Use Of Hierarchical Linear Modeling Research Articles

The Impact of STEM Integration on Student Achievement Using HLM: A Case Study

The study’s purpose was to determine the effectiveness of a science, technology, engineering, and math (STEM) program in an urban, Ohio middle school. The definition of integrated STEM education along with curriculum, standards, and implementation models are described. Participants in this longitudinal, quantitative study were all students enrolled in grades 7-8 beginning in 2012-2013 and continuing for seven years. Per grade level for any given year, there were 25-28 participants receiving the treatment by voluntary enrollment in an integrated STEM education program (containing STEM curriculum and applying pedagogies of project-based learning) and 350-425 control participants receiving general education (defined as traditional and lacking both project-based learning and STEM curriculum). We sought to determine if participation in an integrated STEM education program had an impact on student achievement (measured by Ohio State Test scores) and, if any, interaction effects due to gender, socioeconomic status, student race, and attendance rate were present. The use of hierarchical linear modeling (HLM) determined integrated STEM education had a significant, positive effect on achievement combining math and science (participants scoring 31.8 points higher on average) and in science only (participants scoring 38.2 points higher on average) compared to control participants, respectively. No interaction effects were found. These findings pose strong implications for educational leaders in making teacher training and curriculum decisions with the aim of increasing student achievement.

Using HLM to Determine a STEM Programs Impact on Middle School Academic Achievement

Hierarchical linear modeling (HLM) has become an increasingly popular multilevel method of analyzing data among nested datasets, in particular, the effect of specialized academic programming within schools. The purpose of this methodological study is to demonstrate the use of HLM to determine the effectiveness of STEM programming in an Ohio middle school. This longitudinal study analyzes potential moderators of gender, socioeconomic status, student race, and attendance rate along with state test scores to quantify achievement. HLM determined integrated STEM education had a significant, positive effect on achievement in math and science combined (students scoring 31.8 points higher on average) and science achievement (students scoring 38.2 points higher on average) compared to traditional education students, respectively. There were little to no interaction effects determined between STEM participation and student factors. This demonstrates HLM as a powerful statistical tool for quantifying the impact of academic programming on student achievement.

The Power to Explain Variability in Intervention Effectiveness in Single-Case Research Using Hierarchical Linear Modeling.

This study investigated the power of two-level hierarchical linear modeling (HLM) to explain variability in intervention effectiveness between participants in context of single-case experimental design (SCED) research. HLM is a flexible technique that allows the inclusion of participant characteristics (e.g., age, gender, and disability types) as moderators, and as such supplements visual analysis findings. First, this study empirically investigated the power to estimate intervention and moderator effects using Monte Carlo simulation techniques. The results indicate that larger values for the true effects and the number of participants resulted in a higher power. The more moderators added to the model, the more participants needed to detect the effects with sufficient power (i.e., power ≥.80). When a model includes three moderators, at least 20 participants are required to capture the intervention effect and moderator effects with sufficient power. For that same condition, but only including one moderator, seven participants are sufficient. Specific recommendations for designing a SCED study with sufficient power to estimate intervention and moderator effects were provided. Second, this study introduced a newly developed user-friendly point and click Shiny tool, PowerSCED. This tool assists applied SCED researchers in designing a SCED study that has sufficient power to detect intervention and moderator effects. To end, the use of HLM with the inclusion of moderators was demonstrated using two previously published SCED studies in the journal School Psychology Quarterly.

Does Court Type, Size and Employee Satisfaction Affect Court Speed?. Hierarchical Linear Modelling With Evidence from Kenya

In most judicial institutions, well-functioning courts are usually expected to process a large volume of work within demanding timelines. For courts to have played their role of enhancing access to justice, the yardstick of success is often viewed through the lens of the speed attained in rendering justice. In Kenya, despite the desirable timeline for finalizing most of the cases being ‘within 360 days’ from the date of case filing in courts, by the end of June 2020, 58 per cent of the unresolved cases had surpassed this timeline and subsequently classified as backlog. In the period 2018/19, the percentage of civil cases that were resolved within the set timeline by High Court and Magistrate Court, the two largest court types by volume of work, was 37 and 42 per cent respectively. Over the same period, the percentage of criminal cases that were resolved within the set timeline was 42 and 84 per cent for the two court types respectively. Evidently therefore, the Kenyan courts had not managed to resolve cases within the desirable timeline. To unearth the reasons that could be occasioning the delay, this study investigated the factors that were potentially affecting court speed. Specifically, the study set out to determine the variation in court speed attributable to court type, and further analyze the effect of court size and employee satisfaction on court speed. This was achieved through the use of Hierarchical Linear Modelling, cross sectional data for the period 2018/19 and estimation using Restricted Maximum Likelihood technique. The results revealed the existence of relatively high variation in court speed that is attributable to court type, and that the smaller the court size, the higher the court speed. Further, high level of employee satisfaction was found to increase timely resolution of cases. Consequently, diverse strategies and policy actions for enhancing court speed have been suggested. Keywords: Court Speed, Court Type, Court Size, Employee Satisfaction, Hierarchical Linear Modelling DOI: 10.7176/JLPG/110-02 Publication date: June 30 th 2021

