基于包含426个效应值、26 092个独立样本,总样本量达1 01 358个的117篇独立实证文献进行了Meta分析。研究结果表明,相对于西方情境,变革型领导在中国情境下能带来更高的绩效;中国情境下变革型领导对员工个体绩效(任务、关系、创新绩效)、团队绩效、组织绩效均有显著的促进作用,且促进作用均大于西方情境,但对不同类型个体绩效促进作用的强弱排序与西方情境不一致;情境因素(组织属性、领导层次、地区属性)和测量因素(数据属性、测量工具、同源偏差程度)能够显著调节变革型领导与个体绩效的关系,但调节强度与西方情境也有所不同。研究结果可让中国情境下变革型领导与绩效的相关性研究获得阶段性定论。

在心理、行为、管理和教育等社科领域,经常使用多维测验。本文评介并比较了各种多维测验的测量模型;总结了基于双因子模型计算得到的统计指标;根据不同研究目的,提出了两个兼顾简洁性和精确性的多维测验分析流程。作为例子,在马基雅维利主义人格量表的研究中,通过双因子模型分析了如何报告、解释多维测验分数以及如何利用多维建模进行后续分析。

问卷法作为中国管理研究中最普遍的研究方法近来受到较多质疑。问卷调研法的“严谨性”本身没有问题，而是由于部分学者在使用这一方法过程中的不严谨性（特别是在取样和测量方面），导致学者对此方法产生了一定的误解。基于此，针对近十年发表在《管理世界》上的137篇采用问卷调研法的演绎性研究，对其取样和测量合适性进行评估，提出了八个常见的问题，并以一篇范文为例，提出了对应的建议，以期为采用问卷调研法的管理研究提供一定的借鉴。

研究变量之间的相关性是现代管理学实证研究的基本范式。然而,变量之间统计上的相关性极易受到变量的测量方法和数据类型的影响。特别是,基于同源数据的实证研究会夸大变量间的相关程度甚至带来虚假相关,这已经给管理学实证研究带来了严峻挑战。不幸的是,目前仍有大量的实证研究都在使用同源主观数据。本文以人力资源管理和企业绩效的关系为例,检验了在同一样本中,对变量的不同测量和数据类型如何导致变量间相关性结论的差异。结果显示,当自变量和因变量是＂同源主观数据＂时,自变量和因变量之间的相关程度最高;当自变量和因变量是＂非同源主观数据＂时,相关程度有所下降;当自变量采用主观数据,而因变量采用客观数据时,这种相关没有达到显著性。本文的研究例子表明,在管理学实证研究中一定要谨慎使用同源的主观测量数据。

心理学期刊中的实证研究论文，很多时候都在检验变量之间的因果关系，但学界对因果研究存在一些不同的看法。本文试图回答下面问题：（1）实验中不能操纵的变量是否可以作为原因？（2）非实验研究能不能检验因果关系？（3）因果分析（尤其是中介分析）是否一定要使用追踪数据？追踪设计的主要目的是什么？通过因果要素和因果推理逻辑的辨析，对前面两个问题都给出了肯定的回答，并讨论了判明变量先后顺序、统计控制无关变量的方法；为了回答问题3，厘清了追踪设计在因果分析中的作用：区分变量的先后顺序、有效获得历时性的影响结果，但即时性的因果影响采用追踪设计可能是不合适的。

Common Method Variance (CMV) refers to the overlap in variance between two variables because of the type of measurement instrument used rather than representing a true relationship between the underlying constructs. Researchers should give careful consideration to CMV although it may not surely bias the conclusions about the relationships between measures. CMV effect is often created by using the same method — especially a survey — to measure each variable. Procedural design and statistical control solutions are provided to minimize its likelihood in studies. A statistical control technique is a good solution if it can separate construct varience, method varience and error, and distinguish method bias at the item level from method bias at the construct level, and takes account of Method×Trait interactions. Thus, method-factor approaches are better than partial correlation approaches. It’s very important to understand the model of every method-factor approache for selecting statistical remedies correctly for different types of research settings. Etimating evaluate the effect of CMV within specific research domains and the effect of CMV on empirical findings within a theoretical domain should be concerned for further research.

