The goals of this study are to describe the importance of developing a measure of sibling group cohesion, to define this measure, to test the validity of the measure using similar constructs, and to explore how sibling group cohesion predicts perceived personal achievement. Sibling group cohesion is defined as an individual’s voluntary commitment to one’s group of siblings, which forms an open unit. A 12-item scale of adult sibling group cohesion is developed and validated. Adult siblings from sibling groups of 3 or more were asked to take an online survey and 541 participants from 184 families completed the survey. Three theories are proposed for how sibling group cohesion could impact achievement: support, expectations, and shared identity theories. Results indicate that sibling group cohesion is related to, but still unique from, the average and standard deviation of dyadic sibling relationship positivity quality. Individuals from larger families, who have a high proportion of siblings who inspire them, and who have high and consistently positive dyadic relationships report having high sibling group cohesion. Additionally, results from this study show sibling group cohesion is a strong positive predictor of two measurements of perceived personal achievement. The predictive power of sibling group cohesion is stronger than that of the average of dyadic sibling relationship positivity, and is mediated by a combination of support, average dyadic positivity, and demographic variables. Specifically, receiving active and emotional support, as well as being introduced to activities by a majority of one’s siblings is predictive of better achievement. / text

The multilevel model (MLM) is easily parameterized to handle partially clustered data (see, for example, Baldwin, Bauer, Stice, & Rohde, 2011). The current study evaluated the performance of this model under various departures from underlying assumptions, including assumptions of normally distributed and homoscedastic Level 1 residuals. Two estimating models – one assuming homoscedasticity, the other allowing for the estimation of unique Level 1 variance components – were compared. Results from a Monte Carlo simulation suggest that the MLM for partially clustered data yields consistently unbiased parameter estimates, except for an underestimation of the Level 2 variance component under heteroscedastic generating conditions. However, this negative parameter bias desisted when the MLM allowed for Level 1 heteroscedasticity. Standard errors for variance component estimates at both levels were underestimated in the presence of nonnormally distributed Level 1 residuals. A discussion of results, as well as suggestions for future research, is provided. / text

The present study investigated estimation of the variance of the cross-classified factors’ random effects’ interaction for cross-classified data structures. Results for two different three-level cross-classified random effects model (CCREM) were compared: Model 1 included the estimation of this variance component and Model 2 assumed the value of this variance component was zero and did not estimate it. The second model is the model most commonly assumed by researchers utilizing a CCREM to estimate cross-classified data structures. These two models were first applied to a real world data set. Parameter estimates for both estimating models were compared. The results for this analysis served as a guide to provide generating parameter values for the Monte Carlo simulation that followed. The Monte Carlo simulation was conducted to compare the two estimating models under several manipulated conditions and assess their impact on parameter recovery. The manipulated conditions included: classroom sample size, the structure of the cross-classification, the intra-unit correlation coefficient (IUCC), and the cross-classified factors’ variance component values. Relative parameter and standard error bias were calculated for fixed effect coefficient estimates, random effects’ variance components, and the associated standard errors for both. When Model 1 was used to estimate the simulated data, no substantial bias was found for any of the parameter estimates or their associated standard errors. Further, no substantial bias was found for conditions with the smallest average within-cell sample size (4 students). When Model 2 was used to estimate the simulated data, substantial bias occurred for the level-1 and level-2 variance components. Several of the manipulated conditions in the study impacted the magnitude of the bias for these variance estimates. Given that level-1 and level-2 variance components can often be used to inform researchers’ decisions about factors of interest, like classroom effects, assessment of possible bias in these estimates is important. The results are discussed, followed by implications and recommendations for applied researchers who are using a CCREM to estimate cross-classified data structures. / text

