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ASLIB JOURNAL OF INFORMATION MANAGEMENT 2024年第5期76卷 758-777页

作者： Ouyang, Peng Liu, Jiaming Zhang, Xiaofei Harbin Inst Technol Sch Econ & Management Harbin Peoples R China Beijing Technol & Business Univ Sch Int Econ & Management Beijing Peoples R China Nankai Univ Business Sch Tianjin Peoples R China

PurposeFree knowledge sharing in the online health community has been widely documented. However, whether free knowledge sharing can help physicians accumulate popularity and further the accumulated popularity can help physicians attract patients remain unclear. To unveil these gaps, this study aims to examine how physicians' popularity are affected by their free knowledge sharing, how the relationship between free knowledge sharing and popularity is moderated by professional capital, and how the popularity finally impacts patients' ***/methodology/approachThe authors collect a panel dataset from Hepatitis B within an online health community platform with 10,888 observations from April 2020 to August 2020. The authors develop a model that integrates free knowledge sharing, popularity, professional capital, and patients' attraction. The hierarchical regression model is used to for examining the impact of free knowledge sharing on physicians' popularity and further investigating the impact of popularity on patients' *** authors find that the quantity of articles acted as the heuristic cue and the quality of articles acted as the systematic cue have positive effect on physicians' popularity, and this effect is strengthened by physicians' professional capital. Furthermore, physicians' popularity positively influences their patients' ***/valueThis study reveals the aggregation of physicians' popularity and patients' attraction within online health communities and provides practical implications for managers in online health communities.

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Analyzing multilevel data: comparing findings from hierarchical linear modeling and ordinary least squares regression

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HIGHER EDUCATION 2013年第4期66卷 439-461页

作者： Rocconi, Louis M. Indiana Univ Ctr Postsecondary Res Sch Educ Bloomington IN 47406 USA

This study examined the differing conclusions one may come to depending upon the type of analysis chosen, hierarchical linear modeling or ordinary least squares (OLS) regression. To illustrate this point, this study examined the influences of seniors' self-reported critical thinking abilities three ways: (1) an OLS regression with the student as the unit of analysis, (2) an OLS regression with the institution as the unit of analysis, and (3) a three-level hierarchical linear model. Overall, results demonstrate that one would come to substantively different conclusions regarding the influences on students' perceived critical thinking ability depending upon the type of analysis chosen, especially in regards to the effects of the institutional characteristics.

关键词： hierarchical linear modeling Ordinary least squares regression Methodology Critical thinking

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Augmenting Visual Analysis in Single-Case Research With hierarchical linear modeling

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BEHAVIOR MODIFICATION 2013年第1期37卷 62-89页

作者： Davis, Dawn H. Gagne, Phill Fredrick, Laura D. Alberto, Paul A. Waugh, Rebecca E. Haardoerfer, Regine Georgia State Univ Ctr Teaching & Learning Atlanta GA 30302 USA US Dept Educ Inst Educ Sci Washington DC USA Emory Univ Atlanta GA 30322 USA

The purpose of this article is to demonstrate how hierarchical linear modeling (HLM) can be used to enhance visual analysis of single-case research (SCR) designs. First, the authors demonstrated the use of growth modeling via HLM to augment visual analysis of a sophisticated single-case study. Data were used from a delayed multiple baseline design, across groups of participants, with an embedded changing criterion design in a single-case literacy project for students with moderate intellectual disabilities (MoID). Visual analysis revealed a functional relation between instruction and sight-word acquisition for all students. Growth HLM quantified relations at the group level and revealed additional information that included statistically significant variability among students at initial-baseline probe and also among growth trajectories within treatment subphases. Growth HLM showed that receptive vocabulary was a significant predictor of initial knowledge of sight words, and print knowledge significantly predicted growth rates in both treatment subphases. Next, to show the benefits of combining these methodologies to examine a different behavioral topography within a more commonly used SCR design, the authors used repeated-measures HLM and visual analysis to examine simulated data within an ABAB design. Visual analysis revealed a functional relation between a hypothetical intervention (e.g., token reinforcement) and a hypothetical dependent variable (e.g., performance of a target response). HLM supported the existence of a functional relation through tests of statistical significance and detected significant variance among participants' response to the intervention that would be impossible to identify visually. This study highlights the relevance of these procedures to the identification of evidence-based interventions.