方法变异是由研究方法导致的系统误差。方法变异既可能产生结构层面的影响,也可能产生题目层面的影响;既可能影响单个结构的测量,也可能影响用相同方法研究的多个结构之间的关系,还可能影响用相同方法研究的多个研究之间的关系,进而影响到相关领域的理论建构。在控制方法变异时,可采取过程控制法和统计控制法,最好先用过程控制法。

为了平衡反应偏向,负性表述题目被引入量表,但这可能增加新的方法变异.本研究以核心自我评价量表(CSES)为例,检验正负性表述方法效应对其结构的影响.研究表明,CSES在结构效度和效标关联效度上均受到了表述方法的潜在影响.本研究支持了控制方法效应后的CSES单因子结构.本研究不仅为一直无法统一的CSES中文版提供了新的解释,也为自陈式心理测量工具在设置负性题目方面提供了新的思考.

近些年,由于积极心理学的兴起,员工幸福感的研究得到了广泛的关注。本论文从互动公平这一特定的组织公平概念出发,以公平理论为主体,并结合自我决定理论,从心理授权的视角既分析了互动公平影响员工幸福感的内在机制,又探讨了权力距离对整个影响机制的调节作用。通过对国内一家制造业企业的199名员工多时点匹配问卷的调查,结果表明:互动公平与员工幸福感之间呈现正相关关系;心理授权中介了互动公平对员工幸福感的影响作用;权力距离不仅负向调节了互动公平与心理授权之间的关系,而且还负向调节了互动公平—心理授权—员工幸福感这一中介机制。本研究的发现有利于充分了解互动公平影响员工幸福感的内在机制和边界条件,同时能为管理实践提供更好的指导,有效地提高员工幸福感。

The problem of common method biases has being given more and more attention in the field of psychology, but there is little research about it in China, and the effects of common method bias are not well controlled. Generally, there are two ways of controlling common method biases, procedural remedies and statistical remedies. In this paper, statistical remedies for common method biases are provided, such as factor analysis, partial correlation, latent method factor, structural equation model, and their advantages and disadvantages are analyzed separately. Finally, suggestions of how to choose these remedies are given.

Performance is perhaps the most central concept in public administration research, and this article discusses theoretically and investigates empirically how we can obtain more consistent performance measures. Theoretically, we combine existing arguments in public administration with institutional theory and the sociology of professions. Empirically, we ask whether different measures of individual performance produce different results. The investigated performance measures vary with regard to risk of common data source bias, standardization of assessment criteria, and external verification of the assessment. Our investigated explanatory variables are intrinsic motivation, public service motivation, and job satisfaction. Combining survey and administrative data for 747 lower secondary school teachers (teaching 5,679 students in 85 schools), we analyze 4 different measures of the same performance dimension for the same teachers: the teachers’ self-reported contributions to students’ academic skills, the students’ marks for the year’s work given by the teacher, marks in oral exams with one external examiner and the teacher, and marks in written exams with at least one external examiner. The associations are systematically stronger when the performance measure comes from the same data source as the explanatory variables, but when separate data sources are used and the measurement scale is institutionalized, the level of external verification does not matter much. Based on institutional theory and the sociology of professions, we develop a theoretical argument that can explain this.

Since the idea of method variance was inspired by D. T. Campbell and Fiske in 1959, many papers have demonstrated an ongoing debate about both its nature and impact. Often, method variance entails an upward bias in correlations among observed variables—common method bias. This article reports a split-ballot multitrait–multimethod experimental design for estimating 2 opposite biases: the upward biasing method variance from the reaction to the length of the response scale and the position of the survey items in the questionnaire and the downward biasing effect of poor data quality. The data are derived from self-reported behavior related to emotional and social competencies. This article illustrates a methodology to estimate common method bias and its components: common method scale variance, common method occasion variance, and the attenuation effect due to measurement errors. The results show that common method variance has a much smaller impact than random and systematic measurement errors. The results also corroborate previous findings: the greater reliability of longer scales and the lower reliability of items placed toward the end of the survey.

A panel of experts describes the nature of, and remedies for, method variance. In an attempt to help the reader understand the nature of method variance, the authors describe their experiences with method variance both on the giving and the receiving ends of the editorial review process, as well as their interpretation of other reviewers鈥 comments. They then describe methods of data analysis and research design, which have been used for detecting and eliminating the effects of method variance. Most methods have some utility, but none prevent the researcher from making faulty inferences. The authors conclude with suggestions for resolving disputes about method variance.