The purpose of the simulation was to assess the impact of weight pattern assignment when using the multiple membership random effects model (MMREM). In contrast with most previous methodological research using the MMREM, mobility was not randomly assigned; rather the likelihood of student mobility was generated as a function of the student predictor. Two true weights patterns were used to generate the data (random equal and random unequal). For each set of generated data, the true correct weights and two incorrect fixed weight patterns (fixed equal and fixed unequal) that are similar to those used in practice by applied researchers were used to estimate the model. Several design factors were manipulated including the percent mobility, the ICC, and the true generating values of the level one and level two mobility predictors. To assess parameter recovery, relative parameter bias was calculated for the fixed effects and random effects variance components. Standard error (SE) bias was also calculated for the standard errors estimated for each fixed effect. Substantial relative parameter bias differences between weight patterns used were observed for the level two school mobility predictor across conditions as well as the level two random effects variance component, in some conditions. Substantial SE bias differences between weight patterns used were also found for the school mobility predictor in some conditions. Substantial SE and parameter bias was found for some parameters for which it was not anticipated. The results, discussion, future directions for research, and implications for applied researchers are discussed.

This study was designed to investigate the impact of multiple-membership data structures in multilevel modeling. Multiple-membership arises when lower level units (e.g., students) are nested within more than one higher level unit (e.g., schools). In this case, more than one school will contribute to students' academic achievement and progress. In reality, it is inappropriate to assume a pure nesting of a student within a single school. While use of HLM requires either deletion of the cases involving multiple-membership or exclusion of prior schools attended, MMREM includes students who attend multiple schools and controls for the effect of all schools on student outcomes. The simulation study found level two variability underestimation and corresponding level one variability overestimation when multiple membership data structures were ignored. The study also revealed that when HLM failed to include multiple membership data structures, it underestimated school level predictor. With an increased numbers of mobile students under the No Child Left Behind (NCLB) Act, researchers need to understand MMREM and correctly apply it to multiple membership data structures. This MMREM approach will help improve the generalizability of findings and will improve the validity of the statistical results. / text

abstract: Missing data are common in psychology research and can lead to bias and reduced power if not properly handled. Multiple imputation is a state-of-the-art missing data method recommended by methodologists. Multiple imputation methods can generally be divided into two broad categories: joint model (JM) imputation and fully conditional specification (FCS) imputation. JM draws missing values simultaneously for all incomplete variables using a multivariate distribution (e.g., multivariate normal). FCS, on the other hand, imputes variables one at a time, drawing missing values from a series of univariate distributions. In the single-level context, these two approaches have been shown to be equivalent with multivariate normal data. However, less is known about the similarities and differences of these two approaches with multilevel data, and the methodological literature provides no insight into the situations under which the approaches would produce identical results. This document examined five multilevel multiple imputation approaches (three JM methods and two FCS methods) that have been proposed in the literature. An analytic section shows that only two of the methods (one JM method and one FCS method) used imputation models equivalent to a two-level joint population model that contained random intercepts and different associations across levels. The other three methods employed imputation models that differed from the population model primarily in their ability to preserve distinct level-1 and level-2 covariances. I verified the analytic work with computer simulations, and the simulation results also showed that imputation models that failed to preserve level-specific covariances produced biased estimates. The studies also highlighted conditions that exacerbated the amount of bias produced (e.g., bias was greater for conditions with small cluster sizes). The analytic work and simulations lead to a number of practical recommendations for researchers. / Dissertation/Thesis / Doctoral Dissertation Psychology 2015

abstract: The comparison of between- versus within-person relations addresses a central issue in psychological research regarding whether group-level relations among variables generalize to individual group members. Between- and within-person effects may differ in magnitude as well as direction, and contextual multilevel models can accommodate this difference. Contextual multilevel models have been explicated mostly for cross-sectional data, but they can also be applied to longitudinal data where level-1 effects represent within-person relations and level-2 effects represent between-person relations. With longitudinal data, estimating the contextual effect allows direct evaluation of whether between-person and within-person effects differ. Furthermore, these models, unlike single-level models, permit individual differences by allowing within-person slopes to vary across individuals. This study examined the statistical performance of the contextual model with a random slope for longitudinal within-person fluctuation data. A Monte Carlo simulation was used to generate data based on the contextual multilevel model, where sample size, effect size, and intraclass correlation (ICC) of the predictor variable were varied. The effects of simulation factors on parameter bias, parameter variability, and standard error accuracy were assessed. Parameter estimates were in general unbiased. Power to detect the slope variance and contextual effect was over 80% for most conditions, except some of the smaller sample size conditions. Type I error rates for the contextual effect were also high for some of the smaller sample size conditions. Conclusions and future directions are discussed. / Dissertation/Thesis / Doctoral Dissertation Psychology 2016

