Estimates Of Path Coefficients Research Articles

Investigating weight constraint methods for causal-formative indicator modeling.

Causal-formative indicators are often used in social science research. To achieve identification in causal-formative indicator modeling, constraints need to be applied. A conventional method is to constrain the weight of a formative indicator to be 1. The selection of which indicator to have the fixed weight, however, may influence statistical inferences of the structural path coefficients from the causal-formative construct to outcomes. Another conventional method is to use equal weights (e.g., 1) and assumes that all indicators equally contribute to the latent construct, which can be a strong assumption. To address the limitations of the conventional methods, we proposed an alternative constraint method, in which the sum of the weights is constrained to be a constant. We analytically studied the relations and interpretations of structural path coefficients from the constraint methods, and the results showed that the proposed method yields better interpretations of path coefficients. Simulation studies were conducted to compare the performance of the weight constraint methods in causal-formative indicator modeling with one or two outcomes. Results showed that higher biases in the path coefficient estimates were observed from the conventional methods compared to the proposed method. The proposed method had ignorable bias and satisfactory coverage rates in the studied conditions. This study emphasizes the importance of using an appropriate weight constraint method in causal-formative indicator modeling.

On the Path Analysis Techniques and Decomposition of Correlation Coefficients

Path analysis is an extension of multiple regression technique used to evaluate causal model by examining the relationship among a set of variables. The process of decomposition of path correlation coefficients and estimation of path coefficients was investigated in this work. The outcome of a set of interrelated variables was generated using path diagrams and path coefficients. Decomposition of correlation coefficients into different effects was also carried out through the method of structural equations using path analysis theorem. In order to show the proportion of total variation of the dependent variable, the decision coefficient of a specified pathway was constructed from direct coefficient of determination. The path coefficients were estimated from the specified structural equations by ordinary least squares regression method, and it was deduced that each of the coefficients of determination had large effect on some other variables.

Correcting for Unreliability and Partial Invariance: A Two-Stage Path Analysis Approach

In path analysis, using composite scores without adjustment for measurement unreliability and violations of factorial invariance across groups lead to biased estimates of path coefficients. Although joint modeling of measurement and structural models can theoretically yield consistent structural association estimates, estimating a model with many variables is often impractical in small samples. A viable alternative is two-stage path analysis (2S-PA), where researchers first obtain factor scores and the corresponding individual-specific reliability coefficients, and then use those factor scores to analyze structural associations while accounting for their unreliability. The current paper extends 2S-PA to also account for partial invariance. Two simulation studies show that 2S-PA outperforms joint modeling in terms of model convergence, the efficiency of structural parameter estimation, and confidence interval coverage, especially in small samples and with categorical indicators. We illustrate 2S-PA by reanalyzing data from a multiethnic study that predicts drinking problems using college-related alcohol beliefs.

Reducing Salesperson Turnover Intentions via Organizational Market Orientation and Selective Hiring: A Job Demand-Resources Approach