"JIBS" receives many manuscripts that report findings from analyzing survey data based on same-respondent replies. This can be problematic since same-respondent studies can suffer from common method variance (CMV). Currently, authors who submit manuscripts to "JIBS" that appear to suffer from CMV are asked to perform validity checks and resubmit their manuscripts. This letter from the Editors is designed to outline the current state of best parctice for handling CMV in international business research.

Common method variance (CMV) is the amount of spurious correlation between variables that is created by using the same method-often a survey-to measure each variable. CMV may lead to erroneous conclusions about relationships between variables by inflating or deflating findings. We analyzed recent survey research in IEEE Transactions on Engineering Management, Journal of Operations Management, and Production and Operations Management to assess if and how scholars address CMV. We found that two-thirds of the relevant articles published between 2001 and 2009 did not formally address CMV, and many that did address CMV relied on relatively weak remedies. These findings have troubling implications for efforts to build knowledge within information technology, operations and supply chain management research. In an effort to strengthen future research designs, we provide recommendations to help scholars to better address CMV. Given the potentially severe effects of CMV, authors should apply the recommended CMV remedies within their survey-based studies, and reviewers should hold authors accountable when they fail to do so.

Progress in organizational psychology (OP) research depends on the rigor and quality of the methods we use. This paper identifies ten methodological barriers to progress and offers suggestions for overcoming the barriers, in part or whole. The barriers address how we derive hypotheses from theories, the nature and scope of the questions we pursue in our studies, the ways we address causality, the manner in which we draw samples and measure constructs, and how we conduct statistical tests and draw inferences from our research. The paper concludes with recommendations for integrating research methods into our ongoing development goals as scholars and framing methods as tools that help us achieve shared objectives in our field. Copyright 2008 John Wiley & Sons, Ltd.

Abstract Public administration scholars are beginning to pay more attention to the problem of common source bias, but little is known about the approaches that applied researchers are adopting as they attempt to confront the issue in their own research. In this essay, we consider the various responses taken by the authors of six articles in this journal. We draw attention to important nuances of the common measurement issue that have previously received little attention and run a set of empirical analyses in order to test the effectiveness of several proposed solutions to the common-source-bias problem. Our results indicate that none of the statistical remedies being used by public administration scholars appear to be reliable methods of countering the problem. Currently, it appears as though the only reliable solution is to find independent sources of data when perceptual survey measures are employed.

The issue of common method variance (CMV) has become almost legendary among today's business researchers. In this manuscript, a literature review shows many business researchers take steps to assess potential problems with CMV, or common method bias (CMB), but almost no one reports problematic findings. One widely-criticized procedure assessing CMV levels involves a one-factor test that examines how much common variance might exist in a single dimension. This paper presents a data simulation demonstrating that a relatively high level of CMV must be present to bias true relationships among substantive variables at typically reported reliability levels. The simulation data overall suggests that at levels of CMV typical of multiple item measures with typical reliabilities reporting typical effect sizes, CMV does not represent a grave threat to the validity of research findings.

Surveys have long been a dominant instrument for data collection in public administration. However, it has become widely accepted in the last decade that the usage of a self-reported instrument to measure both the independent and dependent variables results in common source bias (CSB). In turn, CSB is argued to inflate correlations between variables, resulting in biased findings. Subsequently, a narrow blinkered approach on the usage of surveys as single data source has emerged. In this article, we argue that this approach has resulted in an unbalanced perspective on CSB. We argue that claims on CSB are exaggerated, draw upon selective evidence, and project what should be tentative inferences as certainty over large domains of inquiry. We also discuss the perceptual nature of some variables and measurement validity concerns in using archival data. In conclusion, we present a flowchart that public administration scholars can use to analyze CSB concerns.

Researchers are often concerned with common method variance (CMV) in cases where it is believed to bias relationships of predictors with criteria. However, CMV may also bias relationships within sets of predictors; this is cause for concern, given the rising popularity of higher order multidimensional constructs. The authors examined the extent to which CMV inflates interrelationships among indicators of higher order constructs and the relationships of those constructs with criteria. To do so, they examined core self-evaluation, a higher order construct comprising self-esteem, generalized self-efficacy, emotional stability, and locus of control. Across 2 studies, the authors systematically applied statistical (Study 1) and procedural (Study 2) CMV remedies to core self-evaluation data collected from multiple samples. Results revealed that the nature of the higher order construct and its relationship with job satisfaction were altered when the CMV remedies were applied. Implications of these findings for higher order constructs are discussed.

