Hierarchical Linear Modeling Approach Research Articles

Army Combat Fitness Test Scores Moderate Cognitive Function Improvements After a Ruck March: A Hierarchical Linear Model Approach.

Sax van der Weyden, M, Merrigan, JJ, Newman, K, Hahn, J, and Martin, J. Army Combat Fitness Test scores moderate cognitive function improvements after a ruck march: A hierarchical linear model approach. J Strength Cond Res XX(X): 000-000, 2024-Heavy load carriage is a common task for military personnel and can lead to undue physical and mental fatigue. The purpose of this study was to assess effects of load carriage on cognitive function (CF) and the role individual characteristics play in CF responses. Anthropometrics/body composition, demographics, and Army Combat Fitness Test (ACFT) scores were gathered from 47 Reserve Officers' Training Corps cadets. Cadets' CF was assessed through the concentration task grid (CTG) before (preruck), immediately after (postruck), and 3 weeks after (follow-up) a 16.09- to 19.31-km ruck march. Hierarchical linear modeling was used, with α < 0.05. The final model revealed growth, or improvement, in CF from preruck to postruck with growth decelerating, but still improved, at follow-up. The load carriage bout improved CF in most cadets, with average growth from 5.67- to 7.20-grid squares found. However, postruck CF did not return to the same level as a controlled follow-up (9.69). Sex (2.46, female), familiarity with the CTG (1.99), ACFT total scores (0.009), and relative ruck mass (-0.09) were all significant predictors of postruck CF. Army Combat Fitness Test scores (0.008) were also significant predictors of the slope between preruck and postruck, such that an ACFT score 59 points above average would result in a 1-grid square greater improvement in CF postruck. The significance of ACFT scores, lower relative ruck mass, and familiarization highlights the importance physical and mental preparation for desirable CF outcomes after occupational tasks.

Multilevel driving mechanism of ecosystem multidimensional stability in the Yangtze River Economic Belt: A hierarchical linear model approach

Ecosystem stability is a multifaceted and complex concept. Understanding the distribution pattern of multidimensional ecosystem stability and its drivers is essential for sustainability. However, the understanding of both the scale dependence and the interaction among different levels of drivers is limited. In this study, the evaluation framework for the multidimensional stability of ecosystems in the Yangtze River Economic Belt (YREB) was constructed by fitting Normalized Difference Vegetation Index (NDVI) to temperature, precipitation, and other meteorological factors from the background characteristics and dynamic change process of ecosystems, and utilizing Auto-Regression (ARx) as well as correlation analysis. Consequently, a hierarchical linear model (HLM) was utilized to assess the impact of natural elements at the basin level and socioeconomic elements at the prefecture level on stability under scale effects and interactions. The results showed that the spatial stability of YREB was higher in the southeast and exhibited a discontinuous distribution in the southwest. The ecosystem multidimensional stability score was the first in the Qiantang River Basin (SI = 0.2586), and the lowest score was in the Taihu Lake Basin (SI = 0.14). In addition, natural and socio-economic factors promoted ecosystem stability to different degrees and along different directions, and the scale dependence of drivers at different levels must be considered. At the basin level, increased soil organic carbon content led to increased stability, while the expansion of farmland and built-up land led to a decrease in stability. At the prefecture level, the population dominated the decrease in stability. Nesting the two scales, demographic and economic factors mainly control stability through their influence on land use, particularly farmland and forestland. Overall, understanding multi-level, multi-scale, and multi-indicator drivers is crucial for strengthening ecological protection in the YREB and controlling the farmland expansion and the urbanization rate growth to maintain the ecosystem stability.

Challenges to improving higher education students' critical thinking capacity in China

AbstractDrawing on data from the 2016 National Assessment of Collegiate Capacity in China, the study on which this article reports explored the value‐added effects of critical thinking capacity in higher education. Specifically, the analysis was developed using the difference in residuals model based on the Hierarchical Linear Modelling approach. The results show that, first, critical thinking capacity is malleable and can be improved through undergraduate education. Second, the results demonstrate that the value‐added effects of undergraduate education on college students' critical thinking capacity are heterogeneous. Only half of the value‐added effects achieved in higher education in China were positive. Third, the size of value‐added effects was not fully aligned with the level of the institutions. High‐ranking universities did not always perform well in achieving high value‐added effects on critical thinking capacity. This study analyses the reasons why some universities in China face insurmountable challenges in efforts to improve college students' critical thinking capacity. Challenges are highlighted from multiple perspectives. Corresponding improvement strategies are proposed in the concluding discussion.

