Introduction Neuropsychological Symptoms Scale

1. Applying the Generalized Graded Unfolding Model to the Mood Factor of the Neuropsychological Symptoms Scale Steven Malm, W. Holmes Finch, Jacob Lutz, Raymond S. Dean • Results and Discussion • Results • Item Location: • Categories: 0=“Most of the time”, 1=“From time to time”, 2=“Very little of the time”, 3=“Not at all” • Category Probability Function (CPF): Shows the probability of providing a given response for theta level (level of latent trait) • Item Characteristic Curve (ICC): Summarizes the relationship between the individual item response values and thetausing a single curve. • Example (See figures): “Getting to sleep is a problem for me” shows a more gradual pattern from no endorsement to high endorsement, whereas “It is more difficult for me to deal with the demands of daily life than it once was” experiences a high level of no endorsement for more values of theta before any level of endorsement is seen, suggesting it is a more severe symptom. • History/Medication Status: • Those with a personal/family history of mood disorders had a higher mean level of mood dysfunction (F1,1135=163.613, p<0.001) • Those with a history of taking medication for mood disorders also had a higher mean level of mood dysfunction (F1,1135=8.192, p=0.004) • Test Information Function: The mood section of the NSS provides the greatest information for estimates of q between approximately 1 and 2.5. • Test Characteristic Curve: Respondents with thetabelow 0 (the lower half of the distribution) are likely to have sum scores above. However, as thetaincreases beyond 0, the total sum score declines fairly rapidly. Individuals with raw scores less than 60 will likely be 1 standard deviation or more above the average level of mood dysfunction. • Discussion • The results of the present study support the general use of mood scale of the NSS and the GGUM for analyzing it. Most of the items were shown to have the ability to accurately differentiate individuals based on their level of mood dysfunction. Overall, the GGUM proved to be a useful tool for analyzing the performance of a scale like the NSS, in which the relationship between the latent trait and item endorsement is not monotonically increasing. The ICCs and CPFs provided by GGUM proved helpful in determining at what level of theta was a certain response most likely to occur. Finally, the estimates of the latent trait for individuals in the sample can be useful for ascertaining whether specific groups differ on the latent trait of interest. In this instance, we were able to compare the mean thetavalues between those with a history of mood disorders and those without. We hope that researchers can readily see applications of the GGUM to their own work, making use of many of the same tools that we have demonstrated here. Introduction Neuropsychological Symptoms Scale The Neuropsychological Symptoms Scale (NSS; Dean, 2010) was designed for use in the clinical interview to assess a wide range of psychological functioning. The NSS is a revision of the Ball Neuropsychological Symptom Inventory (Dean, 1982), and consists of 100 items presented in a 4-point likert scale format ranging from 1 (“Most of the time”) to 4 (“Not at all”), asking respondents about specific neurological, cognitive, and behavioral functions. While its validity and reliability have been investigated, the psychometric performance of individual items on the NSS has not bee widely studied to date. This information could prove useful in determining the unique profile for each patient. The present study focuses on the mood factor of the NSS, which consists of 28 items assessing symptoms associated with Depression, Anxiety, and Non-specific mood disorders. This factor was chosen due to the pervasive nature of mood symptoms in the general population. Generalized Graded Unfolding Model Item Response Theory (IRT) is typically used to investigate performance of individual items. IRT assumes monotonicity – having more of the latent trait results in greater likelihood of endorsing the item. However, an examination of the NSS has revealed that this assumption may not hold for most of its items (Lutz, 2012). The generalized graded unfolding model (GGUM) does not make the assumption of monotonicity (Roberts, Donoghue, & Laughlin, 2000), and therefore may prove useful in performing an item level analysis on the NSS. This model links the underlying latent trait to the item response by measuring how far an individual respondent is from the location of the item. Thus, the goal of the current study is twofold: to investigate the performance of individual items on the mood factor of the NSS and to demonstrate the utility of the GGUM in doing so. Methodology Participants for the study included 1,134 individuals obtained by contacting adult students, and full time employees of a large Midwestern university. In terms of gender, the sample included 313 males, 818 females, with 3 respondents not indicating their gender. The participants’ age ranged from 17 to 76 (mean=29.05, standard deviation=14.6). Of the total sample, 15% (171) reported having received previous diagnoses of psychiatric or neurologic disorders, and 12.1% (138) reported having been or currently being on medication for a mood disorder. All subjects completed the NSS and took about 15 minutes to complete. A full GGUM was estimated using the GGUM 2004 software (Roberts, Fang, Cui, & Wang, 2006). The analysis was used to indicate for whom specific items will be most diagnostically useful and to provide information regarding the utility of the latent trait scores for differentiating individuals who report a history of mood disorders from those who do not. • References • Dean, R. S. (1982). Ball Neuropsychological Symptom Inventory (BNSI), Muncie, IN: Author. • Dean, R. S. (2010). Neuropsychological Symptom Scale. Complete Revision of the • Ball Neuropsychological Symptom Inventory (1982). Muncie: Ball State • University. • Lutz, J. T. (2012). Factor analysis and application of item response theory to the neuropsychological symptom scale (NSS). Unpublished dissertation. • Roberts, J. S., Fang, H., Cui, W. And Wang, Y. (2006). GGUM2004: A Windows-based Program to Estimate Parameters in the Generalized Graded Unfolding Model. Applied Psychological Measurement, 30,64-65. • Roberts, J.S., Donoghue, J.R., & Laughlin, J.E. (2000). A general item response theory model for unfolding unidimensionalpolytomous responses. Applied Psychological Measurement, 24, 3-32. Contact for Correspondence: whfinch@bsu.edu Presented at the Annual Conference of the Midwestern Psychological Association, 2012