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                  46410.pdf (author's version ) (Open Access)PLS’07, 05 september 200

Callaghan, W.

Henseler, J.

Ringle, C.M.

Wilson, B.

Callaghan, William

Wilson, Bradley

Ringle, Christian M.

Henseler, Jörg

University of Twente Research Information

Exploring Causal Path Directionality for a Marketing Model  Using Cohen’s Path Method1. William Callaghan. Swinburne University, Faculty of Business and Enterprise,Melbourne, Australia. strat@bigpond.com.au. Bradley Wilson. RMIT University, School of Applied Communication, Melbourne, Australia. brad.wilson@rmit.edu.auChristian M. Ringle University of Hamburg, Institute of Industrial Management, Hamburg, Germany. cringle@econ.uni-hamburg.de  Jörg Henseler. Nijmegen School of Management, Radboud University, Nijmegen, Netherlands. j.henseler@fm.ru.nl Introduction Researchers must frequently consider the directionality of relationships between variables when linking variables as well as when positing construct-to-construct relationships or when relations are specified at a higher order level of abstraction (Wilson, Callaghan & Stainforth, 2006). The psychometric literatures have been particularly mindful of these path directionality issues with measurement model specifications (item-to-construct directionality being either reflective or formative in orientation2).  Chin (1998b, p. IX) has recognised that, “a common and serious mistake often committed by research is used to inadvertently apply formative indicators in a (covariance-based) SEM analysis.” This measurement model specification c ncern has also been empirically proven by Jarvis, Mackenzie and Podsakoff (2003) who highlighted the magnitude of the problem. They found that 29% of constructs were modeled incorrectly. In the majority of cases items should have been treated in a formative fashion but were analyzed as if they were reflective in nature.  The objective of this paper is to revisit the relatively simple path analysis procedure to help explore issues of causal direction between variables of interest. In view of his enormous contribution to this field we refer to it as “Cohen’s path method” in this paper. We consider the                                                 1 We would like to acknowledge the inspiration and direction for this research through t e excellent conference and journal submissions of Heshan Sun and Ping Zhang. Theywere also very helpful in email correspondence in clarifying matters of analytical process. We would also like to thank Paul Cohen for answers to our queries. 2 We expect that the reader is familiar with basic measurement concepts such as reflective and formative measures and their unique characteristics. The interested reader is recommended to review Bollen & Lennox (1991); Chin (1998a), and Jarvis, MacKenzie & Podsakoff, (2003).   1methodological issues using a simple path example. We believe that social scientists focus on the vast array of fit measures and predictive diagnostics that currently exist within CBSEM and PLS and perhaps do not consider directionality issues po t hoc or the investigation of alternative models. In the theoretical development and model building stage assumptions are made about causal direction and are often not revisited. Not considering directionality issues with alternative models post hoc may be a small problem when th model is based on extremely well established theoretical underpinnings but this is often not the basis from which theorists are working from.  Cohen’s Path Analysis Method and its Application More sophisticated techniques to investigate path directionality exist, including Exploratory Tetrad Analysis (Glymour, Scheines, Spirtes, Kelly, 1987)] and Confirmatory Tetrad Analysis (CTA) (Bollen & Ting, 1993; Ting, 1995) and covari nce-based structural equation modeling (CBSEM) techniques via nested chi-square tests analysis techniques. Howev r, these approaches are complex and require computational approaches that are not yet widely used. The simplicity of Cohen’s path analysis therefore offers advantages to a range of research contexts.  Sun & Zhang (2006) distinguish between the “connectedness” (which is addressed in SEM approaches) and the actual directionality issues in investigating causal relationships and highlight the limitations of currently used approaches. The consequences of failing to explore alternative causal representations in establishing the “best” underlying causal sequence is clearly very important in marketing where often the theoretical underpinnings are comparatively weak. It may also influence other decisions such as the choice of an appropriate data analysis method and the number of items that are necessary in the questionna re representing a particular construct. Bollen and Lennox (1991) believe that if the measures are refl ctive, a small sample of measures from the population of measures of the construct is sufficient to represent the construct.   