ABSTRACT Purpose This study is designed to respond to repeated calls for research on sales person retentionby building upon a mature research stream to identify ways to reduce turnover in boundary spanning employees and the resultant effect it has on organizational productivity. Specifically, this research draws on the Job Demands-Resources model to explore the effect of employee perceptions of firm market orientation as a way to reduce role stressors and subsequently turnover intentions. It also looks at employee traits that may serve as a buffer to the role stress to turnover intentions link and can be part of the hiring selection process (in this case grit). In so doing, this research uses a sample of early career salespeople to examine the effects of a firm’s market orientation (MO) and selective hiring for specific traits (level of grit) on a salesperson’s intention to quit using Job Demand-Resources as a framework. Design/Methodology/Approach The authors utilized a questionnaire to collect data from a sample consisting of 260 respondents who were employed full-time and worked in either B2B or B2C sales as boundary-spanning employees. The measures utilized in this study have been used in previous research and have demonstrated acceptable reliability and validity. Analysis of the latent constructs was conducted using PLS-SEM. The model was assessed as specified in the hypotheses and was estimated using the PLS algorithm to obtain path coefficient estimates. Findings Findings suggest that organizations can benefit internally from strong MO by mitigating stressors placed on boundary-spanning employees. Those who perceived strong MO in their employer experienced more clearly defined roles, which led to decreased role ambiguity. These increased levels of job satisfaction, which reduced turnover intention. Additionally, Grit appears to only moderate the link between role conflict and job satisfaction, which suggests that grit can serve as an additional resource salespeople can tap into in response to the demands of role stressors thereby reducing their likelihood of leaving their positions. Research Implications In this research, inclusion of market orientation as an antecedent to role stressors in the Job Demands-Resources model expands the conceptualization of the construct as it relates to salespeople in that market orientation is something the firm can control directly. This opens research opportunities to use this model in the sales literature. In addition, the job-demands-resources model expands the role of grit beyond positive psychology to be viewed as a resource in stressful situations and puts the role of grit into a theoretical framework for use in business contexts. As such, the current work suggests that the grit scale can be used to replace measures used for similar constructs such as tenacity. Practical Implications This research suggests that role ambiguity can be reduced by the presence of a market orientation. This reduction in role ambiguity leads to an increase in job satisfaction which leads to reduced turnover intention. Thus, this study indicates that there may be a cost-saving benefit (e.g. recruiting and retention efforts, lost sales, etc.) that accrues to firms with a market orientation due to reduced salesperson turnover via role perceptions. This supports that the idea that organizations can benefit from market orientation not only externally, but also internally in terms of mitigating stressors placed on boundary-spanning employees. Therefore, companies should push for adoption of market orientation in order to alleviate some key role stressors placed on their salespeople in addition to the already established benefits from external customers. The link between grit, job satisfaction, and turnover intentions is another positive finding. As expected, job satisfaction helps reduce turnover intentions; however, the relationship is moderated by grit in such a way that people with higher grit show a weaker link between job satisfaction and turnover intentions while those with lower levels of grit show a stronger link between job satisfaction and turnover intentions. This raises human resource issues in the hiring process: human resource managers should work to identify salespeople with high levels of grit in order to better minimize turnover intentions given the cost of turnover to organizations. Originality This research expands existing research by examining the effects of a firm’s market orientation (MO) and selective hiring for specific traits (level of grit) on a salesperson’s intention to quit using Job Demand-Resources as a framework Combining these two literature streams – market orientation and salesperson grit – is novel and will inspire new thinking and foster new insights. This study serves as a starting point for future research to examine firm controllable resources (market orientation) along with employee traits that can act as a buffer (grit) when resources are scarce.

Factors that Influence Professional Socialization in Nursing Students: A Multigroup Analysis

Background: Nursing professional socialization significantly influences the sustainable development of nursing students’ careers. This study aims to examine the factors that influence professional socialization in nursing students. Methods: This is a quantitative study of a sample of 108 nursing students (80 females and 28 males). The Simulation Learning Effectiveness Scale, Caring Assessment Report Evaluation Q-sort, Self-Reflection and Insight Scale, and Holistic Nursing Competence Scale were used to collect the data on the independent variables. The Team Skills Scale, Nursing Image as a Profession questionnaire, and Nursing Professional Commitment Scale were used to assess the level of students’ nursing professional socialization, which is an outcome construct variable. A multigroup analysis was used to examine the professional socialization research model. Results: The full model findings show that individual learning effectiveness had a significantly positive effect on nursing students’ caring behavior and on self-reflection and insight. In addition, caring behavior and self-reflection and insight had a significantly positive effect on nursing competence. Finally, nursing competence also had a significantly positive effect on nursing professional socialization. Overall, all independent variables were significant predictors of nursing professional socialization, explaining 36.7% of the variance in the integrated model of professional socialization. In addition, a permutation test revealed no differences in the two comparisons’ path coefficient estimates in the model (female vs. male). Conclusion: It is important to further strengthen nursing students’ self-learning effectiveness to promote their self-reflection and caring behavior. Increasing students’ nursing competence is a key factor in improving their professional socialization.