A Comparative Analysis of Data Mining Methods and Hierarchical Linear Modeling Using PISA 2018 Data

A Comparative Analysis of Data Mining Methods and Hierarchical Linear Modeling Using PISA 2018 Data

Exploring the Curvilinear Relationship between Academic-Industry Collaboration Environment and Innovation Performance: A Multilevel Perspective

Academic institutions play a crucial role in knowledge production and driving innovation and economic growth. To enhance their capacity to deliver on these responsibilities, they are increasingly urged to establish academic–industry collaboration (AIC) environments to support research and innovation activities by their faculty and students. Despite the recognized importance of AIC in stimulating innovation performance, there is limited research exploring the cross-level impact of AIC on innovation performance. This study aims to address this research gap by investigating the relationship between the AIC environment and innovation performance while specifically examining the cross-level mediating role of researchers’ technological capability in this relationship. The study used a hierarchical linear model (HLM) approach, drawing on data from 187 researchers at 14 universities and academic institutions in the United Arab Emirates. The study’s results reveal a curvilinear (inverted U-shaped) effect of the AIC environment on innovation performance and a positive correlation between researchers’ technological capability and innovation performance. Furthermore, the AIC environment demonstrates a curvilinear effect on researchers’ technological capability, with researchers’ technological capability partially mediating the relationship between the AIC environment and innovation performance. These findings have significant theoretical and practical implications for policymakers in government agencies and university management seeking to develop evidence-based policies for effective science and technology management that enhance innovation performance.

The Influence of Organizational Culture to Innovation Behavior: A Hierarchical Linear Modelling Approach

Purpose – The purpose of this paper is to use Hierarchical Linear Modelling Approach of the studies that have analyzed the impact of organizational culture (innovative-oriented culture, learning culture, adhocracy culture, clan culture, hierarchical culture, and market/rational culture) on innovative behavior. Design/methodology/approach – The authors carried out a Hierarchical Linear Modelling (HLM). Based on a total of 240 in SMEs actors of culinary creative business Sleman Regency, Yogyakarta, Indonesia. Data collection with questionnaires and interviews. Findings – After reporting the findings of the HLM, the authors discuss how a variety of culturally related factors combine to facilitate or restrict innovation performance in their corresponding cluster. The findings highlight the complex and idiosyncratic relationship between culture and innovation. Future research lines are recommended. Research limitations – The data in this study is gathered using a cross-sectional method. Future research will be much improved if it is done as a longitudinal study over a period of time, so that the consistency of respondents’ answers on the same variables and instruments can be known. Practical implications – The paper offers interesting implications for managers and academia. For business practitioners, this study can provide a useful reference regarding the role of cultures in the corporate internal management or international operations; especially for small business in culinary, the study can provide a current research landscape and development process in this field. Originality/value – The findings are derived from a hierarchical linear modelling (HLM) that has studied the influence of culture on innovation behavior. Study about the influence of organizational culture on innovation using HLM is very limited, especially in small and medium-sized businesses. In addition, implications and insights as to where future research might be usefully inquired in this field are provided. Keywords: Organizational culture; Innovative Behavior; Hierarchical Linear Modelling

Strategic competence, task complexity, and foreign language learners’ speaking performance: a hierarchical linear modelling approach

AbstractUnderstanding the intricate relationships among strategic competence, tasks and performance is an issue of perennial interest in the assessment of foreign/second languages especially in integrated speaking assessment, a field that is under-researched. Against this background, we investigated such complex relationships in the context of integrated speaking assessment of English as foreign language (EFL) learners, hoping to provide additional empirical evidence to address the problem. In the investigation, strategic competence was defined as metacognitive strategy use and was measured via an inventory administered on 120 Chinese university EFL students; task characteristics were conceptualised as task complexity and were measured on a self-rating scale by the students and five EFL teachers; and the students’ speaking performance was indicated by their scores on four integrated speaking assessment tasks. Data analysis through a hierarchy linear modelling approach led to two primary findings: Monitoring, one form of strategic competence, moderated the effect of task complexity on performance; strategic competence had no substantial effects on performance which had an inverse relationship with task complexity. These findings will add validity evidence for the foreign language speaking assessment literature and provide implications for speaking instruction and test development.