Common source bias has been the focus of much attention. To minimize the problem, researchers have sometimes been advised to take measurements of predictors from one observer and measurements of outcomes from another observer or to use separate occasions of measurement. We propose that these efforts to eliminate biases due to common source variance create serious problems. To demonstrate the problems of using what we term the “distinct sources” measurement design, we provide an integrative review of the literature regarding both contamination and deficiency of measures. Building on this theme, the article uses simulated data to demonstrate how using data from distinct observers or occasions of measurement can distort estimates of predictor importance at least as much as common source variance. Alternative multisource designs are advocated and examined for tractability by simulating various numbers of observations and sources in the research design.

The purpose of this study was to demonstrate how examining the bivariate correlations between items in self-report measures can assist in differentiating between possible common method variance vs. model specification errors. Specifically, social desirability was viewed as either a possible source of common method variance or as a theoretically meaningful construct that should be included in the model of interest (i.e., a specification error). In the first instance, LISREL was used, and the level of correlation between measures of social desirability and measures of the five constructs of interest was manipulated. These results provided some insight as to when one needs to be concerned about the possible “common variance effects” on the structural model. In the second instance, the correlations between measures of social desirability and the measures of only two constructs of interest were again manipulated. These analyses illustrated the point at which the omission of social desirability as a theoretically relevant variable began to result in a poor fit of the structural model.

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.

react-text: 506 The questionnaire survey is one of the most commonly used methods of data collection in public management research. These surveys often provide the information used to measure both the independent and dependent variables in an analysis. However, this introduces the risk of common method bias—a serious methodological challenge that has not received much attention as a distinct topic in public... /react-text react-text: 507 /react-text [Show full abstract]

There is a great deal of evidence that method bias influences item validities, item reliabilities, and the covariation between latent constructs. In this paper, we identify a series of factors that may cause method bias by undermining the capabilities of the respondent, making the task of responding accurately more difficult, decreasing the motivation to respond accurately, and making it easier for respondents to satisfice. In addition, we discuss the psychological mechanisms through which these factors produce their biasing effects and propose several procedural remedies that counterbalance or offset each of these specific effects. We hope that this discussion will help researchers anticipate when method bias is likely to be a problem and provide ideas about how to avoid it through the careful design of a study.

Despite recurring concerns about common method variance (CMV) in survey research, the information systems (IS) community remains largely uncertain of the extent of such potential biases. To address this uncertainty, this paper attempts to systematically examine the impact of CMV on the inferences drawn from survey research in the IS area. First, we describe the available approaches for assessing CMV and conduct an empirical study to compare them. From an actual survey involving 227 respondents, we find that although CMV is present in the research areas examined, such biases are not substantial. The results also suggest that few differences exist between the relatively new marker-variable technique and other well-established conventional tools in terms of their ability to detect CMV. Accordingly, the marker-variable technique was employed to infer the effect of CMV on correlations from previously published studies. Our findings, based on the reanalysis of 216 correlations, suggest that the inflated correlation caused by CMV may be expected to be on the order of 0.10 or less, and most of the originally significant correlations remain significant even after controlling for CMV. Finally, by extending the marker-variable technique, we examined the effect of CMV on structural relationships in past literature. Our reanalysis reveals that contrary to the concerns of some skeptics, CMV-adjusted structural relationships not only remain largely significant but also are not statistically differentiable from uncorrected estimates. In summary, this comprehensive and systematic analysis offers initial evidence that (1) the marker-variable technique can serve as a convenient, yet effective, tool for accounting for CMV, and (2) common method biases in the IS domain are not as serious as those found in other disciplines.

Abstract In this article we discuss and analyze the critical issues related to common method variance (CMV) that are particularly relevant to advertising research and recommend best practices for assessing the effects of CMV in this domain. Specifically, we cover the development of CMV as a domain-specific methodological concern and the underlying sources of CMV that are likely to operate in cross-sectional survey-based studies in the field of advertising. We discuss in detail the available procedural and statistical techniques that can be applied to control for and/or measure the effects of sources of CMV in a single study and across research domains. In addition, we provide a critical look at how these techniques have been employed in past research and make recommendations for future examinations of CMV in advertising research.

