The purpose of this study was to assess the effects of misspecification (i.e., under-specification, over-specification, and general misspecification) of the within-person level errors in longitudinal data under quadratic, exponential, and logistic latent growth curve models and to make a contribution to the literature in this area. A Monte Carlo simulation in R uncovered a common bias pattern throughout this study. For under-specification of autocorrelated processes and general misspecification of MA as AR, the mean intercept was more likely to be downward- biased, but the linear slope and nonlinear slope for an individual tend to be upward-biased. On average, the variation of the intercept is more likely to be underestimated for under-specification of autocorrelated errors in three types of nonlinear models, whereas the variation of linear slope variance is more often to be more overestimated, Opposite results were found for over-specification and general misspecification of AR data misspecified as MA. The covariance between intercept and linear slope was downward-biased by overspecified models but upward- biased by under-specified quadratic and exponential latent growth curve models. The covariance between intercept and logistic slope was downward-biased by over-specified models, but upward-biased by under-specified models. The reverse pattern was observed for the covariance between linear slope and logistic slope, which was underestimated by under-specified logistic latent growth curve models and overestimated by over-specified logistic LGCs. Longitudinal data studies are widely conducted, and nonlinear change over time is often the focus of such studies. Rarely, do applied researchers hypothesize, a priori, stochastic effects (in this case, AR, MA, and ARMA) because such processes are primarily nuisance conditions which are not the focus of longitudinal data studies. Nevertheless, stochastic effects are widely known to be present in such data, dating at least as far back as Box and Jenkins (1978). Applied researchers encounter stochastic effects when they are force to confront correlated errors over time. By modeling the impact of various stochastic effects on parameter estimates, applied researchers are introduced to alternative models to consider as rival hypotheses when conducting longitudinal data wherein nonlinear processes are anticipated, so as to filter stochastic processes should they occur, especially as evidenced by correlated errors so common in longitudinal data.

A regional area of a school district in central Florida used the implementation of a house system as a school improvement intervention to impact school climate and academic achievement. The purpose of this study was to determine if a house system is an effective school improvement intervention that has a positive effect on school climate and academic achievement of students in elementary schools. Four research questions were developed to investigate if there was a difference in elementary schools that implemented a house system and ones that did not during the 2018-2019 school year. All schools included in the study had 5th grade students who responded to Cognia© elementary student survey. Those responses were used to study school climate. Historical attendance and suspension data for school year 2018-2019 was also used. The scale scores from the Florida Standard Assessment (FSA) were used to measure student achievement in reading and math. Of the six schools included in this study it was found that the implementation of a house system did have an impact on suspensions and student achievement on standardized tests in reading and mathematics. There was no evidence to support the implementation of a house system having an impact on school climate and student attendance. This study adds to the literature of the impact of a house system on elementary schools. The findings of this research have implications for further research on the house system as an intervention to improve academic achievement in reading and math at elementary schools.

In conducting a cross-cultural study with a quantitative method, the researchers need to effectively address the cultural and linguistic influence on the operation of the instrument (or scale) across population groups. Measurement invariance (MI) provides valuable information to this concern and is the key to many psychological and developmental research studies. It is tested by evaluating how well the specified model fits the observed data. Researchers had developed effective fit indices to evaluate MI. Most scholars utilized the chi-square test with some alternative fit indices (such as root mean square error of approximation (RMSEA), standardized root-mean-square residual (SRMR), comparative fit index (CFI), and others) to report MI. Researchers argue about the sensitivity of these fit indices, and whether these fit indices accurately reflect the MI level. The current study followed Khojasteh and Lo (2015)'s study to test the sensitivities of a series of fit indices, including ΔCFI, ΔRMSEA, ΔSRMR, ΔGamma, and Δχ2, under specified conditions with Monte Carlo simulation data. Experimental conditions included test length, number of factors, sample sizes, factor loadings, and the percentage of noninvariant items. Results showed that the ΔCFI and ΔGamma are most powerful in testing invariance and are less sensitive to the sample size and non-invariance (or lack of invariance, LOI) situations. There was inflation in Type I error in the 2 factors 8 variable models. ΔSRMR and ΔRMSEA are more powerful only when the sample size is 1,000. ΔSRMR is sensitive to sample size and level of LOI; hence, it is not recommended. The results are compared with previous simulation studies and provide significant implications to researchers who are applying measurement invariance procedures about what fit indices to adopt in their studies.

The problem I address in this study is the persistence of inequity and low-self-concept among Black students. This study examines the ways in which dark-skinned African American girls' educational experiences support or impair their identity development. It adds to the body of knowledge by exploring personal journeys toward self-awareness amid family, school, and societal realities. While research exists regarding the achievement gap between Black students and other groups and the efforts to close that gap, research that specifically addresses the needs of Black girls is lacking. This study is significant because it brings attention to an underrepresented group, Black girls. Further, this study recognizes and attends to the nuances among this group by focusing on dark-skinned African American girls. Self-concept does affect the achievement of students. However, this study investigates self-concept in a new way. It considers the internal processing of self-awareness and identity formation. The purpose of the study is to examine the ways in which African American girls' educational experiences support or impair their identity development. This qualitative study uses the transcendental phenomenological approach to describe the educational experiences of dark-skinned African American middle grades and high school girls as they develop and explore their identities. I interviewed African American girls who perceive themselves as dark-skinned about their educational experiences and how these experiences have or have not supported their understanding and perceptions of their own heritage and Black identity. Findings show that the participants perceived the school environment to be hostile and unsupportive of their identity development.

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Increasingly researchers are turning to online focus groups as a qualitative research method, yet rigorous methodological studies regarding the quantity, quality and diversity of the data generated relative to traditional in-person focus groups are limited. This study experimentally tests the idea generation capabilities of online text-based focus groups versus traditional in-person focus groups using sustainability in the hospitality industry as the idea generation topic. Participants were purposively sampled from the hospitality program at a large Southeastern university and randomly assigned into one of two treatment groups: online text-based or traditional in-person focus groups. The in-person focus groups resulted in a larger word count, and a higher number of ideas generated, although both in-person and online generated an equivalent number of unique ideas. The online focus group generated a comparable average quality of ideas and number of good ideas. There was a high degree of overlap in themes generated by both groups. The results show that online focus groups are capable of generating a comparable level of idea quantity, quality and diversity relative to in-person focus groups.

The racial academic achievement gap, identified as the underachievement of racially, culturally, ethnically, and linguistically diverse (RCELD) students and documented by research, is visible in all education organizations (Griner & Stewart, 2013; National Center for Education Statistics, 2019; O'Connor, Hill, & Robinson, 2009). Since the United States Supreme Court decision of Brown v. Board of Education of Topeka, Kansas, repudiating the concept of separate but equal, laws concerning the need for equal education access have been enacted and educational policies have been created and implemented, but this achievement gap continues to exist (Williams, 2011). Culturally responsive leadership and culturally relevant pedagogy are research-based theories and practices that work towards meeting the needs of the growingly diverse population (Gay, 2018). A mixed method design utilized the explanatory sequential approach to research what culturally responsive beliefs, espoused practices, and supports elementary public-school principals convey in high performing urban schools. The design included the use of surveys and interviews to collect anonymous data from principals on their experiences in these roles and the influences they convey at their urban elementary school to narrow the racial academic achievement gap. Survey data collected with the use of Qualtrics Survey Software and interview data collected through individual interviews were conducted. Audio recordings of the interviews were transcribed and reviewed for themes. Creswell's six steps of data analysis and interpretation were utilized to analyze and interpret the data collected. Multiple reliability and validity measure occurred in this study. The results are compiled for future implications and recommendations are made.