Hierarchical Linear Modeling Method Research Articles

MANAGERS’ ETHICS OF TAX EVASION: THE ROLES OF FAMILY, RELIGION, AND SOCIAL CONDITIONS

Tax income is one of the essential financial resources to maintain a nation’s development, as tax revenue promotes the advancement of social welfare and community affairs. However, tax evasion has been a persistent governmental and societal concern. In order to expand insights on tax evasion of managers, the authors used Emile Durkheim’s sociological theory of anomie to investigate the individual-level association between managers’ perceived family and religion importance and their attitude toward tax evasion. Additionally, the theory was employed to examine how country-level aspects moderate that individual-level relationship. The hierarchical linear modeling (HLM) method was utilized to analyze data of 3,475 managers from 47 countries obtained from the World Values Survey (WVS). Results indicated that the managers’ perceived family and religion importance were negatively related to their ethics of tax evasion. Moreover, the individual-level relationships were moderated by the country-level factors of poverty, good governance, political integration, and social integration. Most study findings supported Durkheim’s original propositions, whereas complementary arguments were offered to explain the results contradicting them. Since there are no existing studies on the influence of managers’ perceived family and religion importance on the ethics of tax evasion and how social perspectives moderate their impacts, the results of this study offer deeper insights into understanding the issue. Practical implications for organizations and society were discussed to reinforce managers’ ethics of tax evasion. The study findings will help organizations and governments establish social programs that will decrease managers’ likelihood to evade taxes, thereby contributing to the development of organizations and the nation.

Is there an incompatibility between personal motives and social capital in triggering pro-environmental behavioral intentions in urban parks? A perspective of motivation-behavior relations

Drawing from the perspective of motivation-behavior relations, this study explores whether self-transcendent and activating status motives trigger pro-environmental behavioral intentions of residents living nearby urban parks. Meanwhile, this study particularly examines the moderating effect of community social capital and whether there exists an incompatibility between community social capital and personal motives. This study adopts Hierarchical Linear Modeling (HLM) method to conduct data analysis and reveals that self-transcendent and activating status motives positively influence nearby residents' pro-environmental behavioral intentions. The relationships between personal motives and pro-environmental behavioral intentions are negatively affected by community social capital, suggesting that there is an incompatibility between community social capital and personal motives. Furthermore, a positive spillover effect between low and high effort pro-environmental behavioral intentions is noted. These findings provide park and community managers with insights as far as the residents' engagement in urban park sustainability is concerned.

Does Postsecondary Education Attainment Matter in Community Service Engagement? Evidence from Across 18 OECD Countries

This study is concerned with the central issues of community service engagement (CSE) in 21st century democratic societies around the world. To examine the factors influencing postsecondary education attainment’s relationship to CSE, this study utilized data from the Organization for Economic Co-operation and Development (OECD) countries using ordinary least square (OLS) and two-level hierarchical linear modeling (HLM) methods, including various factors for each country’s individual and country levels. The results show that attainment in postsecondary education at the individual level and investment and enrollments in tertiary education both have an influence on increasing CSE in 18 OECD countries. The present study is expected to contribute to an understanding of the relationship between postsecondary education and CSE across the world.

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 Racial/Ethnic Composition and Medical Discrimination’s Relation to Mammograms: A Philadelphia Case Study

Late-stage breast cancer diagnosis is common for women of color even though cancer screening rates in this group have increased over time. Perceived medical discrimination from health care providers is a key culprit of this inconsistency, discouraging women of color from seeking out timely preventative care. Non-white neighborhoods offer social support structures that mitigate discrimination. However, it is not clear whether this mitigation applies to the chance women of color get timely mammograms. This study addresses that question, applying Hierarchical Linear Modeling methods onto the Public Health Management Corporation’s 2006 and 2008 survey, as well as the 2005–2009 American Community Survey. Findings indicate that while neighborhood racial/ethnic composition does not directly reduce medical discrimination’s association to screening, factors closely connected to place, like health care access, do have a mitigating influence on this association. Also, neighborhood racial/ethnic composition and social support have a positive direct relation with screening.