Investigate the Importance of the Promotion factor in the Marketing of Therapeutic Tourism Services

Background: Medical tourism represents the travel of individuals to receive health care and treatment to another country. One of the important factors in the marketing of medical tourism is promotion. The purpose of this study is to investigate the importance of promoting factor in the proposed marketing pattern of medical tourism services.
 Methods: This study is a Descriptive-Correlational research method and an applied research in terms of purpose conducted in2017. The statistical population of the study consisted of all staff of medical centers and active units in the medical tourism area of the medical Science Universities in Iran and 630 of them were selected as a statistical sample by using simple sampling method. The data collection tool was a questionnaire with 63 questions related to tourism marketing components with scoring based on Likert scale. The validity of the questionnaire was evaluated by experts' judgment and reliability was measured by using Cronbach's alpha coefficient (0.82) and Test re Test (0.92) in SPSS22.
 Results: According to the standard estimates of path coefficients, the promotion factor with score of 0.98 consisted of 8 components ranked first among others was determined. 
 Conclusion: Considering the importance of the promotion component, Policy makers in the field of medical tourism services should pay special attention to promotion and related issues in order to properly identify the Capacities and capabilities of active centers globally.
 
 Key words: Medical Tourism, Marketing , Pattern

Correlation-Based Meta-Analytic Structural Equation Modeling: Effects of Parameter Covariance on Point and Interval Estimates

More and more researchers use meta-analysis to conduct multivariate analysis to summarize previous findings. In the correlation-based meta-analytic structural equation modeling (cMASEM), the average sample correlation matrix is used to estimate the average population model. Using a simple mediation model, we illustrated that random effects covariation in population parameters can theoretically bias the path coefficient estimates and lead to nonnormal random effects distribution of the correlations. We developed an R function for researchers to examine by simulation the impact of random effects in other models. We then reanalyzed two real data sets and conducted a simulation study to examine the magnitude of the impact on realistic situations. Simulation results suggest parameter bias is typically negligible (less than .02), parameter bias and root mean square error do not differ across methods, 95% confident intervals are sometimes more accurate for the two-stage structural equation modeling approach with a diagonal random effects model, and power is sometimes higher for the traditional Viswesvaran-Ones approach. Given the increasing popularity of cMASEM in organizational research, these simulation results form the basis for us to make several recommendations on its application.

Consistent Partial Least Squares Path Modeling via Regularization.

Partial least squares (PLS) path modeling is a component-based structural equation modeling that has been adopted in social and psychological research due to its data-analytic capability and flexibility. A recent methodological advance is consistent PLS (PLSc), designed to produce consistent estimates of path coefficients in structural models involving common factors. In practice, however, PLSc may frequently encounter multicollinearity in part because it takes a strategy of estimating path coefficients based on consistent correlations among independent latent variables. PLSc has yet no remedy for this multicollinearity problem, which can cause loss of statistical power and accuracy in parameter estimation. Thus, a ridge type of regularization is incorporated into PLSc, creating a new technique called regularized PLSc. A comprehensive simulation study is conducted to evaluate the performance of regularized PLSc as compared to its non-regularized counterpart in terms of power and accuracy. The results show that our regularized PLSc is recommended for use when serious multicollinearity is present.

Variation Sharing

The most fundamental problem currently associated with structural equation modeling employing the partial least squares method is that it does not properly account for measurement error, which often leads to path coefficient estimates that asymptotically converge to values of lower magnitude than the true values. This attenuation phenomenon affects applications in the field of business data analytics; and is in fact a characteristic of composite-based models in general, where latent variables are modeled as exact linear combinations of their indicators. The underestimation is often of around 10% per path in models that meet generally accepted measurement quality assessment criteria. The authors propose a numeric solution to this problem, which they call the factor-based partial least squares regression (FPLSR) algorithm, whereby variation lost in composites is restored in proportion to measurement error and amount of attenuation. Six variations of the solution are developed based on different reliability measures, and contrasted in Monte Carlo simulations. The authors' solution is nonparametric and seems to perform generally well with small samples and severely non-normal data.