关键词： single-case research hierarchical linear modeling growth modeling repeated measures data visual analysis

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A Monte Carlo Study of the Effects of Common Method Variance on Significance Testing and Parameter Bias in hierarchical linear modeling

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ORGANIZATIONAL RESEARCH METHODS 2013年第2期16卷 243-269页

作者： Lai, Xin Li, Fuli Leung, Kwok Chinese Univ Hong Kong Sch Publ Hlth & Primary Care Div Biostat Hong Kong Hong Kong Peoples R China Xi An Jiao Tong Univ Sch Management Xian 710049 Shaanxi Peoples R China City Univ Hong Kong Dept Management Hong Kong Hong Kong Peoples R China

Despite that common method variance (CMV) is widely regarded as a serious threat to the validity of findings based on self-reports, there is insufficient research on its confounding influence. We extend Evans's (1985) pioneering work, and the more recent works by Ostroff, Kinicki, and Clark (2002) and Siemsen, Roth, and Oliveira (2010), to delineate the influence of CMV in a two-level hierarchical linear model based on self-report data. Our simulation results clearly show that in the absence of true effects, it is extremely unlikely for CMV to generate significant cross-level interactions. In fact, if a true cross-level interaction exists, CMV tends to lower the likelihood of its identification and erroneously underestimate the regression coefficient. Our simulation results also show that CMV may lead to a false significant cross-level main effect and overestimate the regression coefficient when no true effect exists. To reduce the probability of Type I errors, we show that raising the significance level to .01, the split sample strategy, and the addition of more CMV contaminated variables are effective in the vast majority of real-life situations and are more effective than increasing the number of groups or persons in each group. Both the split sample strategy and the addition of more CMV contaminated variables are also effective in reducing parameter bias when no true cross-level main effect exists. Trade-offs associated with different strategies are discussed.

关键词： common method variance self-report data hierarchical linear modeling cross-level relationships Monte Carlo approach

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Impact of group work environment and size on L2 motivation in project-based learning

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SYSTEM 2025年 130卷

作者： Tanaka, Mitsuko Osaka Metropolitan Univ Inst Liberal Arts Sci & Global Educ 1-1 Gakuen choNaka ku Sakai Osaka 5998531 Japan Osaka Metropolitan Univ Grad Sch Sustainable Syst Sci 1-1 Gakuen choNaka ku Sakai Osaka 5998531 Japan

Group work is often employed in second language (L2) classrooms that focus on project-based learning (PBL). While the significance of group properties in facilitating effective collaboration is widely recognized, the influence of both group size and variations in group work environments on L2 motivation remains inadequately understood within this context. Additionally, although individual learner factors are known to affect L2 motivation, the role of the group work environments in moderating these effects has been less explored. This study aimed to address these gaps by analyzing data from 154 Japanese university students engaged in L2 group work as part of a semester-long PBL framework. The students' questionnaire responses were analyzed using hierarchical linear modeling, a multilevel analytical approach. The results revealed significant differences in L2 motivation across groups, depending on the quality of the group work environment. However, group size did not influence L2 motivation, nor did the impact of individual differences on L2 motivation vary based on the group work environment. These findings underscore the distinct contributions of both contextual factors and individual characteristics to L2 motivation in a PBL setting.