An examination of organizational and community effects on police response time

PurposeThe primary objective of the current study is to examine the impact that the size of a police department and workload on a department have on response time. Secondarily, the authors look at the effect that incident-level factors such as the severity of a call for service (CFS) and community-level factors such as ethnic diversity have on police response time.Design/methodology/approachThe current study examined CFSs data collected over a three-year period and organizational information from 26 police departments in Northeast Texas, as well as community-level information. In order to measure the effect of organizational variables, community factors and incident-level variables on police response time, the authors employed the use of hierarchical linear models (HLMs).FindingsThe results of hierarchical linear modeling indicated that incident-level factors and police department size are significant predictors of response time.Research limitations/implicationsThere are two primary limitations: first, there were a lack of available organizational structure correlates such as age and differentiation. Second, the primary data set had a significant number of incomplete or repeating cases, thereby limiting the accuracy of the current study’s analysis.Originality/valueThe most unique aspect of this manuscript is that it examines how organizational factors affect police response time. Numerous studies analyzed determinants of police response time such as incident- and community-level factors, but the importance of organizational factors has not been analyzed.

Relationship between self-efficacy and language proficiency: A meta-analysis

This study aims to estimate the average effect size of the relationship between self-efficacy and language proficiency and to examine factors that moderate the relationship. A meta-analysis of the 493 effect sizes for the relationship between language learners’ self-efficacy beliefs and their language proficiency from 74 published journal articles, book chapters, and dissertations revealed an average of small to medium effect size. The use of hierarchical linear models indicated 76.7% of the variance between the published works, suggesting that the effect size varies across studies. Effect sizes reported in studies with East Asian students were larger than those reported in studies with students in Western culture. Parameter estimates of fixed effects noted that the effect sizes reported from zero-order correlations inflated the effect size without consideration of the language, culture, grade-level, sample size, and data analysis procedures. This study contributes to the literature by summarizing studies from the last two decades in a systematic way and cautions researchers when interpreting effect sizes reported in the literature.

A Systematic Methodological Review of Hierarchical Linear Modeling in Mixed Methods Research

The use of advanced quantitative methods within mixed methods research has been investigated in a limited capacity. In particular, hierarchical linear models are a popular approach to account for multilevel data, such as students within schools, but its use and value as the quantitative strand in a mixed methods study remains unknown. This article examines the role of hierarchical linear modeling in mixed methods research with emphasis on design choice, priority, and rationales. The results from this systematic methodological review suggest that hierarchical linear modeling does not overshadow the contributions of the qualitative strand. Our study contributes to the field of mixed methods research by offering recommendations for the use of hierarchical linear modeling as the quantitative strand in mixed methods studies.

The Use of Hierarchical Linear Modeling to Address Lack-of-Independence in Empirical Auditing Research

Prior empirical auditing research has typically used linear regression analysis to analyze auditor relationships. However, because audit firms, audit partners, and audit clients are nested and clustered, data on them lacks independence, and violates the assumptions necessary for valid tests using simple linear regressions. This deficiency can be overcome by employing the hierarchical linear modeling (HLM) technique to conduct empirical tests. We illustrate this by employing HLM to explain the relationship between audit quality and audit firm, and audit partner tenure. We show that employing HLM yields different results than those found using ordinary least squares.

Teacher characteristics and student performance: An analysis using hierarchical linear modelling

This research makes use of hierarchical linear modelling to investigate which teacher characteristics are significantly associated with student performance. Using data from the SACMEQ III study of 2007, an interesting and potentially important finding is that younger teachers are better able to improve the mean mathematics performance of their students. Furthermore, younger teachers themselves perform better on subject tests than do their older counterparts. Identical models are run for Sub Saharan countries bordering on South Africa, as well for Kenya and the strong relationship between teacher age and student performance is not observed. Similarly, the model is run for South Africa using data from SACMEQ II (conducted in 2002) and the relationship between teacher age and student performance is also not observed. It must be noted that South African teachers were not tested in SACMEQ II so it was not possible to observe differences in subject knowledge amongst teachers in different cohorts and it was not possible to control for teachers’ level of subject knowledge when observing the relationship between teacher age and student performance. Changes in teacher education in the late 1990s and early 2000s may explain the differences in the performance of younger teachers relative to their older counterparts observed in the later dataset.