The influence of ICT use and related attitudes on students’ math and science performance: multilevel analyses of the last decade’s PISA surveys

This study analyzed the latest four PISA surveys, 2009, 2012, 2015, and 2018, to explore the association between students’ ICT-related use and math and science performance. Using ICT Engagement Theory as a theoretical framework and a three-level hierarchical linear modeling approach, while controlling for confounding effects, ICT-related independent variables of interest were added to the models at the student, school, and country levels. The series of models revealed that, in general, an increase in ICT availability and ICT use both inside and outside school had a negative association with learning outcomes, while students’ positive attitude toward ICT demonstrated a strong positive relationship. However, students’ perceived autonomy related to ICT use had the strongest association with academic performance, which is consistent with the changing nature of the modern learning environments. Findings revealed that virtually all forms of student ICT use, both inside and outside of school and whether subject related or not, had no substantive positive relationship with student performance in math or science. Conversely, higher student attitude toward, confidence in, belief in utility of, and autonomous use of ICT was associated with higher math and science performance for each of the four years of the study. Incidentally, we also found that while country GDP per capita had no consistent association with student performance, a school’s provision of extra-curricula activities did. Recommendations for educational leaders, teachers, and parents are offered.

The impacts of supervisor support, role perception, and emotional exhaustion on the turnover intentions of real estate brokers

This study investigated the factors affecting turnover tendency of real estate brokers. The impact of individual-level factors (role conflict, role ambiguity, interpersonal conflict, and emotional exhaustion) and organizational-level factors (supervisor support and group trust) on turnover intentions was assessed. A hierarchical linear mediation modeling approach was used. A questionnaire was administered to real estate brokers working at real estate companies in Kaohsiung City. Ten questionnaires were administered to each of the selected 97 branch offices. Of the 970 questionnaires, 393 were recovered from 71 branches, and after omitting 43 invalid responses, there were 350 valid questionnaires from 59 branches, indicating an effective response rate of 36.1 %. The empirical results showed that interpersonal conflict mediated the impact of supervisor support on emotional exhaustion, with a full mediating effect being observed. Group trust did not mediate the impact of supervisor support on turnover tendency, which was direct, negative, and statistically significant. Job satisfaction partially mediated the impact of emotional exhaustion on turnover intentions. Our results suggest that supervisor support and emotional exhaustion, at the organizational and individual level, respectively, had the greatest impacts on turnover intentions. This demonstrates the importance of supervisor support and emotional exhaustion when researching turnover intentions.

The longitudinal effects of children’s temperament on maternal depression: A hierarchical linear modeling approach

Purpose: This study aimed to identify the longitudinal effects of children’s temperament on maternal depression.Methods: Data from a longitudinal cohort of the Panel Study of Korean Children (PSKC) from 2010 to 2012 were analyzed using hierarchical linear modeling. The survey included 1,721 mother-child dyads. The mothers reported on their children’s temperament and on maternal depression. The children’s temperament was measured by the Emotionality, Activity and Sociability-Temperament Survey for Children-Parental Ratings, while maternal depression was measured by the Kessler 6 Psychological Distress Scale.Results: The results showed that both children’s temperament and maternal depression were relatively stable when the children were between the ages of 2 to 4. The mean maternal depression scores were 11.83 in 2010, 11.88 in 2011, and 11.75 in 2012. There were significant negative correlations between the maternal depression scores and children’s ages, and sociability and activity subdomain scores ranged from r=-.05 to -.11 (p&lt;.05). There was a significant positive correlation between children’s emotionality subdomain scores and maternal depression scores (r=.35, p&lt;.001). Children’s temperament rament (emotionality: β=0.26, activity: β=-0.07, and sociability: β=-0.03) were significant factors in maternal depression.Conclusion: These findings indicate the need for the early assessment of and intervention for children’s temperament and maternal depression. The results of this study will provide basic data for the development of nursing education programs related to early assessment and intervention to improve the health and quality of life of young children and mothers.

Patterns of cognitive returns to Information and Communication Technology (ICT) use of 15-year-olds: Global evidence from a Hierarchical Linear Modeling approach using PISA 2018