In single-case research, multiple-baseline (MB) design is the most widely used design in practical settings. It provides the opportunity to estimate the treatment effect based on not only within-series comparisons of treatment phase to baseline phase observations, but also time-specific between-series comparisons of observations from those that have started treatment to those that are still in the baseline. In MB studies, the average treatment effect and the variation of these effects across multiple participants can be estimated using various statistical modeling methods. Recently, two types of statistical modeling methods were proposed for analyzing MB studies: a) within-series model and b) between-series model. The within-series model is a typical two-level multilevel modeling approach analyzing the measurement occasions within a participant, whereas the between-series model is an alternative modeling approach analyzing participants’ measurement occasions at certain time points, where some participants are in the baseline phase and others are in the treatment phase. Parameters of both within- and between-series models are generally estimated with restricted maximum likelihood (ReML) estimation and ReML is developed based on the assumption of normality (Hox, et al., 2010; Raudenbush & Bryk, 2002). However, in practical educational and psychological settings, observed data may not be easily assumed to be normal. Therefore, the purpose of this study is to investigate the robustness of analyzing MB studies with the within- and between-series models when level-1 errors are non-normal. A Monte Carlo study was conducted under the conditions where level-1 errors were generated from non-normal distributions in which skewness and kurtosis of the distribution were manipulated. Four statistical approaches were considered for comparison based on theoretical and/or empirical rationales. The approaches were defined by the crossing of two analytic decisions: a) whether to use a within- or between-series estimate of effect and b) whether to use REML estimation with Kenward-Roger adjustment for inferences or Bayesian estimation and inference. The accuracy of parameter estimation and statistical power and Type I error were systematically analyzed. The results of the study showed the within- and between-series models are robust to the non-normality of the level-1 error variance. Both within- and between-series models estimated the treatment effect accurately and statistical inferences were acceptable. ReML and Bayesian estimations also showed similar results in the current study. Applications and implications for applied and methodology researchers are discussed based on the findings of the study.