Teacher characteristics and student performance: An analysis using hierarchical linear modelling

This research makes use of hierarchical linear modelling to investigate which teacher characteristics are significantly associated with student performance. Using data from the SACMEQ III study of 2007, an interesting and potentially important finding is that younger teachers are better able to improve the mean mathematics performance of their students. Furthermore, younger teachers themselves perform better on subject tests than do their older counterparts. Identical models are run for Sub Saharan countries bordering on South Africa, as well for Kenya and the strong relationship between teacher age and student performance is not observed. Similarly, the model is run for South Africa using data from SACMEQ II (conducted in 2002) and the relationship between teacher age and student performance is also not observed. It must be noted that South African teachers were not tested in SACMEQ II so it was not possible to observe differences in subject knowledge amongst teachers in different cohorts and it was not possible to control for teachers’ level of subject knowledge when observing the relationship between teacher age and student performance. Changes in teacher education n in the late 1990s and early 2000s may explain the differences in the performance of younger teachers relative to their older counterparts observed in the later dataset.

The Case of Chichewa and English in Malawi: The Impact of First Language Reading and Writing on Learning English as a Second Language

We investigated the relationship between Chichewa (L1) and English (L2) literacies in Malawi. Through our use of hierarchical linear modeling, we found that cross-language literacy transfer between Chichewa and English did occur, but that the pattern and the strength of the relationships varied depending on the literacy domain (i.e., reading or writing) and grade level. Our work has implications for students, teachers, and policy makers in countries like Malawi, where the language of the former colonial power is the official language (L2) of the country and students must be prepared to participate within a globalized society.

Investigating Race, Class and Context through Historical Evidence: Segregation and the Ecology of Aspirations of Black College Students in the 1960s South

In an era of re-segregation among school systems in the United States, we find ourselves revisiting times of forced school segregation (pre Brown v. Board) for a better understanding of explanations and potential consequences. One consequence, depressed aspirations for occupational mobility, is examined here. Using data for Mathews and Prothro's Negro Participation Survey, administered to black college students in 1961, we examine this relationship while controlling for various individual level indicators of capital, demographics, political participation, and ecologically-centered organizational and economic factors through the use of Hierarchical Linear Modeling (HLM). We uncover patterns of aspirations (or lack thereof) concerning social class mobility that are directly related to social class background and the cultural orientations of the environment in which the student hails. Our conclusions link such patterns to the current trajectory of re-segregation in the education system.

The interactive effect of team and manager absence on employee absence: A multilevel field study

Although it is commonly assumed that manager and team absence levels have a significant impact on an individuals’ absence level, research has yet to simultaneously test the effect of these sources, as well their interactive effect on employee absence behaviour. Using archival attendance records for 955 employees, grouped in 79 teams, and the absence records of their respective managers from a large professional services organization, this study considers absence behaviour through the lens and social learning theory and social information processing theory to suggest that absence norms are socially constructed based on social influences of the absence pattern of one's team and manager. Through the use of hierarchical linear modelling to account for group‐level influences on absence behaviour, findings suggest that team‐level absence exerts a greater influence on employee absence than manager absence and that manager absence exerts a moderating influence on this relationship. Implications for attendance management as well as future research are considered.Practitioner points Team absence behaviour exerts a group‐level influence on employee absence behaviour. Manager absence moderates the team absence effect.

A hierarchical approach to Students’ Assessments of Instruction

A teacher evaluation system can be threatening to faculty, especially if used for summative decisions. Therefore, it is important to obtain valid and pertinent information. Since students are extensively exposed to course elements, students’ evaluation of instruction should be one of several components in the teacher evaluation system. Since traditional methods, such as Cronbach’s alpha and ordinary least squares regression, do not address the hierarchical data of the classroom, the current study used the statistical techniques of confirmatory factor analysis and hierarchical linear modelling in order to properly investigate the reliability and validity of the Students’ Assessment of Instruction (SAI) instrument. Use of hierarchical linear modelling to analyse teacher evaluation instruments could not be found in the literature, although it has been used in educational settings. This study will illustrate its usefulness in determining what measures are related, either as evidence of validity or as a bias, to instructional effectiveness. Student responses were also compared with faculty self-evaluations, one indicator of effective teaching, in order to determine if the SAI does measure instructional effectiveness. Overall, the SAI was found to have good reliability and validity with relatively few biases and could be used to extract five distinguishable traits of instructional effectiveness.