The simple principle involved in Cohen’s path analysis (Cohen et al., 1993) is that estimated correlations based on path analysis hould be as close as possible to the actual correlations. Cohen et al. (1993) describe it as a generalization of multiple linear regression that builds models with causal interpretations. The first stage in the analysis procedure is to calculate the actual correlation coefficients between all model variables (see Table 1 for a presentation of the specific actual correlations for the linkages in Figure 1). These actual correlation estimates are then compared with estimates derived from calculations involving the original (Model 1) and the alternative model (Model 2) where the one alternative causal link is involved. The substantive  2theoretical detail of the model is not germane to this paper. The data is from a commercial marketing research study on meat consumption where the dependent vari ble was number of servings of red meat consumed per week with X1 to X5 considered variables that might impact on this and included perceived health benefits, top of mind awareness, price perceptions and advertising recall. The nodes represent these variables or constructs. PLS path model estimation uses the SmartPLS 2.0 (Ringle et al. 2005) software application.  Cohen, Carlson, Ballesteros & St Amant (1993) observed that when examining paths between independent (X) and dependent (Y) variables in a path model, analysts can utilize two basic rules extending Sewall Wrights’ original path analysis rules. These rules enable the researcher investigate all the relevant paths between variables in the model. These are stated as: 1. A path cannot go through a node twice. 2. Once a node has been entered by an arrowhead, no node can be left by an arrowhead. The estimated correlations for each model and between any two variables linked by various paths can be estimated by considering all direct and indirect relationships. The total effects are compared to the actual correlations (Appendix 1) for each model. The actual coefficients may derive from SEM or related procedures that fit the overall model.  In this case model total squared errors are 0.035 for Model 1 and 0.082 for Model 2. The error changes from Model 1 to Model 2 indicated that the TSE changed by 130% (0.082-0.035/0.035).  To employ another Cohen contribution the effect size is calculated using Cohen’s d formula (Cohen, 1988) (see equation 1). Cohen (1988) indicated an effect size of 0.2 is small, 0.5 as medium and greater than 0.8 as large. The Cohen d value estimated for the difference between these models is 1.65 indicating that Model 1 is to be strongly preferred:  d = TSE2 – TSE1 / σ (1) where σ is the pooled standard deviation of the TSE values.  3Discussion and Conclusions Cohen’s path method needs to be regarded as exploratory in establishing causal relationships rather than definitive. Cohen’s path method is appealing when theory or the literature in an area do not offer clear guidelines on causal directions. The approach is not without limitations. One of the most important limitations is the use of correlations that do not take into account the error within the measures and by default constructs (if investigating structural models). This error becomes more imbedded within a structural model when utilising PLS modeling approaches. We therefore recommend that this exploratory technique be used when item and construct reliabilities are relatively high. The consistency at large assumption with PLS may overcome this to a degree (Dijkstra, 1983). Issues of attenuation may also need to be explored further as they affect the underlying correlations. Secondly, the decision rule in accepting the model result with the lowest total squared error is sensitive to variable “noise.” Cohen et al. (1993; 1994) suggest that other decision heuristics be valuated to supplement the total squared error (TSE) estimates. We urge further simulation research in this area to ascertain TSE robustness. Finally, it is important to acknowledge that there are now other quantitative techniques that assist when solving path direction questions with non-experimental designs. For instance, Exploratory TETRAD analysis, (Glymour et al., 1987), Confirmatory3 TETRAD analysis and CBSEM nested tests can be considered. In addition Jarvis et al. (2003) provide a comprehensive series of qualitative decision rules for examining the formative versus reflective issue in modeling. Cohen et al. (1993) and Gregory & Cohen (1994) have also integrated an algorithm with Cohen’s path method that finds the most plausible causal path given the data at hand. The Cohen’s path model algorithm runs through all variable path combinations4 (not just the one path that we have illustrated here). This work and the algorithm offers much potential for future research and subsequent integration within PLS software packages. We believe researchers can probably benefit from using a combination of both exploratory and confirmatory approaches in most research instances. Future validation studies utilizing experimental designs is the ultimate goal.                                                 3 We realise that directionality can never be confirmed “perse” and do not have the space required to discuss the varying opinions on the philosophy of causation. Experimental design methods are ideal but often not practical in complex SEM models to implement. This point is more in reference to the method being “confirmatory” in that is fixes a specific path for investigation. E.g., Confirmatory Tetrad Analysis (Bollen & Ting, 1993). 4 2N2 is the total number of models explored by the algorithm. Where N = no. of variables.  