Natural environment influencing people’s affinity for solitude

This study examined the relationships between people’s affinity for solitude and solitude preference in natural environments to determine whether solitude experienced in natural environments is an outcome of people’s affinity for solitude or vice versa. Data was collected from college students to measure their affinity for solitude and solitude preference in natural environments. We tested two competing hypotheses by examining the standardized estimates of path coefficients between latent factors. Results indicated that the measure of affinity for solitude was not a significant predictor of solitude preference in the natural environments; while solitude preference in the natural environments contributed to the formation of affinity for solitude, β=0.269, SE=0.062, t-value=5.891, p<0.001. Study findings will benefit landscaping and local park management by providing useful information about how people view and use natural environments as a place for being alone.

A Simulation Study of Polychoric Instrumental Variable Estimation in Structural Equation Models

Data collected from questionnaires are often in ordinal scale. Unweighted least squares (ULS), diagonally weighted least squares (DWLS) and normal-theory maximum likelihood (ML) are commonly used methods to fit structural equation models. Consistency of these estimators demands no structural misspecification. In this article, we conduct a simulation study to compare the equation-by-equation polychoric instrumental variable (PIV) estimation with ULS, DWLS, and ML. Accuracy of PIV for the correctly specified model and robustness of PIV for misspecified models are investigated through a confirmatory factor analysis (CFA) model and a structural equation model with ordinal indicators. The effects of sample size and nonnormality of the underlying continuous variables are also examined. The simulation results show that PIV produces robust factor loading estimates in the CFA model and in structural equation models. PIV also produces robust path coefficient estimates in the model where valid instruments are used. However, robustness highly depends on the validity of instruments.

Consistent Partial Least Squares Path Modeling

This paper resumes the discussion in information systems research on the use of partial least squares (PLS) path modeling and shows that the inconsistency of PLS path coefficient estimates in the case of reflective measurement can have adverse consequences for hypothesis testing. To remedy this, the study introduces a vital extension of PLS: consistent PLS (PLSc). PLSc provides a correction for estimates when PLS is applied to reflective constructs: The path coefficients, inter-construct correlations, and indicator loadings become consistent. The outcome of a Monte Carlo simulation reveals that the bias of PLSc parameter estimates is comparable to that of covariance-based structural equation modeling. Moreover, the outcome shows that PLSc has advantages when using non-normally distributed data. We discuss the implications for IS research and provide guidelines for choosing among structural equation modeling techniques.

Multicollinearity in path analysis of maize (Zea mays L.)

The objective was to evaluate the effect of multicollinearity on three types of path analysis (traditional, under multicollinearity and traditional with elimination of variables) in maize (Zea mays L.). We used data from 14 maize cultivar competition trials. Seven explanatory variables (number of days to 50% tasselling, plant height, ear height, relative ear position, number of plants, number of ears and prolificity) and a response variable (grain yield) were measured for each cultivar of each trial. For each trial, descriptive statistics, correlation coefficients between the seven explanatory variables (correlation matrix X'X) and correlation coefficients between each explanatory variable and grain yield (correlation matrix X'Y) were calculated. The multicollinearity in the X'X correlation matrix was determined by using three methods, including tolerance, condition number and matrix determinant. Path analysis was conducted by using a system of normal equations, X'Xβˆ = X'Y, in three distinct ways (traditional, under multicollinearity and traditional with elimination of variables). The tolerance, condition number and matrix determinant, indicated high degree of multicollinearity between the seven explanatory variables. The addition of the k = 0.10 constant and the elimination of variables were both effective for reducing the degree of multicollinearity. Traditional path analysis, with a high degree of multicollinearity in the correlation matrix generates path coefficient estimates without biological significance that should not be considered. Using traditional path analysis with the elimination of highly correlated variables is more adequate than path analysis under multicollinearity for estimating the true direct and indirect effects of path analysis in maize crop.

