Enumerated CTA Model Predicting Psychosocial Adaptation in Young AdolescenceOptimal Data Analysis     Copyright 2010 by Optimal Data Analysis, LLC 2010, Vol. 1, Release 1 (September 17, 2010)  2155-0182/10/$3.00    56  Table 1: Comparing Performance of Manually-Derived vs. Enumerated CTA Models ------------------------------------------------------------------------------------------------------------------------------                                            Predicted Class Status                              Predicted Class Status                                              Manual CTA Model                              Enumerated CTA Model                                          Non-Positive      Positive                          Non-Positive      Positive                                           Adaptation      Adaptation                        Adaptation      Adaptation                  Non-Positive            40                   16           71.4                   49                     9            84.5 Actual       Adaptation Class Status        Positive                   10                    51           83.6                   14                   37            72.6                  Adaptation                                                 80.0                76.1                                  77.8                80.4            Total N Classified                      117                                                          109                        PAC (%)                      77.8                                                         78.9                     Model ESS                     55.0                                                         57.0     Number of Attributes                        5                                                              4           Model Efficiency                     11.0                                                         14.3 ------------------------------------------------------------------------------------------------------------------------------ Note: Values given to the right of the Positive Adaptation columns are the specificity (for non-positive adaptation) and sensi-tivity (for positive adaptation), and values given under the Positive Adaption row, beneath columns, are the negative (for non-positive adaptation) and positive (for positive adaptation) predictive values.3  Total N classified varies as a function of missing data.  PAC=percentage accuracy in classification=100% x (sum of correctly classified observations)/(total N classi-fied).3  ESS=effect strength for sensitivity, a normed index on which 0 is the level of classification accuracy that is expected by chance, and 100 is perfect accuracy.3  The number of attributes in the CTA model is given, and model efficiency is de-fined as model ESS divided by number of attributes; is expressed in terms of mean ESS-units-per-attribute; and is a measure of the mean level of explanatory power per attribute which is used in the model—commonly, as “bang-for-the-buck”.3    The enumerated model used four attribu-tes rather than five as used in the manual model, and thus it was 80% as complex, or 20% more parsimonious, than the manually-derived model.  Compared to the manual model the enumerated model yielded greater ESS (3.6%), PAC (1.4%), efficiency (30%), specificity (18.3%), and posi-tive predictive value (5.7%).  In contrast, the manual model had greater sensitivity (15.2%) and negative predictive value (2.8%) than the enumerated model. The enumerated model predicted 80.4% accurately that 42.2% of the sample would have a positive adaptation, and identified 72.6% of all subjects experiencing positive adaptation.  And, the enumerated model predicted 77.8% accu-rately that 57.8% of the sample would have a non-positive adaptation, identifying 84.5% of all subjects experiencing non-positive adaptation. The size of sample strata identified by the enumerated model is relatively homogene-ous: the largest strata (N=30, 27.5% of classi-fied sample) is 1.3-times larger than the smallest strata (N=13, 11.9% of classified sample).  And, Optimal Data Analysis     Copyright 2010 by Optimal Data Analysis, LLC 2010, Vol. 1, Release 1 (September 17, 2010)  2155-0182/10/$3.00    57  all of the attributes loading in the model influ-enced the classification decisions which were made for a substantial portion of the sample.  The percentage of observations classified in part on the basis of their score on the attribute was: Behavioral Conduct (100% of sample); Family-Level Conflict (58.7%), Attention (41.3%) and Parent-Child Conflict (31.2%). The automated CTA model has several important similarities to the manually-derived CTA model.  First, as with the manual model, neither health status (spina bifida vs. able-bod-ied) nor socioeconomic status emerged as fac-tors in the automated model.   This suggests that both CTA models were able to identify factors that were more predictive of psychosocial ad-aptation than the group differences often identi-fied in pediatric research.   Second, the factor “behavioral conduct in the classroom” emerged as being highly significant in both models.   This demonstrates consistency between the models and reinforces the relationship between behavioral control in the classroom and psycho-social adaptation. There were also important differences between the two models.  Counter to our origi-nal hypotheses, the manually derived model did not identify any family-level variables, nor did it include any variables based on mother or father report.  In contrast, the automated CTA model supported our original hypothesis by identifying two family-level variables in the model and including three variables based in part on mother and father report.  Another dif-ference between the two models is that in the manual model all of the factors were based on characteristics of the child and two of the factors represented more internalized child qualities (i.e., intrinsic motivation, coping style).  In comparison, only half of the automated model focused on child factors and these included only externalized or observable behaviors (i.e., con-duct, attention).  In summary, the automated model pre-sents a more parsimonious way of classifying this sample and supports the researchers’ origi-nal hypotheses by including family-level factors and information from multiple informants (par-ents, teachers, child).  However, it identifies a substantially different constellation of factors in the classification of psychosocial adaptation as compared to the manual model.  Many theoreti-cally important factors that emerged in the man-ual model that are well supported in pediatric research on psychosocial adaptation (e.g., moti-vation, IQ, coping style, and attractiveness) were not included in the automated model.   In-stead, the automated model selected a narrower constellation of factors that was highly focused on behavioral presentation and family-level con-flict.  These models likely represent two theo-retically viable and empirically supported paths to psychosocial adaptation. References 1Coakley RM, Holmbeck GN, Bryant FB.  Con-structing a prospective model of psychosocial adaptation in young adolescents with spina bifi-da: an application of optimal data analysis.  Journal of Pediatric Psychology 2006, 31:1084-1099. 2Yarnold PR. Soltysik RC.  Manual vs. auto-mated CTA: optimal preadmission staging for inpatient mortality from Pneumocystis carinii pneumonia.  Optimal Data Analysis 2010, 1:50-54. 3Yarnold PR, Soltysik RC. Optimal data anal-ysis: a guidebook with software for Windows. APA Books, Washington, DC, 2005. Author Notes  Completion of this manuscript was sup-ported in part by research grants from the March of Dimes Birth Defects Foundation (12-FY01-0098) and the National Institute of Child Health and Human Development (RO1 HD048629).  The authors wish to thank Ann Walsh Johnson, Joy Ito, Pat McGovern, Pat Braun, Caroline Optimal Data Analysis     Copyright 2010 by Optimal Data Analysis, LLC 2010, Vol. 1, Release 1 (September 17, 2010)  2155-0182/10/$3.00    58  Anderson, David McLone, John Lubicky, the Spina Bifida Association of Illinois, the staff of the spina bifida clinics at Children’s Memorial Hospital, Shriners Hospitals for Children, Chi-cago, and Loyola University of Chicago Medi-cal Center.  We also thank numerous under-graduate and graduate research assistants for help with data collection and data entry.  Most importantly, we gratefully acknowledge the contributions to this study by the parents, chil-dren, teachers, and health professionals who participated over many years.  Correspondence: Rachael M. Coakley, Children's Hospital Bos-ton, Pain Treatment Service, 333 Longwood Ave, 5th Floor, Boston, MA 0215.  eMail: rachael.coakley@childrens.harvard.edu.   

Manual vs. automated CTA: Psychosocial Adaptation in Young Adolescents with Spina Bifida

https://ecommons.luc.edu/cgi/viewcontent.cgi?article=1066&context=psychology_facpubs

Manual vs. automated CTA: Psychosocial Adaptation in Young Adolescents with Spina Bifida

Abstract

Similar works

Full text

Available Versions

Loyola eCommons