4 5Bibliography Bollen, K. A., & Lennox, R. (1991). “Conventional Wisdom on Measurement: A Structural Equation Perspective.” Psychological Bulletin, 110 (2), 305-314. Bollen, K. A. & Ting, K. F. (1993). “Confirmatory tetrad analysis.” In P. Marsden (ed.), Sociological Methodology. Washington, DC: American Sociological Society, 147-175. Chin W. W. (1998a). “The Partial Least Squares Approach to Structural Equation Modeling.” in Marcoulides, George A. (Ed.) Modern Methods for Business Research. Lawrence Erlbaum, Mahwah, NJ, 295-336. Chin, Wynne W (1998b). “Issues and Opinion on Structural Equation Modeling.” MIS Quarterly. (March), VII-XVI. Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences. Hillsdale, NJ: Lawrence Erlbaum. Cohen, P. R., Carlsson, A., Ballesteros, L., & Amant R. S. (1993). “Automating path analysis for building causal models from data. in Proceedings of the International Workshop on Machine Learning, 57-64. Cohen, Paul R., Hart, David R., St. Amant, R., Ballesteros, L. A., & Carlson, A. (1994). “Path Analysis Models of an Autonomous Agent in a Complex Environment.” In Selecting Models from Data: AI and Statistics IV, Peter Cheeseman and R. W. Oldford, Eds. Springer-Verlag, Technical Report 94-33, Dept. of Computer Science, University of Massachusetts/Amherst, 243-251. Dijkstra, Theo. (1983). “Some Comments on Maximum Likelihood and Partial Least Squares Methods.” Journal of Econometrics. 22, 67-90. Glymour, C., Scheines, R., Spirites, P., & Kelly, K. (1987). Discovering Causal Structure. Academic Press. Gregory, D. E., & Cohen, P.R. (1994). “Two Algorithms for Inducing Causal Models from Data.” In K owledge Discovery in Databases Workshop, Twelfth National Conference on Artificial Intelligence, Technical Report 94-42. Department of Computer Science, University of Massachusetts/Amherst. pp. 73-84. Jarvis, C. B., MacKenzie, S. B., & Podsakoff, P. M. (2003). “A Critical Review of Construct Indicators and Measurement Model Misspecification in Marketing and Consumer Research.” Journal of Consumer Research. 30 (September), 199-218. Ringle, C. M., Wende, S. & Will, A. (2005). SmartPLS 2.0 (beta). Hamburg: www.smartpls.de Sun, H. & Zhang, P. (2006). “Causal Relationships between Perceived Enjoyment and Perceived Ease of Use: An Alternative Approach.” Journal of the Association for Information Systems (JAIS), 7 (9), 618-645.  Ting, K. F. (1995). “Confirmatory tetrad analysis is SAS.” tructural Equation Modeling. 2, 163-171.  Wilson, B., Callaghan, W., & Stainforth, G. (2006). “An Investigation of Path Directionality Issues in a Branding Structural Model: An Application of Vanishing Tetrads Analysis.” Third International Business Research Conference, Melbourne, November 20-26th.  Zhang, P., Li, N., & Sun, H. (2006). “Affective quality and cognitive absorption: Extending technology acceptance research”, Proceedings of the 39th Hawaii International Conference on System Sciences (HICSS 2006). January 4-7, 2006, Kauai, Hawaii.  6 Model 1 Model 2 Path  Direct indirect Estimated correlation for indirect Total estimated correlations (including direct) Actual correlation Squared error Path  Direct indirect Estimated correlation for indirect Total estimated correlations (including direct) Actual correlation Squared error 1-6 none 1-5-6        0.152 0.315 0.330 0.00023 1-6 none 1-5-6 0.152 0.288 0.330 0.00179    1-5-3-6 -0.019           1-5-3-6 -0.019           1-3-6 0.112           1-3-6 0.112           1-4-6 0.051           1-4-6 0.051           1-3-4-6 0.019           1-5-3-6 -0.008       2-6 none 2-1-5-6        0.011 0.207 0.150 0.00326 2-6 none 2-1-5-6 0.011 0.207 0.150 0.00329    2-1-3-6 0.008           2-1-3-6 0.008           2-3-4-6 0.012                         2-1-4-6 0.004           2-1-4-6 0.004           2-3-6 0.069           2-3-6 0.069           2-4-6 0.092           2-4-6 0.092           2-3-4-6 0.012           2-4-3-6 0.026           2-1-3-4-6 0.001                         2-1-5-3-4-6 0.000           2-1-5-3-6 0.000           2-1-5-3-6 -0.001           2-1-5-3-6 -0.001       3-6           3-6 3-4-6 0.075 0.505 0.620 0.01327 3-6 3-6 none  0.430 0.620 0.036104-6              4-6 none 0.340 0.290 0.00250 4-6 4-6 4-3-6 0.095 0.435 0.290 0.02091                            5-6             5-6 5-3-4-6 -0.009 0.349 0.280 0.00482 5-6 5-6 5-3-6 -0.052 0.358 0.280 0.00615    5-3-6 -0.052                   1-2               2-1 none 0.070 0.043 0.00073 1-2 2-1 none 0.070 0.043 0.000731-3             1-3 1-5-3 -0.044 0.216 0.265 0.00244 1-3 1-3 1-5-3 -0.044 0.216 0.265 0.002441-4             1-4 1-3-4 0.057 0.197 0.170 0.00075 1-4 1-4 1-3-4 0.057 0.207 0.170 0.00138    1-5-3-4 -0.010                     1-5               1-5 none 0.370 0.400 0.00090 1-5 1-5 none 0.370 0.400 0.000902-3             2-3 2-1-3 0.018 0.175 0.150 0.00063 2-3 2-3 2-1-3 0.018 0.175 0.150 0.00063    2-1-5-3 -0.003           2-1-5-3 -0.003       2-4              2-4 2-3-4 0.035 0.305 0.280 0.00064 2-4 2-4 none 0.270 0.280 0.000103-4               3-4 none 0.220 0.260 0.00160 3-4 3-4 none 0.220 0.260 0.001603-5               5-3 none -0.120 -0.180 0.00360 3-5 5-3 none -0.120 -0.180 0.003604-5 none       -0.07   4-5 none 5-3-4 -0.0264 -0.026 -0.07 0.00190 Total Squared error (TSE) 0.03537  Total Squared error (TSE) 0.08152  Appendix 1. The Results of Path Analysis for Models 1 and 2. 

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Exploring Causal Path Directionality for a Marketing Model:Using Cohen’s Path Method

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Exploring Causal Path Directionality for a Marketing Model Using Cohen’s Path Method

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Exploring Causal Path Directionality for a Marketing Model Using Cohen’s Path Method

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