Holmes Finch

Purpose, hope, and life satisfaction in three age groups

Using the Revised Youth Purpose Survey (Bundick et al., 2006), the Trait Hope Scale (Snyder et al., 1991), and the Satisfaction with Life Scale (Diener, Emmons, Larsen, & Griffin, 1985), the present study examined the relationship among purpose, hope, and life satisfaction among 153 adolescents, 237 emerging adults, and 416 adults (N = 806). Results of this cross-sectional study revealed that having identified a purpose in life was associated with greater life satisfaction at these three stages of life. However, searching for a purpose was only associated with increased life satisfaction during adolescence and emerging adulthood. Additionally, aspects of hope mediated the relationship between purpose and life satisfaction at all three stages of life. Implications of these results for effectively fostering purpose are discussed.

The MIMIC Model as a Method for Detecting DIF: Comparison With Mantel-Haenszel, SIBTEST, and the IRT Likelihood Ratio

This study compares the ability of the multiple indicators, multiple causes (MIMIC) confirmatory factor analysis model to correctly identify cases of differential item functioning (DIF) with more established methods. Although the MIMIC model might have application in identifying DIF for multiple grouping variables, there has been little examination of how well the technique works in terms of correct and incorrect identification of DIF. A Monte Carlo methodology is used in this study, with manipulation of the number of items, number of examinees, differences between the mean abilities of the reference and focal groups, level of DIF contamination of the anchor items, and amount of DIF in the target item. Results indicate that the MIMIC model is effective for DIF identification for 50 items or when the two-parameter logistic model underlies the data but has a very high rate of incorrect DIF identification for 20 items with three-parameter logistic data.

Perfectionism in High-Ability Students: Relational Precursors and Influences on Achievement Motivation

The purpose of the present study was to create and test a model that (a) illustrated variables influencing the development of perfectionism, and (b) demonstrated how different types of perfectionism may influence the achievement goals of high-ability students. Using a multiple-groups path analysis, the researchers found that parenting style was related to attachment, with authoritative and permissive parenting associated with secure attachment and authoritarian and uninvolved parenting associated with insecure attachment. Attachment, in turn, was related to perfectionism, with insecure attachment associated with either self-oriented or socially prescribed perfectionism. In addition, the model then illustrated that perfectionism would influence achievement goals, with self-oriented perfectionists more likely to set mastery or performance-approach goals, and socially prescribed perfectionists more likely to set performance-approach or performance-avoidance goals. The findings of this study are interpreted in the context of the existing literature, and implications for working with high-ability perfectionistic students are discussed.

Comparison of the Performance of Nonparametric and Parametric MANOVA Test Statistics when Assumptions Are Violated

Abstract. Multivariate analysis of variance (MANOVA) is a useful tool for social scientists because it allows for the comparison of response-variable means across multiple groups. MANOVA requires that the observations are independent, the response variables are multivariate normally distributed, and the covariance matrix of the response variables is homogeneous across groups. When the assumptions of normality and homogeneous covariance matrices are not met, past research has shown that the type I error rate of the standard MANOVA test statistics can be inflated while their power can be attenuated. The current study compares the performance of a nonparametric alternative to one of the standard parametric test statistics when these two assumptions are not met. Results show that when the assumption of homogeneous covariance matrices is not met, the nonparametric approach has a lower type I error rate and higher power than the most robust parametric statistic. When the assumption of normality is untenable, th...

Performance of DIMTEST-and NOHARM-Based Statistics for Testing Unidimensionality.

This Monte Carlo study compares the ability of the parametric bootstrap version of DIMTEST with three goodness-of-fit tests calculated from a fitted NOHARM model to detect violations of the assumption of unidimensionality in testing data. The effectiveness of the procedures was evaluated for different numbers of items, numbers of examinees, correlations between underlying ability dimensions, skewness of underlying ability distributions, and the presence or absence of a guessing parameter. In the absence of guessing, DIMTEST and the NOHARM-based statistics had similar power, with the χ2 statistic having a very low Type I error rate. In the presence of guessing, however, two of the NOHARM-based statistics had unacceptably high Type I error rates, while the third performed similarly to DIMTEST. Given this inflated error rate, the study compares the empirical powers after adjusting for the discrepancy in Type I error rates.