The effect of learning organization culture on the relationship between interpersonal trust and organizational commitment

AbstractThe primary purpose of this research was to assess the effect of learning organization culture on the linkage between interpersonal trust and organizational commitment. The study sample was obtained from employees of two major Korean conglomerates. Online questionnaires were completed by 321 respondents. Structural equation modeling (SEM) was used to detect the effects of learning organization culture on the basis of the model fit to data comparisons and the significance of path coefficient estimates in the hypothesized model. The results suggest that learning organization culture works as a mediating variable to explain the association between interpersonal trust and organizational commitment. Recommendations for future research and implications for human resource development research and practice are discussed.

Overt and Subtle Racial Discrimination and Mental Health: Preliminary Findings for Korean Immigrants

We examined differential effects of overt and subtle forms of racial discrimination on 2 dimensions of mental health-positive affect and depressive symptoms, and explored the mediating roles of emotional arousal and cognitive appraisal. Cross-sectional survey data were collected through face-to-face interviews with a sample (N=180) of adult Korean immigrants living in Toronto, Ontario. Maximum likelihood estimates of path coefficients were obtained using structural equation models. Perceived racial discrimination was associated with both the erosion of positive affect and depressive symptoms. Overt discrimination was associated with the erosion of positive affect, and subtle discrimination was associated with depressive symptoms. Effects of subtle discrimination on depressive symptoms were mediated through cognitive appraisal. The results emphasize the salience of subtle discrimination for the mental health of Asian immigrants. Experiences of overt racial bias seemed to be of little importance for the levels of depressive symptoms among those in our sample, although the experience of blatant discrimination tended to reduce positive mood.

Using Parcels to Convert Path Analysis Models Into Latent Variable Models

The biasing effects of measurement error in path analysis models can be overcome by the use of latent variable models. In cases where path analysis is used in practice, it is often possible to use parcels as indicators of a latent variable. The purpose of the current study was to compare latent variable models in which parcels were used as indicators of the latent variables, path analysis models of the aggregated variables, and models in which reliability estimates were used to correct for measurement error in path analysis models. Results showed that point estimates of path coefficients were smallest for the path analysis models and largest for the latent variable models. It is concluded that, whenever possible, it is better to use a latent variable model in which parcels are used as indicators than a path analysis model using total scale scores.

Teacher practices associated with students' sense of the classroom as a community

Data from observations of 232 elementary classrooms and from student questionnaires were used to test a model linking teacher classroom practices to students' sense of the classroom as a community (assessed by questionnaire) through intermediate effects on students' classroom behavior. The model was generally confirmed and showed that teacher practices (emphasis on prosocial values, elicitation of student thinking and expression of ideas, encouragement of cooperation, warmth and supportiveness, andreduced use of extrinsic control) were related to student classroom behaviors (engagement, influence, andpositive behavior), which, in turn, were related to students'sense of community. Teachers' encouragement of cooperative activities appeared to be particularly important in this sequence. The appropriateness of the model was tested for schools serving populations that were both high and low in level of poverty, and all estimates of path coefficients were found to be invariant across these groups.

Inferring Multiple Causality: The Limitations of Path Analysis

1. Review of published ecological studies using path analysis suggests that path analysis is often misused and collinearity is a moderate to severe problem in a majority of the analyses. 2. Multiple regression is shown to be related to path analysis, and thus collinearity, which is a well-known problem in regression analysis, can also be a problem in path analysis. Collinearity occurs when independent variables are highly correlated and causes estimates of standardized partial regression coefficients and of path coefficients to be less precise, less accurate and prone to rounding error. 3. A review of 24 path analyses in 12 papers revealed incorrectly calculated path coefficients in 13 cases and problems with collinearity in 15 cases. Estimates of path coefficients are biased and lack precision if there is collinearity. 4. An additional 40 path analyses could not be evaluated because of incomplete information. Most studies lack sufficient sample size to justify the use of path analysis. Problems associated with the use of categorical variables, which were used in six cases, are unappreciated. 5. These problems suggest that path coefficients may often mislead ecologists about the relative importance of ecological processes.

On Fraas and Newman's goodness‐of‐fit test for structural equation models

Fraas and Newman's (1994) binomial test of goodness of fit of structural equation models is based on the assumption that counts of path estimates consistent with a researcher's expectations can be viewed as, binomial random variables. In this article I argue that these counts do not have binomial distributions because of the lack of independence among estimates of path coefficients of structural equation models.