Multilevel analysis of knowledge sources for product innovation in Russian SMEs

This research was aimed at analyzing the moderating role of region on the impact of internal and external sources of knowledge on product innovation from a multilevel perspective. This study has made a contribution to the knowledge and innovation management field for small and medium-sized enterprises (SMEs), by analyzing the utilization of internal and external sources of knowledge in rapidly changing environments, such as the Russian business context, with consideration given to regional disparity. Empirical estimations are carried out on the basis of more than 700 Russian manufacturing SMEs, observed in 2018 within the framework of the project, “Factors of Competitiveness and Growth of Russian Manufacturing Enterprises”. Internal and external sources of knowledge were identified through latent variables and a method of hierarchical linear modeling was applied, considering firm-level data nested within different regions. The results obtained, show that in Russian SMEs, when considering the moderation role of the region, internal and external knowledge have a positive impact on product innovation. Moreover, external knowledge contributes more by comparison to internal knowledge. Meanwhile, the region context conditions the strength of the innovation effect for both knowledge sources. The significance of regional conditions in transforming internal and external sources of knowledge into product innovation, requires specific policy elaboration at regional level. Moreover, the dominating role of external knowledge sources for product innovation in SMEs, proves the necessity of specific policy elaboration with regard to the knowledge-sharing infrastructure connecting different business units.

School social organization influences adolescents' cognitive engagement with school: The role of school support for learning and of autonomy support

School-effects research has provided insights into what schools can do to keep their students engaged. Further studies with a multidimensional perspective of engagement are required to elucidate the precise roles of different features of school social organization in promoting different dimensions of engagement. This longitudinal study examined the influences of social support from teachers and peers, as well as autonomy support, on students' trajectories of cognitive engagement. The sample (n = 2646) included two cohorts of students who attended the same schools (n = 70) across two academic years: between 7th and 8th, and 10th to 11th grades. Given the multilevel nature of our research question, we used Hierarchical Linear Modeling (HLM) methods. Cognitive engagement declined over time. This decline was less pronounced in schools where social support from peers and autonomy support were more prevalent. These findings are important because they imply schools can lessen their students' declining cognitive engagement by promoting social support and autonomy support.

Leader–Member Exchange in Mediating Interpersonal Justice and Informational Justice Climate toward Counterproductive Work Behavior

This cross-level study (individual level) examines the influence of interpersonal justice climate and informational justice climate (unit level) on counterproductive work behavior (individual level). The study also examines the role of leader–member exchange (individual level) in mediating the influence of these climates on counterproductive work behavior. The study was carried out by distributing a questionnaire to non-civil-servant lecturers in three new state universities in Indonesia. Of the questionnaires distributed, 399 were returned completely filled out and were then processed using hierarchical linear modeling. Hierarchical linear modeling is the appropriate analysis tool for examining cross-level models in which there is variance at both the individual level and the group level, with individual-level outcomes. The hierarchical linear modeling method still considers variance both within and between units and the role of both individuals and units simultaneously. The instruments were tested using confirmatory factor analysis, and reliability tests were carried out by looking at the coefficient of Cronbach’s alpha. The results of the hypothesis test show that while interpersonal and informational justice climates do not have an influence on counterproductive work behavior (H1 and H2 not accepted), they do have a significant influence on leader–member exchange (H3 and H4 accepted), which itself has a positive influence on counterproductive work behavior (H5 accepted). The test results for H6 and H7 show leader–member exchange fully mediates the influence of interpersonal and informational justice climate on counterproductive work behavior. The results of this study encourage further discussion and open opportunities for cross-level research with different variables and objects.

A hierarchical interannual wheat yield and grain protein prediction model using spectral vegetative indices and meteorological data

The use of remote sensing data for predicting wheat yield and quality is becoming a more feasible alternative to destructive and post-harvest laboratory-based test methods. However, most prediction models which make use of remote sensing data are statistical rather than mechanistic, therefore difficult to extend at interannual and regional scales. In this work, an interannual expandable wheat yield and quality predicting model using hierarchical linear modeling (HLM) was developed, integrating hyperspectral and meteorological data. The results showed that the ordinary least squares (OLS) regression for predicting wheat yield and grain protein content (GPC), one key indicator of grain quality, had low stability at the interannual extension. The predictive power for yield by HLM method was higher than OLS, with R2, RMSEv and nRMSE values of 0.75, 1.10 t/ha, and 20.70 %, respectively. GPC prediction by the HLM method was enhanced when the gluten type was considered, with R2, RMSEv and nRMSE values of 0.85, 1.02 %, and 6.87 %, respectively. The results of this study confirmed that HLM can be a robust method for improving yield and GPC predicting stability under various growing seasons in winter wheat.