Common method variance has received much attention in the behavioral sciences. Nonetheless, scant scholarly effort has been invested in handling common method variance in hospitality research. This study investigates the current status of controlling for common method variance in hospitality research and assists researchers in taking appropriate actions. Study 1 shows hospitality researchers鈥 endeavors to control for common method bias through a critical review of literature published in four leading hospitality journals in the ten years from 2006 to 2015:International Journal of Hospitality Management,Journal of Hospitality & Tourism Research, Cornell Hospitality QuarterlyandInternational Journal of Contemporary Hospitality Management. In Study 2, empirical investigations examine the effectiveness of a procedural remedy (temporal separation) and a statistical control (an unmeasured method factor approach) with two independent samples. The results of Study 1 reveal that most survey-related publications in the four journals fail to address or acknowledge common method variance. Moreover, only a limited number of techniques is found to be used to control for method variance. The findings of Study 2 suggest that temporal separation with a time lag of one day leads to a weak control for method variance; however, the use of an unmeasured method factor significantly helps control for method variance in the model.

Abstract To examine hypothesized influence of method variance from negatively keyed items in measurement of callous-unemotional (CU) traits, nine a priori confirmatory factor analysis model comparisons of the Inventory of Callous-Unemotional Traits were evaluated on multiple fit indices and theoretical coherence. Tested models included a unidimensional model, a three-factor model, a three-bifactor model, an item response theory-shortened model, two item-parceled models, and three correlated trait-correlated method minus one models (unidimensional, correlated three-factor, and bifactor). Data were self-reports of 234 adolescents (191 juvenile offenders, 43 high school students; 63% male; ages 11-17 years). Consistent with hypotheses, models accounting for method variance substantially improved fit to the data. Additionally, bifactor models with a general CU factor better fit the data compared with correlated factor models, suggesting a general CU factor is important to understanding the construct of CU traits. Future Inventory of Callous-Unemotional Traits analyses should account for method variance from item keying and response bias to isolate trait variance.

Despite the increased attention paid to biases attributable to common method variance (CMV) over the past 50 years, researchers have only recently begun to systematically examine the effect of specific sources of CMV in previously published empirical studies. Our study contributes to this research by examining the extent to which common rater, item, and measurement context characteristics bias the relationships between organizational citizenship behaviors and performance evaluations using a mixed-effects analytic technique. Results from 173 correlations reported in 81 empirical studies (N = 31,146) indicate that even after controlling for study-level factors, common rater and anchor point number similarity substantially biased the focal correlations. Indeed, these sources of CMV (a) led to estimates that were between 60% and 96% larger when comparing measures obtained from a common rater, versus different raters; (b) led to 39% larger estimates when a common source rated the scales using the same number, versus a different number, of anchor points; and (c) when taken together with other study-level predictors, accounted for over half of the between-study variance in the focal correlations. We discuss the implications for researchers and practitioners and provide recommendations for future research.

Abstract Interest in the problem of method biases has a long history in the behavioral sciences. Despite this, a comprehensive summary of the potential sources of method biases and how to control for them does not exist. Therefore, the purpose of this article is to examine the extent to which method biases influence behavioral research results, identify potential sources of method biases, discuss the cognitive processes through which method biases influence responses to measures, evaluate the many different procedural and statistical techniques that can be used to control method biases, and provide recommendations for how to select appropriate procedural and statistical remedies for different types of research settings.

The need for more theory building scholarship remains one of the pressing issues in the field of HRD. Researchers can employ quantitative, qualitative, and/or mixed methods to support vital theory-building efforts, understanding however that each approach has its limitations. The purpose of this article is to explore common method variance bias as one of the possible major threats to the validity of quantitative research findings upon which significant theory building relies. Common method variance has been shown to introduce systematic bias into a study by artificially inflating or deflating correlations, thereby threatening the validity of conclusions drawn about the links between constructs. Both procedural design and statistical control solutions are provided to minimize its likelihood in studies with monomethod designs. Finally, editors and reviewers are called upon to support knowledge-building about how best to handle common method variance bias in quantitative studies.

ABSTRACT Each research domain carries the burden of examining the effects of common method variance (CMV) on published research within the domain. To focus on this concern in the context of the theory of planned behavior (TPB), this research empirically compares several methods of detecting the presence of and estimating the level of CMV in the TPB domain. These methods include various implementations of the marker variable technique and versions of the multitrait-multimethod (MTMM) technique. The results show that the marker variable technique provides estimates of CMV and CMV-corrected correlations and paths that are consistent with those produced using the other methods. Next, one implementation of the marker variable technique method is implemented post hoc on a large data set of published TPB studies. This analysis provides strong confirmatory evidence that the effects of CMV do not alter the substantive inferences of study results in prior research. Overall, these findings support putting to rest concerns about the adverse influence of CMV in the TPB domain.