On the Estimation of Path Coefficients for Unmeasured Variables from Correlations Among Observed Variables

This paper discusses the use of unmeasured variables in path models. Problems of estimation of the path coefficients of a path model are explored when unmeasured variables are utilized as both causes and effects (intervening variables). The paper concludes with a discussion of conditions for the identification of a path model containing unmeasured variables and some remarks on the substantive interpretation of unmeasured variables. B eginning with H. M. Blalock's (1960; 1961a; 1961b) extension of Herbert Simon's (1957) seminal essay on spurious correlation, sociologists have become increasingly aware of the utility of linear causal models as a procedure for bridging the gap between sociological theory on the one hand and the results of classical statistical analyses on the other. That is, the practices of: (1) writing a causal model as a set of simultaneous equations which are assumed to represent the structural relations among a given set of variables, and (2) engaging in statistical analysis to estimate the values of the parameters of the model, are rapidly becoming standard aspects of sociological research methodology. Finally, the conditions under which the researcher can infer that the model adequately reflects the empirical phenomena under investigation (make causal inferences) have been explicated and synthesized. There have been a number of contributions * This is a revision of a paper presented at the annual meeting of the Southern Sociological Society, 1969. It is based on Kenneth C. Land, Explorations in Mathematical Sociology, unpublished Ph.D. dissertation, University of Texas at Austin, 1969. The author is grateful to the anonymous reviewers for helpful comments on an earlier draft of the paper. This content downloaded from 157.55.39.162 on Thu, 11 Aug 2016 05:45:38 UTC All use subject to http://about.jstor.org/terms PATH COEFFICIENTS FOR UNMEASURED VARIABLES 507 by sociologists to the development of causal modeling methodology. Blalock, for example, has been most active in the formalization of criteria for making causal inferences (e.g., Blalock, 1961c; 1961d; 1962a; 1962b; 1963a; 1965; 1967a; 1968a; 1968b). Recently, he (Blalock, 1966; 1967b; 1967c) has also studied some particular cases of the identification problem in sociology. Duncan (1966) introduced sociologists to path analysis, a technique developed by the geneticist Sewall Wright (1960). Finally, Boudon (1968) extended path analysis to the cases of dichotomous variables and nonadditive relationships. The technique of path analysis seems particularly useful to sociologists as a linear modeling device. The primary advantage of path analysis accrues from the fact that it deals with standardized structural coefficients (cf. Boudon, 1968:202-208). This property yields a theorem which facilitates the decomposition of the zero-order correlation coefficient for two variables into parameter estimates of the direct and indirect effects of one variable on the other through the postulated causal structure (cf. Duncan, 1966:5-6). The technique has been well-developed for the case of one-way, i.e., recursive, causal systems (e.g., Heise, 1969a; Land, 1969). Boudon (1968:227-233) has also shown how the method can be applied to a sociological example of lagged reciprocal interaction. A second advantage of path analysis lies in its capacity to treat hypothetical (unmeasured) variables in a postulated causal structure (cf. Land, 1969). There are two general cases for which sociologists will be interested in utilizing unmeasured variables in a causal model. The first of these arises in the study of measurement error. This application of path analysis has been explored by Siegel and Hodge (1968), Heise (1969b), and Costner (1969), and the reader is referred to those sources for a treatment of the problem. Briefly, Siegel and Hodge utilize path analysis to study discrepancies between census and sample survey measures of the same variables. On the other hand, Heise develops formulas for estimating the reliability and stability of a measure from data collected at three or more points in time. Finally, Costner derives conditions under which the parameters of a path model can be estimated if one has multiple indicators of each variable. The common characteristic of all of these applications of path analysis is that the hypothetical (unmeasured) variables enter the path models only as causes of the observed variables. A second case in which sociologists will utilize unmeasured variables is as variables which intervene between measured variables in a causal model. The methodology for path analysis in this case has not as yet been developed for sociologists, and it is that goal to which this paper is de-