Mathematics achievement in the secondary high school context of STEM and non‐STEM schools

AbstractThe purpose of this study is to compare students’ mathematics achievement growth rate in Texas science, technology, engineering, and mathematics (T‐STEM) academies to students’ mathematics achievement growth rate in traditional public high schools. Forty‐six schools, 23 of which were T‐STEM academies and 23 of which were matched non‐STEM schools, were included in this study. A hierarchical linear modeling method was conducted. The result of the present study revealed that there was no difference in students’ mathematics achievement growth rate in T‐STEM academies compared to students’ mathematics achievement growth rate in comparison schools. However, in terms of ethnicity, the results indicated that African American and Hispanic students in T‐STEM academies outperformed African American and Hispanic students in comparison schools on their mathematics growth rate.

How Does Transformational Leadership Promote Innovation in Construction? The Mediating Role of Innovation Climate and the Multilevel Moderation Role of Project Requirements

Innovation plays a critical role in the sustainable development of the construction industry. This research aims at examining transformational leadership’s role in shaping employees’ innovative behavior by analyzing the mediating effect of innovation climate and the cross-level moderating effect of innovativeness as a project requirement. To achieve this aim, a questionnaire survey was conducted with 300 construction industry professionals in China and 251 valid replies were received. Data collected by the questionnaire were analyzed using the method of hierarchical linear modeling (HLM). The results showed that transformational leaders could nurture a mutual climate for innovation to motivate employees’ innovative behaviors. In addition, innovativeness as a project requirement at the project level strengthens the indirect link amongst transformational leadership and innovative behavior via the innovation climate. Therefore, in the presence of higher innovativeness as a project requirement, transformational leadership is more prone to exert a positive influence upon an individual’s innovative behavior via the perceived innovation climate. The research findings improve understanding of the roles of leadership and innovation climate in affecting individual behavioral outcomes, and could help project managers and leaders encourage innovative ideas within project organizations.

Effect of socioeconomic inequalities on cholecystectomy outcomes: a 10-year population-based analysis

BackgroundAlthough numerous epidemiological studies on cholecystectomy have been conducted worldwide, only a few have considered the effect of socioeconomic inequalities on cholecystectomy outcomes. Specifically, few studies have focused on the low-income population (LIP).MethodsA nationwide prospective study based on the Taiwan National Health Insurance dataset was conducted during 2003–2012. The International Classification of ICD-9-CM procedure codes 51.2 and 51.21–51.24 were identified as the inclusion criteria for cholecystectomy. Temporal trends were analyzed using a joinpoint regression, and the hierarchical linear modeling (HLM) method was used as an analytical strategy to evaluate the group-level and individual-level factors. Interactions between age, gender and SES were also tested in HLM model.ResultsAnalyses were conducted on 225,558 patients. The incidence rates were 167.81 (95% CI: 159.78–175.83) per 100,000 individuals per year for the LIP and 123.24 (95% CI: 116.37–130.12) per 100,000 individuals per year for the general population (GP). After cholecystectomy, LIP patients showed higher rates of 30-day mortality, in-hospital complications, and readmission for complications, but a lower rate of routine discharge than GP patients. The hospital costs and length of stay for LIP patients were higher than those for GP patients. The multilevel analysis using HLM revealed that adverse socioeconomic status significantly negatively affects the outcomes of patients undergoing cholecystectomy. Additionally, male sex, advanced age, and high Charlson Comorbidity Index (CCI) scores were associated with higher rates of in-hospital complications and 30-day mortality. We also observed that the 30-day mortality rates for patients who underwent cholecystectomy in regional hospitals and district hospitals were significantly higher than those of patients receiving care in a medical center.ConclusionPatients with a disadvantaged finance status appeared to be more vulnerable to cholecystectomy surgery. This result suggested that further interventions in the health care system are necessary to reduce this disparity.