Perfectionistic standards are ubiquitous features conveyed in several aspects of life. Although some aspects of perfectionism may be beneficial to promote achievement, continuously targeting perfection and flawlessness has been shown to impede on one’s psychological adjustment, motivation, and self-regulation (Hewitt & Flett, 1991). Essentially, there still exists no consensus among researchers to identify whether perfectionism—or at least, some facets of perfectionism— is likely to promote or undermine positive outcomes (e.g., Gotwals, Stoeber, Dunn, & Stoll, 2012). The 2 × 2 model of perfectionism (Gaudreau, 2012; Gaudreau & Thompson, 2010) is a welcome addition for researchers studying perfectionism because it proposes an open-ended theoretical system in which novel hypotheses are amenable to empirical scrutiny, thus offering leeway for researchers to theorize and reinterpret those past mixed findings. The overarching goal of this dissertation was to address some of the gaps of the perfectionism literature in order to better understand under which circumstances perfectionistic standards are useful to foster achievement without thwarting psychological adjustment. Accordingly, the current dissertation used the 2 × 2 model of perfectionism as theoretical framework to propose four original studies regrouped under three articles. In Article 1, we aimed at providing a multilevel extension of the 2 × 2 model in order to better understand how the relationships between subtypes of perfectionism and indicators of positive and negative psychological adjustment may vary according to the level of analysis that is being studied. In other words, in this study, we examined the within-person relationships between subtypes of perfectionism and psychological adjustment (i.e., accounting for the fact that these relationships may vary within each person from one life domain to another) in complement to the between-person relationships (i.e., accounting for individual differences across people). A sample of 338 undergraduate students completed measures of perfectionism, vitality, goal progress, affect, and stress for each life domain in which they reported being invested. Preliminary analyses of multilevel confirmatory factor analysis supported the multilevel factorial structure of our measure. Furthermore, results of multilevel regressions with random coefficient supported most hypotheses of the model with positively-, but not negatively-worded outcomes, deserving further discussion. In an attempt to better understand these unexpected yet interesting findings, Article 2 aimed at extending the findings of Article 1 by examining the multilevel associations between subtypes of perfectionism and coping strategies of undergraduate students. Two studies were conducted to examine the between- and within-person relationships respectively. Accordingly, 332 undergraduate students completed measures to assess their dispositional perfectionism and coping tendencies in Study 1 (i.e., between-person). In Study 2, 203 undergraduate students completed repeated measures of perfectionism and coping for each life domain in which they reported being invested (i.e., within-person). Results of multiple regressions from Study 1 (i.e., between-person) showed similar findings than those obtained in past research with task- and disengagement-oriented coping, and support of all four hypotheses was obtained with relative coping (i.e., proportion of task-oriented compared to one’s overall coping). Results of multilevel regressions with random coefficient from Study 2 (i.e., within-person) provided support for all hypotheses with disengagement-oriented coping, two hypotheses with task-oriented coping, and three hypotheses with relative coping. Finally, in Article 3, we aimed at identifying the potential role of moderators in the 2 × 2 model of perfectionism, particularly the role of sociocultural identity. A sample of 697 undergraduate students (538 Euro Canadians and 159 Asian Canadians) completed measures aimed at assessing perfectionism and indicators of school achievement (i.e., satisfaction and grade-point average). Preliminary multi-group confirmatory factor analyses with invariance testing supported the factorial structure of our measure across both samples, thus rendering the measure equivalent across both sociocultural groups. Furthermore, results provided support for our socially prescribed perfectionism as a cultural makeup hypothesis, suggesting that Asian Canadians with a subtype of mixed perfectionism (i.e., high self-oriented and high socially prescribed perfectionism)—in contrast to their Euro Canadians counterparts—were able to reach both the achievement and satisfaction targets known to play an important part in the positive academic experience of students. Overall, the current dissertation bears significant theoretical implications by providing further validation of the 2 × 2 model of perfectionism, as well as supporting a multilevel and cross-cultural extension. It also holds methodological contributions by supporting the factorial invariance of the short-Multidimensional Perfectionism Scale across levels of analysis and sociocultural groups. Furthermore, this dissertation involves practical implications for clinical psychologists by underlining the need to compare clients to their own average across significant domains of their life (e.g., to monitor their progress or areas of concern) along to the normative standards designed to compare them with individuals (e.g., to monitor their levels in comparison to the population).

Multiple criteria systems are recommended as best practice to identify culturally, linguistically, economically diverse students for gifted services, in which schools often incorporate measures of creativity. However, the role of creativity in identification systems and its recruitment of diverse student populations is unclear. The Torrance Test of Creative Thinking (TTCT) is the most widely used norm-referenced creativity test in gifted identification. Although commonly used for identifying talent, little is known on the variability in composite scores on the TTCT-Figural and student demographics (i.e., race/ethnicity, sex, socioeconomic status, English language learning status). This study evaluated student demographic subgroup differences that exist after the initial phase of an identification process (i.e., universal screening, referrals) and examined the relationship among student demographics (i.e., race/ethnicity, free/reduced lunch status, English language learning status, sex), cognitive ability, academic achievement, and creativity, as measured by the TTCT-Figural Form A or B, to the probability of being identified for gifted programs. In a midsized school district in the state of Texas, findings indicate several demographic differences for students who were referred or universally screened across the measures of cognitive ability, academic achievement, and creativity. However, there were lower differences when using the TTCT-Figural. Results of a hierarchical generalized linear regression indicate underrepresented groups showed no difference in the probability of being identified after controlling for measures of cognitive ability, academic achievement, and creativity. Though, cognitive ability and academic achievement tests were more predictive of identification compared to the TTCT-Figural. Implications and recommendations for future research are discussed.