Existing literature shows varying impacts of Information and Communication Technology (ICT) on learning outcomes depending on the type and quality of technology used. However, current research on the optimal level of ICT use for the cognitive development of students is scarce and has remained country-specific, primarily focusing on developed economies. This paper undertakes a cross-country comparison across 79 nations, investigating ICT use and its association with cognitive gain patterns as determined by reading, mathematics, and science test scores of 15-year-olds. The Programme for International Student Assessment (PISA) 2018 dataset collected by the Organisation of Economic Cooperation and Development (OECD) has been used by applying two-stage regression analysis. The first stage involves a three-level Hierarchical Linear Modeling (HLM) factoring for data nested at the student-, school-, and country-level. The second stage of the empirical model involves a heterogeneity analysis to evaluate variance in ICT use patterns across different groups of countries, clustered on the basis of their level of ICT development. The results show a positive impact of ICT engagement on the test scores of students across all the subjects. However, returns to ICT use tend to start diminishing after the engagement level of students crosses a medium threshold of using ICT several times within a week. Furthermore, the heterogeneity analysis supports conspicuous diminishing patterns in ICT use irrespective of the economic status of the students. Cross-country comparisons show that diminishing returns to ICT use are more prominent in countries with well-developed ICT infrastructure than in less-developed ones. Where diminishing returns hold, excessive use of ICT in education is not an optimal choice, and significant cognitive gains can be achieved by using the complementarity between traditional learning techniques with ICT-based learning in different blended settings.

Understanding the Role of Industry, Firm, CEO, and Cfo Effects in Financial Reporting Quality: A Hierarchical Linear Modelling Approach

Understanding the Role of Industry, Firm, CEO, and Cfo Effects in Financial Reporting Quality: A Hierarchical Linear Modelling Approach

From Efficiency Analyses to Policy Implications: a Multilevel Hierarchical Linear Model Approach

This paper examines the key factors that influenced the cost efficiency of 7,633 Vietnamese manufacturing firms during 2010–2016 via a hierarchical linear modelling (HLM) approach. The main reason for using HLM in this case is that observations in the same group may not be independent from each other (e.g. firms operate within the same city), and some variables may not vary across those observations. Although most of the findings are consistent with previous studies, the statistical power of our HLM model is higher than that of the traditional single-level analysis, suggesting that HLM can provide better analytical insights. The results further indicate a case for cities or provinces pursuing different policies aimed at improving the performance of their local firms.

Teacher expectation effects in Chinese junior high schools: Exploring links between teacher expectations and student achievement using a hierarchical linear modelling approach

Teacher expectation effects in Chinese junior high schools: Exploring links between teacher expectations and student achievement using a hierarchical linear modelling approach

Impact of within-case variability on Tau-U indices and the hierarchical linear modeling approach for multiple-baseline design data: A Monte Carlo simulation study

ABSTRACT This study focuses on two different measures to quantify intervention effectiveness for multiple-baseline design data. The influence of 216 design conditions on the estimate of the Tau-U index (with three different variants) and the regression-based effect size is empirically investigated through a Monte Carlo simulation study. Results demonstrate that the magnitude of the within-case variance influences the Tau-U indices the most when the true intervention effect is small relative to large within-case variance. The within-case variance does not systematically influence the regression-based effect size measure. The Critical Tau-U is introduced to help determine if Tau-U estimates indicate evidence in support of an intervention effect and address underlying issues with currently used benchmarks.

Characteristics of Moderators in Meta-Analyses of Single-Case Experimental Design Studies.

Hierarchical linear modeling (HLM) has been recommended as a meta-analytic technique for the quantitative synthesis of single-case experimental design (SCED) studies. The HLM approach is flexible and can model a variety of different SCED data complexities, such as intervention heterogeneity. A major advantage of using HLM is that participant and-or study characteristics can be incorporated in the model in an attempt to explain intervention heterogeneity. The inclusion of moderators in the context of meta-analysis of SCED studies did not yet receive attention and is in need of methodological research. Prior to extending methodological work validating the hierarchical linear model including moderators at the different levels, an overview of characteristics of moderators typically encountered in the field is needed. This will inform design conditions to be embedded in future methodological studies and ensure that these conditions are realistic and representative for the field of SCED meta-analyses. This study presents the results of systematic review of SCED meta-analyses, with the particular focus on moderator characteristic. The initial search yielded a total of 910 articles and book chapters. After excluding duplicate studies and non peer-reviewed studies, 658 unique peer-reviewed studies were maintained and screened by two independent researchers. Sixty articles met the inclusion criteria and were eligible for data retrieval. The results of the analysis of moderator characteristics retrieved from these 60 meta-analyses are presented. The first part of the results section contains an overview of moderator characteristics per moderator level (within-participant level, participant level, and study level), including the types of moderators, the ratio of the number of moderators relative to the number of units at that level, the measurement scale, and the degree of missing data. The second part of the results section focuses on the metric used to quantify moderator effectiveness and the analysis approach. Based on the results of the systematic review, recommendations are given for conditions to be included in future methodological work.