Using hierarchical linear modeling to test the effect of overall strategic fit on firm performance in different industry

PurposeThis study aims at investigating the effect of an overall fit among strategy, environmental factors and organizational resources on firm performance, and the moderating role of industry on this relationship.Design/methodology/approachThis paper uses hierarchical linear modeling to analyze the nested data structure from the financial and electronics companies in Taiwan’s stock market from 2011 to 2012.FindingsThe empirical results indicate that overall strategic fit is positively related to firm performance, and this relationship varies across financial industry and electronics industry.Originality/valueThis study provides important implications for both academic researchers and practitioners. From a theoretical aspect, this study integrates two research streams of strategic fit (external fit and internal fit) into an overall model of strategic fit, and has explored the moderating role of industry on the relationship between strategic fit and firm performance. In addition, this study has also used hierarchical linear modeling method to test the hypotheses, which has not been used in the previous strategic fit literature. From a practical aspect, the empirical results have derived implications for managers as to understand the effects of overall strategic fit on performance in different industry, which may be helpful for making decisions in specific industry context.

Individual and group antecedents of satisfaction: One lab- multilevel study

<p>This study examines the simultaneous effect of individual (self-efficacy) and group variables (cohesion and gender diversity) on satisfaction. A laboratory study was conducted involving 373 college students randomly distributed across 79 small groups, who performed a laboratory task in about five hours. Two-level Hierarchical Linear Modeling (HLM) method was used. Results show the main effect from individual self-efficacy to satisfaction (both level 1), the cross-level effect from group cohesion (level 2) to individual satisfaction (level 1), and the interaction effect between self-efficacy and gender diversity to satisfaction. These results suggest that in a work group, satisfaction has a background in individual and group variables. Group cohesion and gender diversity have important effects on satisfaction. The article concludes with practical strategies and with limitations and suggestions for future research.</p>

Informal institutions and technology use by entrepreneurs

Purpose– Given the increasing relevance of emerging economies in the global economy and the neoclassical argument that technological progress is the main driver of economic growth, the purpose of this paper is to investigate the under-researched role of informal institutions on the likelihood that individuals will enter into technology entrepreneurship in emerging markets.Design/methodology/approach– Since the authors combined individual-level and country-level observations, data were analyzed employing hierarchical linear modeling methods and random-effect logistic regressions to estimate the influence of country-level factors on the likelihood of individuals’ entry into techno-entrepreneurship. The data set comprised 10,280 observations for 18 emerging countries during the 2002-2008 period.Findings– The selected informal institutions relate to techno-entrepreneurship as follows: the size of the shadow economy has aU-shaped relationship; ethnic diversity is positively associated; and ethnic polarization is negatively associated, though the latter is not significant.Research limitations/implications– The authors did not theorize on cross-level mechanisms through which these informal institutions could influence individual-level attitudes, nor did the authors assess the role of such institutions on general entrepreneurship. However, this paper provides a base for more fine-grained studies.Originality/value– The authors disseminate novel insights into the particularities of emerging economies since all informal institutions studied here have been negatively associated with the overall economic experiences of developing and least developed countries. In addition, the authors provide a unique contribution by identifying a potentialU-shaped relationship between the size of the shadow economy and the likelihood of individuals engaging in techno-entrepreneurship.

Investigating the dual-route effects of corporate branding on brand equity

This research investigated the multilevel relationships among corporate branding, brand citizenship behavior, and brand equity using a hierarchical linear modeling method. The corporate branding construct represents practices that could improve the brand cognition and brand attitude of multiple stakeholders. Brand citizenship behavior indicates that employees are altruistic toward and identify themselves with brands. Brand equity was assessed using customer data to measure their awareness, association, quality perception, attitude, and loyalty toward a corporate brand. This study obtained data from 283 employees, 250 supervisors, and 577 customers of 31 franchise organizations to demonstrate the results at different levels. The results of our analysis indicate that corporate branding exerts a positive effect on brand citizenship behavior and customer-based brand equity. Furthermore, when aggregated, the brand citizenship behavior of firms positively affects their corporate brand equity. In addition, brand citizenship behavior mediates the relationship between corporate branding and brand equity. Brand managers should consider adopting corporate branding practices to encourage employees to engage in brand citizenship behavior, which contributes to brand equity.