This paper presents a meta-analysis-based technique to estimate the effect of common method variance on the validity of individual theories. The technique explains between-study variance in observed correlations as a function of the susceptibility to common method variance of the methods employed in individual studies. The technique extends to mono-method studies the concept of method variability underpinning the classic multitrait ultimethod technique. The application of the technique is demonstrated by analyzing the effect of common method variance on the observed correlations between perceived usefulness and usage in the technology acceptance model literature. Implications of the technique and the findings for future research are discussed.

An experiment investigated whether measurement features affected observed relationships between counterproductive work behavior (CWB) and organizational citizenship behavior (OCB) and their relationships with other variables. As expected, correlations between CWB and OCB were significantly higher with ratings of agreement rather than frequency of behavior, when OCB scale content overlapped with CWB than when it did not, and with supervisor rather than self-ratings. Relationships with job satisfaction and job stressors were inconsistent across conditions. We concluded that CWB and OCB are likely unrelated and not necessarily oppositely related to other variables. Researchers should avoid overlapping content in CWB and OCB scales and should use frequency formats to assess how often individuals engage in each form of behavior.

By P. E. Spector and M. T. Brannick, Published on 01/01/09

Cross-mode surveys are on the rise. The current study compares levels of response styles across three modes of data collection: paper-and-pencil questionnaires, telephone interviews, and online questionnaires. The authors make the comparison in terms of acquiescence, disacquiescence, and extreme and midpoint response styles. To do this, they propose a new method, namely, the representative indicators response style means and covariance structure (RIRSMACS) method. This method contributes to the literature in important ways. First, it offers a simultaneous operationalization of multiple response styles. The model accounts for dependencies among response style indicators due to their reliance on common item sets. Second, it accounts for random error in the response style measures. As a consequence, random error in response style measures is not passed on to corrected measures. The method can detect and correct cross-mode response style differences in cases where measurement invariance testing and multitrait multimethod designs are inadequate. The authors demonstrate and discuss the practical and theoretical advantages of the RIRSMACS approach over traditional methods.

Common method variance (CMV) is an ongoing topic of debate and concern in the organizational literature. We present four latent variable confirmatory factor analysis model designs for assessing and controlling for CMV hose for unmeasured latent method constructs, Marker Variables, Measured Cause Variables, as well as a new hybrid design wherein these three types of method latent variables are used concurrently. We then describe a comprehensive analysis strategy that can be used with these four designs and provide a demonstration using the new design, the Hybrid Method Variables Model. In our discussion, we comment on different issues related to implementing these designs and analyses, provide supporting practical guidance, and, finally, advocate for the use of the Hybrid Method Variables Model. Through these means, we hope to promote a more comprehensive and consistent approach to the assessment of CMV in the organizational literature and more extensive use of hybrid models that include multiple types of latent method variables to assess CMV.

Background:The purpose of this study was to evaluate the extent, if any, that the association between socio-ecological parameters and physical activity may be influenced by common method bias (CMB). Methods:This study took place between February and May of 2017 at a Southeastern University in the United States. A randomized controlled experiment was employed among 119 young adults.Participants were randomized into either group 1 (the group we attempted to minimize CMB)or group 2 (control group). In group 1, CMB was minimized via various procedural remedies,such as separating the measurement of predictor and criterion variables by introducing a time lag (temporal; 2 visits several days apart), creating a cover story (psychological), and approximating measures to have data collected in different media (computer-based vs. paper and pencil) and different locations to control method variance when collecting self-report measures from the same source. Socio-ecological parameters (self-efficacy; friend support; family support)and physical activity were self-reported. Results:Exercise self-efficacy was significantly associated with physical activity. This association (尾 = 0.74, 95% CI: 0.33-1.1; P = 0.001) was only observed in group 2 (control), but not in group 1 (experimental group) ( = 0.03; 95% CI: -0.57-0.63; P = 0.91). The difference in these coefficients (i.e., = 0.74 vs. = 0.03) was statistically significant (P = 0.04). Conclusion:Future research in this field, when feasible, may wish to consider employing procedural and statistical remedies to minimize CMB.