The Effects of Game and Student Characteristics on Persistence in Educational Games: A Hierarchical Linear Modeling Approach

Persistence is an important part of student success—both in and out of school. To enhance persistence, we first need to assess it accurately. Digital games can be used as vehicles for measuring and enhancing persistence. The purpose of this study is to test the effects of (a) game-level characteristics (i.e., game mechanics and conceptual difficulty), and (b) student-related characteristics (e.g., students’ incoming knowledge and gender) on persistence in a game called Physics Playground. The participants in this study were 137, eighth and ninth-grade students from a K-12 school in Florida. We used a Hierarchical Linear Modeling Approach (HLM) to analyze the data. The major findings are (1) the degree of difficulty relating to both the physics concepts and game mechanics of each game problem are significant predictors of persistence, with the former being more effective than the latter in predicting students’ persistence, and (2) the number of gold and silver trophies students attained in the game were the only significant student-level predictors of persistence. We conclude by discussing the findings, the implications, limitations, and future research related to this study.

A Regional Maize Yield Hierarchical Linear Model Combining Landsat 8 Vegetative Indices and Meteorological Data: Case Study in Jilin Province

The use of satellite remote sensing could effectively predict maize yield. However, many statistical prediction models using remote sensing data cannot extend to the regional scale without considering the regional climate. This paper first introduced the hierarchical linear modeling (HLM) method to solve maize-yield prediction problems over years and regions. The normalized difference vegetation index (NDVI), calculated by the spectrum of the Landsat 8 operational land imager (OLI), and meteorological data were introduced as input parameters in the maize-yield prediction model proposed in this paper. We built models using 100 samples from 10 areas, and used 101 other samples from 34 areas to evaluate the model’s performance in Jilin province. HLM provided higher accuracy with an adjusted determination coefficient equal to 0.75, root mean square error (RMSEV) equal to 0.94 t/ha, and normalized RMSEV equal to 9.79%. Results showed that the HLM approach outperformed linear regression (LR) and multiple LR (MLR) methods. The HLM method based on the Landsat 8 OLI NDVI and meteorological data could flexibly adjust in different regional climatic conditions. They had higher spatiotemporal expansibility than that of widely used yield estimation models (e.g., LR and MLR). This is helpful for the accurate management of maize fields.

Neighborhood context, police legitimacy and willingness to help the police in Shanghai, China

PurposeFollowing social disorganization theory and the process-based model of policing, the present study aims to examine how characteristics of communities are meaningful in shaping citizens' willingness to work with the police in urban China.Design/methodology/approachBased on survey data from Shanghai, China, the study adopts a generalized hierarchical linear modeling (GHLM) approach to examine the effects of both individual- and neighborhood-level predictors on the outcome, while taking into consideration the unobserved additional neighborhood-level variations.FindingsResults suggest potential need of the process-based model to be modified in a Chinese context in that while police presence matters, measures on legitimacy are nonsignificant in shaping willingness to help the police, after controlling for neighborhood characteristics. More importantly, collective efficacy at the neighborhood level is related positively to residents' willingness to work with the police. Constant attention should be paid to the promotion of a collaborative and collectively caring environment, which can contribute to better coordination with the police, and greater willingness to be part of the larger cause of public safety.Originality/valueThe paper advances the scholarship with multi-level modeling and the role of communities in building stronger relationship with the police.

Examining influences of science teachers' practices and beliefs about technology-based assessment on students’ performances: A hierarchical linear modeling approach

This study aimed at investigating how the factors across the student and school levels would influence students' performance on a technology-based assessment (TBA). TBAs not only enable teachers to evaluate their students' complex abilities, but have also been adopted by large-scale and international evaluation programs in recent years. Although some factors such as students' engagement or teachers' beliefs about assessments have been articulated, relatively little is understood about how the factors across levels affect students' performances on TBAs. This study thus collected data of 494 science teachers and 1774 eighth and 11th graders from 32 schools, and conducted a hierarchical linear modeling analysis to provide a more accurate estimation of the effects of the variables in each level on students' performances. The results indicated the importance of students' engagement in related learning activities and their computer experiences at both the individual and school levels. Additionally, although none of the teachers' variables at the school level such as the time teachers spent on the use of various types of TBAs or their intentions to use TBAs had significant main effects on students' performances, the teachers' influence at the school level on students' learning could still be found by a significant moderating effect from teachers' usage of TBAs. Our results provide insight into how to promote students’ performance on TBAs and can contribute in various ways to future research efforts concerning the use of TBAs in classrooms.