School Effects, Gender and Socioeconomic Differences in Reading Performance: A Multilevel Analysis

The purpose of this paper is to examine the characteristics of Albanian secondary schools, which are associated with reading achievement and the effects of gender and socio-economic status on reading performance of 15-year-old students. This study used data on the background and achievement of 4,596 students in 181 Albanian schools from the 2009 Programme for International Student Assessment (PISA). Given the nested structure of the data, two-level Hierarchical Linear Modeling (HLM) methods were used to address multilevel research questions. About a third of the total variance in reading performance lies between schools, indicating that school characteristics are important in predicting student achievement. The results clearly reveal the significant relationships of socio-economic status (SES) and gender with student achievement, even after controlling for family structure (two parent families versus others), learning strategies use, and reading engagement. There is also a substantial variability among schools on gender difference in student performance and the effect of SES on student performance. The results from the between-school model suggest that school type, school SES and classroom environment are significant predictors of school performance. Moreover, gender gap in reading performance is associated with school sector (public/private), school location (urban/rural), and average reading engagement; and SES effect on student performance is associated with school type (general/vocational), school location and average reading engagement. The characteristics of schools that make them excellent and more equitable are summarized.

Effects of Traditional and Nontraditional Forms of Parental Involvement on School-Level Achievement Outcome: An HLM Study Using SASS 2007–2008

ABSTRACTThe authors examined parental involvement's (PI) impact on school performance. The hierarchical linear modeling method was applied to national Schools and Staffing Survey 2007–2008 data. They found that PI variables explained significant variance for the outcomes of (a) meeting adequate yearly progress (AYP) and (b) being free from sanctions. The authors also found that PI in (a) open houses, (b) parent–teacher conferences, and (c) special events were statistically significant positive predictors for meeting AYP, and that PI (a) on hiring teachers, (b) in open houses, and (c) in special events were statistically significant positive predictors for being free from sanctions. PI on hiring teachers seemed to be an important strategy in turning around schools. The similarity and difference in the sets of statistically significant predictors for both outcomes indicate that some PI strategies were effective for all schools while others were effective for particular situations. Implications and limitations of the study were discussed.

Relationships Between Student, Teacher, and School Characteristics and Mathematics Achievement

A variety of factors contributes to student achievement in mathematics, including but not limited to student behaviors and student, teacher, and school characteristics. The purpose of this study was to explore which of these factors have an impact on student mathematics achievement. The target population for this study was North Carolina Algebra II students. Analyses of variance models were examined for group differences and a Three‐level Hierarchical Linear Modeling method was employed to examine individual predictors of student achievement in mathematics. Statistically, significant differences were found between students of different ethnicities, socioeconomic statuses (SES), and parental education levels. No gender effects were statistically significant. All teacher‐level variables investigated were found to be statistically significant, impacting student achievement in mathematics. School size and SES were not found to significantly contribute to student achievement. More research on the relationships between these factors shown to make statistically significant differences on mathematics achievement is needed to further explain several phenomena that this research reveals.

Outcomes of Acupuncture for Chronic Pain in Urban Primary Care

The purpose of this study was to describe outcomes of the Acupuncture to Decrease Disparities in Outcomes of Pain Treatment (ADDOPT) trial, testing acupuncture as an adjunct to usual treatment for chronic pain in urban health centers. We conducted a quasi-experimental trial. Primary care patients (>21 years old) with chronic pain caused by osteoarthritis or neck or back pain at 4 hospital-owned safety net health centers in the Bronx, New York, received weekly acupuncture treatments provided by supervised acupuncture students for up to 14 weeks. Pain and functional status were assessed during a 6-week run-in period before acupuncture, during treatment, and after treatment. Of 495 referred patients, 226 (47%) initiated acupuncture. Back pain was the most common referring diagnosis (59.5%) followed by osteoarthritis (16.3%). Patients were older (mean age, 54.3 years), mostly insured by Medicaid (60.4%), often receiving disability (38.3%), and often in poor or fair overall health (46.7%). They had high baseline levels of pain (mean severity per the Brief Pain Inventory, 6.8; mean days with pain, 12.3 of 14). The mean number of treatments was 9.7 (standard deviation, 7.3). Pain severity improved from baseline (6.8 vs. 5.6 at 12 weeks and 5.5 at 24 weeks), as did physical well-being (31.8 vs. 35.7 at 12 weeks and 35.3 at 24 weeks). Using hierarchical linear modeling methods, reduction in pain severity between baseline and the treatment phase was significant (P < .001). Improvements in physical well-being were significant at 12 and 24 weeks after baseline (P < .001). Referred primary care patients experienced high levels of pain and pain-related disability. Weekly acupuncture was associated with short-term improvements in pain and quality of life.