An assessment of hierarchical linear modeling in international business, management, and marketing

International marketing research, in most cases, involves at least two levels (e.g., firms within countries) that make the hierarchical linear modeling (HLM) a suitable data analysis technique. Due to its robustness, the use of HLM in the international business (IB) research has increased substantially over the last decade. However there is still a lack of standardization in the fundamental issues that hinders the wide spread use of HLM. This study provides unified approach to HLM use in IB research by providing standards for appropriate use of HLM. To achieve this goal, a detailed analysis of the method's use in 42 IB studies is provided and these studies are compared with 104 non-international studies to determine where IB research stands in terms of HLM use. Finally this study focuses on the good HLM practices and offers suggestions designed to maximize the effective use of HLM and potential in international business studies.

Explaining the Strategic Groups–Firm Performance Relationship: A Multilevel Approach Applied to Small and Medium‐Sized Hotel Companies in Spain

One of the main research questions in the field of strategic management is why firms obtain different performance levels. This paper answers this question from the strategic groups approach. This paper analyzes the linkage between strategic groups and firm performance offering a multilevel analysis about the relative importance of intergroup and intragroup performance differences based on the use of hierarchical linear models. The results show that intragroup differences explain firm performance better than intergroup differences.

Basic concepts of Hierarchical Linear Modelling (HLM) for educational technology research

Increasingly, researchers are faced with nested and cross‐level data. For example, students are clustered within classrooms, classrooms are clustered within schools and schools are clustered within districts. However, most traditional statistical analyses assume that observations are independent of each other. When people are clustered within naturally occurring organisational units such as schools, classrooms or districts, the responses of people from the same cluster are likely to exhibit some degree of relatedness with each other. In other words, they are not independent of each other. The use of Hierarchical Linear Modelling (HLM) allows researchers to adjust for and model this non‐independence (McCoach, 2010)...

Dealing With Dependence (Part II): A Gentle Introduction to Hierarchical Linear Modeling

In education, most naturally occurring data are clustered within contexts. Students are clustered within classrooms, classrooms are clustered within schools, and schools are clustered within districts. When people are clustered within naturally occurring organizational units such as schools, classrooms, or districts, the responses of people from the same cluster are likely to exhibit some degree of relatedness with each other. The use of hierarchical linear modeling allows researchers to adjust for and model this non-independence. Furthermore, it may be of great substantive interest to try to understand the degree to which people from the same cluster are similar to each other and then to try to identify variables that help us to understand differences both within and across clusters. In HLM, we endeavor to understand and explain between- and within-cluster variability of an outcome variable of interest. We can also use predictors at both the individual level (level 1), and the contextual level (level 2) to explain the variance in the dependent variable. This article presents a simple example using a real data set and walk through the interpretation of a simple hierarchical linear model to illustrate the utility of the technique.

Multi-Campus Studies of College Impact: Which Statistical Method is Appropriate?

In most multi-campus studies of college impact that have been conducted over the past four decades, investigators have relied on ordinary least squares (OLS) regression as the analytic method of choice. Recently, however, some investigators have advocated the use of Hierarchical Linear Modeling (HLM), a method specifically designed for analyses that involve both individual (student) and aggregate (institutional) level measures. Cross-validation analyses using a national database show that the two methods yield an equally good “fit” with empirical data. Existing OLS software has the advantage of enabling one to perform path analytical causal modeling; HLM has the advantage of yielding a more conservative estimate of the significance of institution-level effects.

Dynamic variation in pleasure in children predicts nonlinear change in lateral frontal brain electrical activity.

Individual variation in the experience and expression of pleasure may relate to differential patterns of lateral frontal activity. Brain electrical measures have been used to study the asymmetric involvement of lateral frontal cortex in positive emotion, but the excellent time resolution of these measures has not been used to capture second-by-second changes in ongoing emotion until now. The relationship between pleasure and second-by-second lateral frontal activity was examined with the use of hierarchical linear modeling in a sample of 128 children ages 6-10 years. Electroencephalographic activity was recorded during "pop-out toy," a standardized task that elicits pleasure. The task consisted of 3 epochs: an anticipation period sandwiched between 2 play periods. The amount of pleasure expressed during the task predicted the pattern of nonlinear change in lateral frontal activity. Children who expressed increasing amounts of pleasure during the task exhibited increasing left lateral frontal activity during the task, whereas children who expressed contentment exhibited increasing right/decreasing left activity. These findings indicate that task-dependent changes in pleasure relate to dynamic, nonlinear changes in lateral frontal activity as the task unfolds.