Differential Item Functioning Analysis for Accommodated Versus Nonaccommodated Students

The No Child Left Behind act resulted in an increased reliance on large-scale standardized tests to assess the progress of individual students as well as schools. In addition, emphasis was placed on including all students in the testing programs as well as those with disabilities. As a result, the role of testing accommodations has become more central in discussions about test fairness and accessibility as well as evidence of validity. This study seeks to examine whether there exists differential item functioning for math and language items between special education examinees receiving accommodations and those not receiving accommodations.

Item Parameter Estimation for the MIRT Model: Bias and Precision of Confirmatory Factor Analysis-Based Models

The accuracy of item parameter estimates in the multidimensional item response theory (MIRT) model context is one that has not been researched in great detail. This study examines the ability of two confirmatory factor analysis models specifically for dichotomous data to properly estimate item parameters using common formulae for converting factor loadings and thresholds to discrimination and difficulty indices. The two MIRT estimation methods included in this research, unweighted least squares (ULS) and robust weighted least squares (RWLS), and the unidimensional estimation approach used are accessible in the widely distributed software packages NOHARM, Mplus, and BILOGMG, respectively. These techniques have been assessed in terms of the overall accuracy, bias, and standard error of item parameter estimates under a variety of sample sizes, test lengths, intertrait correlations, pseudo-guessing, and latent trait distribution conditions. Results indicate that there exists a complex relationship between these manipulated factors and the estimation accuracy of these methods. Recommendations for practice in light of these results are provided.

Multidimensional Item Response Theory Parameter Estimation With Nonsimple Structure Items

Estimation of multidimensional item response theory (MIRT) model parameters can be carried out using the normal ogive with unweighted least squares estimation with the normal-ogive harmonic analysis robust method (NOHARM) software. Previous simulation research has demonstrated that this approach does yield accurate and efficient estimates of item discrimination and difficulty across a variety of conditions. However, these studies have been limited primarily to the case of simple structure, where each item is associated only with one of the latent traits underlying the data. The current simulation study seeks to extend this earlier work by comparing NOHARM and unidimensional IRT-based estimates of difficulty and discrimination values for items that do not conform to simple structure, that is, are associated with more than one latent trait. The outcomes of interest were relative bias and standard error values for parameter estimates under a variety of conditions, including the degree of nonsimple structure present. Results demonstrate that both bias and standard error tended to be larger for items that do not conform to simple structure than for those that do, but that the degree of such differences was influenced by factors such as correlation between the latent traits, sample size and distribution of the latent trait, among others.

The Use of Multiple Imputation for Missing Data in Uniform DIF Analysis: Power and Type I Error Rates

Methods of uniform differential item functioning (DIF) detection have been extensively studied in the complete data case. However, less work has been done examining the performance of these methods when missing item responses are present. Research that has been done in this regard appears to indicate that treating missing item responses as incorrect can lead to inflated Type I error rates (false detection of DIF). The current study builds on this prior research by investigating the utility of multiple imputation methods for missing item responses, in conjunction with standard DIF detection techniques. Results of the study support the use of multiple imputation for dealing with missing item responses. The article concludes with a discussion of these results for multiple imputation in conjunction with other research findings supporting its use in the context of item parameter estimation with missing data.

Classification Accuracy of Neural Networks vs. Discriminant Analysis, Logistic Regression, and Classification and Regression Trees

Abstract. This paper compares the predictive accuracy of three commonly used parametric methods for group classification, linear discriminant analysis, quadratic discriminant analysis, and logistic regression, with two less common approaches, neural networks and classification and regression trees. The simulation study examined the impact of such factors as inequality of covariance matrices, distribution of predictors, and group size ratio (among others) on the performance of each method. Results indicate that quadratic discriminant analysis always performs as well as the other methods while neural networks behave very similarly to linear discriminant analysis and logistic regression.