关键词： Contextual impact English as a foreign language (EFL) Environmental impact Group size Group work hierarchical linear modeling L2 motivation Project-based learning

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Context matters: The importance of investigating random effects in hierarchical models for early childhood education researchers

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EARLY CHILDHOOD RESEARCH QUARTERLY 2025年 70卷 178-186页

作者： Corkins, Clarissa M. Harrist, Amanda W. Washburn, Isaac J. Hubbs-Tait, Laura Topham, Glade L. Swindle, Taren Oklahoma State Univ Human Dev & Family Sci Stillwater OK 74078 USA Kansas State Univ Dept Appl Human Sci Manhattan KS 66506 USA Univ Arkansas Med Sci Dept Family & Prevent Med Little Rock AR 72205 USA Kansas State Univ Kansas Child Care Training Opportun Manhattan KS USA Univ Oklahoma Hlth Sci Ctr Oklahoma City OK USA

This paper highlights the importance of examining individual, classroom, and school-level variables simultaneously in early childhood education research. While it is well known that hierarchical linear modeling (HLM) in school-based studies can be used to account for the clustering of students within classrooms or schools, less known is that HLM can use random effects to investigate how higher-level factors (e.g., effects that vary by school) moderate associations between lower-level factors. This possible moderation can be detected even if higher-level data are not collected. Despite this important use of HLM, a clear resource explaining how to test this type of effect is not available for early childhood researchers. This paper demonstrates this use of HLM by presenting three analytic examples using empirical early childhood education data. First, we review school-level effects literature and HLM concepts to provide the rationale for testing cross-level moderation effects in education research;next we do a short review of literature on the variables that will be used in our three examples (viz., teacher beliefs and student socioemotional behavior);next we describe the dataset that will be analyzed;and finally we guide the reader step-by-step through analyses that show the presence and absence of fixed effects of teacher beliefs on student social outcomes and the erroneous conclusions that can occur if school-level moderation (i.e., random effects) tests are excluded from analyses. This paper provides evidence for the importance of testing for how teachers and students impact each other as a function of school differences, shows how this can be accomplished, and highlights the need to examine random effects of clustering in educational models to ensure the full context is accounted for when predicting student outcomes.

关键词： Random effects hierarchical linear modeling School-level effects Moderation Early childhood

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Innovative organizations' CSR signaling: Consumer perceptions, expectations, and outcomes

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JOURNAL OF BUSINESS RESEARCH 2025年 186卷

作者： Aronson, Olivia Hanson, Sara Lafont, Matthew Univ Richmond Robins Sch Business 102 UR Dr Richmond VA 23173 USA Randolph Macon Coll 114 Coll Ave Ashland VA 23005 USA

This research builds upon signaling theory and employs three studies to examine the unique CSR signaling of innovative organizations and the associated consumer perceptions, expectations, and outcomes. Archival data analysis reveals that high innovativeness organizations participate in more and distinct types of CSR signaling, compared to their less innovative counterparts. A theoretical framework integrating signaling and expectations theories explores whether the unique CSR signaling of innovative organizations aligns with consumer expectations and the associated outcomes. Two experimental studies demonstrate that consumers have heightened CSR signaling expectations for innovative brands, leading to greater purchase intentions when CSR practices and expectations align. A final study explores how multiple signals inform consumer expectations, specifically the interactive influence of a CEO transition signal. These findings yield insight into the unique CSR signaling of innovative organizations, the related consumer perceptions, expectations, and outcomes, and the importance of signal portfolio management to attain marketplace advantages.

关键词： Brand expectations corporate social responsibility (CSR) Experiments Innovativeness hierarchical linear modeling Signaling theory

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R-squared Measures for Multilevel Models with Three or More Levels

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MULTIVARIATE BEHAVIORAL RESEARCH 2023年第2期58卷 340-367页

作者： Rights, Jason D. Sterba, Sonya K. Univ British Columbia Dept Psychol 2136 West Mall Vancouver BC V6T 1Z4 Canada Vanderbilt Univ Dept Psychol & Human Dev 221 Kirkland Hall Nashville TN 37235 USA

Applications of multilevel models (MLMs) with three or more levels have increased alongside expanding software capability and dataset availability. Though researchers often express interest in R-squared measures as effect sizes for MLMs, R-squareds previously proposed for MLMs with three or more levels cover a limited subset of choices for how to quantify explained variance in these models. Additionally, analytic relationships between total and level-specific versions of MLM R-squared measures have not been clarified, despite such relationships becoming increasingly important to understand when there are more levels. Furthermore, the impact of predictor centering strategy on R-squared computation and interpretation has not been explicated for MLMs with any number of levels. To fill these gaps, we extend the Rights and Sterba two-level MLM R-squared framework to three or more levels, providing a general set of measures that includes preexisting three-level measures as special cases and yields additional results not obtainable from existing measures. We mathematically and pedagogically relate total and level-specific R-squareds, and show how all total and level-specific R-squared measures in our framework can be computed under any centering strategy. Finally, we provide and empirically demonstrate software (available in the r2mlm R package) to compute measures and graphically depict results.

关键词： Multilevel modeling effect size mixed effects modeling R-squared hierarchical linear modeling

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Text-to-Speech Technology and Math Performance: A Comparative Study of Students With Disabilities, English Language Learners, and Their General Education Peers

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EDUCATIONAL RESEARCHER 2024年第5期53卷 285-295页

作者： Wei, Xin Digital Promise Global 1001 Connecticut Ave NWSuite 935 Washington DC 20036 USA

This study investigates the relationship between text-to-speech (TTS) usage and item-by-item performance in the 2017 eighth-grade National Assessment of Educational Progress (NAEP) math assessment, focusing on students with disabilities (SWDs), English language learners (ELLs), and their general education (GE) peers. Results indicate that all students use TTS more for longer and more difficult math items as well as for multiple-choice or short-response formats. Among SWDs and GE students, lower math proficiency and higher perceived time pressure are linked to higher TTS usage. Moreover, among GE students, factors such as male gender, minority status, lower math persistence, and higher math interest and effort during testing contribute to higher TTS usage. TTS usage is positively associated with item performance for SWDs and ELLs who received extended time accommodations but not for those who did not receive such accommodations or for general education students. The study suggests that the time constraints of speeded digital assessments may limit the potential benefits of TTS for SWDs and ELLs in math problem-solving.

关键词： computerized testing descriptive analysis effect size English language learners extended time accommodation general education students hierarchical linear modeling mathematics education math performance NAEP secondary data analysis special education students with disabilities text-to-speech

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Source of perceived social support and cognitive change: an 8-year prospective cohort study

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AGING & MENTAL HEALTH 2023年第8期27卷 1496-1505页

作者： Du, Chenguang Dong, Xinqi Katz, Benjamin Li, Mengting Univ N Carolina Sch Med Chapel Hill NC 27515 USA Rutgers State Univ Inst Hlth Hlth Care Policy & Aging Res New Brunswick NJ 08903 USA Virginia Polytech Inst & State Univ Dept Human Dev & Family Sci Blacksburg VI USA Rutgers State Univ Sch Nursing New Brunswick NJ 08903 USA Renmin Univ China Sch Lab & Human Resources Dept Social Secur Beijing Peoples R China

Objectives:This study explored the longitudinal association between overall and individual sources of social support and global cognitive function in older adults. Methods: Data were drawn from three waves (2006, 2010 and 2014) of the Health and Retirement Study (HRS). The analytic sample included participants aged 65 and above who were married and had at least one child (N= 1319). Global cognitive function was measured through a 35-pointTelephone Interview Cognitive Screen (TICS). Perceived social support was measured via questions across four sources of support (spouse, child, otherfamily members, and friends). Analyses were conducted using hierarchical linear modeling (HLM) with the addition of a cross-lagged panel model (CLPM). Results: There was no significant association between perceived overall social support and global cognitive function overtime (beta= -0.02, p =0.19). However, changes in perceived support from children were positively associated with changes in global cognition (beta= 0.05,p <0.01). In contrast, changes in perceived support from other family members were negatively associated with changes in cognitive function (beta= -0.07,p < 0.01) . Conclusions: Our findings highlight the importance of examining perceived social support from specific elements of one's social networks, rather than in an aggregated variable. Although the positive effect of perceived support from children may be small to moderate, the findings could provide a target for interventions to protect the cognitive function of older adults.

关键词： Older adults perceived social support cognitive function hierarchical linear modeling longitudinal study

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