Building path analysis model in SEM with SPSS Amos

By Riya Jain & Priya Chetty on May 23, 2022

Path analysis is a statistical method used for establishing a causal relationship between variables. It is used when there are multiple variables in a study. It is an important Structural Equation Modeling (SEM) analysis type used commonly by researchers for testing the hypothesis. The previous article explained how to conduct path analysis in SPSS Amos. This article demonstrates through a case study how to interpret the findings from a path analysis model in your research.

The impact of job satisfaction on organisational commitment using the path analysis model

Employees are an integral part of any organization. If they are satisfied with their job, they will be more committed to their organisation (Ćulibrk et al., 2018). In this case study, the path analysis model of SEM is used to examine the influence of job satisfaction on organisational commitment. For this, first-hand data was obtained from 350 employees of a company using a close-ended questionnaire. It contained questions related to organisational commitment and job satisfaction.

Job satisfaction and organisational commitment were the main variables in the questionnaire. Each of them contained sub-variables which are as follows.

Sub-variables of job satisfaction and organisational commitment
Figure 1: Sub-variables of job satisfaction and organisational commitment

SEM for impact assessment

The path analysis model used showing the linkage between organisational commitment and job satisfaction is shown below.

Path analysis model for job satisfaction
Figure 2: Path analysis model for job satisfaction

In the above model. JS1, JS2, JS3 and JS4 denote the sub-variables of job satisfaction. OC1, OC2, OC3 and OC4 denote sub-variables of organisational commitment.

The first step towards the finalisation of the path analysis model is to establish the validity and reliability of the model.

Reliability and validity of path analysis model in SEM

Model fitness in an SEM model is established only after reliability and validity are proven. This is denoted through four types of validity:

  • Convergent validity
  • Internal consistency
  • Composite reliability
  • Discriminant validity

The below table presents the results of the model for this present case study.

 AVECRCronbach alphaSQRT(AVE)
Job satisfaction0.760.850.850.87
Organization Commitment0.620.780.780.79
Table 1: Path model reliability and validity

In the above table, AVE (Average variance extracted) determines the convergent validity of the model. It should be at least 0.5 (Alarcón & Sánchez, 2015). Here, the AVE value for job satisfaction is 0.76 and organizational commitment is 0.62 > 0.5, thus, there is a presence of convergent validity in the model.

The examination of internal consistency by the Cronbach alpha test depicts each construct linkage with the other. Herein, as the value of Cronbach alpha shows that job satisfaction is 0.85 and job satisfaction is 0.78 > 0.7 (Alarcón & Sánchez, 2015),  thus, internal consistency is present in the model.

Composite reliability depicts each construct’s significance in the model. For job satisfaction, the CR value is 0.85 while organizational commitment is 0.78. As the values are more than 0.7,  composite reliability exists in the model.

Lastly, discriminant validity defines the difference between each construct from others. The below table shows that the correlation value of organizational commitment and job satisfaction is 0.70. As the value is less than the square root of the average variance extracted, i.e. 0.87 and 0.79, discriminant validity is present in the model.

 Job satisfactionOrganization Commitment
Job satisfaction0.87 
Organization Commitment0.700.79
Table 2: Discriminant validity of path model

Since all the conditions are being met, this path model is valid and reliable.

Model fitness in SEM

The next step is to assess the model’s fitness. For this, values of different indices are examined. Results for the model are shown below.

Name of categoryName of indexIndex valueAdequate fit
Absolute fit measureCMIN/Df (normed/relative Chi-Square)5.119Less than 5
GFI (Goodness of fit)0.935Greater than 0.90
AGFI (adjusted goodness of fit)0.882Greater than 0.90
RMSEA (root mean square of approximation)0.109Less than 0.10
Incremental fit measureNFI (normal fit index)0.93Greater than 0.90
CFI (comparative fit index)0.942Greater than 0.90
TLI (Tucker Lewis index)0.919Greater than 0.90
IFI (Incremental fit index)0.943Greater than 0.90
Parsimonious fit measurePGFI (parsimony goodness of fit index)0.519Greater than 0.50
PCFI (parsimony comparative fit index)0.673Greater than 0.50
PNFI (parsimony normed fit index)0.664Greater than 0.50
Table 3: Model fitness indices of path analysis in SEM

In the above table, ‘Absolute fitness measure’ indices represent that CMIN/Df is 5.119 > 5, GFI is 0.935 > 0.90, AGFI is 0.882 < 0.9, and RMSEA is 0.109 > 0.10. As 3 out of 4 indices are not fulfilling fitness requirements, the model is not absolutely fit.

In ‘incremental fitness’ indices, the value of NFI is 0.93 > 0.90, CFI is 0.942 > 0.9, TLI is 0.919 > 0.9, and IFI is 0.943 > 0.9. As all the incremental fitness measures have required indices values, the model is incrementally fit.

For ‘parsimonious fitness measure’, PGFI is 0.519 > 0.5, PCFI is 0.673 > 0.5 and even PNFI is 0.664 . 0.5. Hence, the model is incrementally and parsimoniously fit but not absolutely.

For improving the fitness of the model, based on the modification values computed in AMOS, the covariance-based linkage is developed between variables. With this, the results of modified path model fitness indices are shown below.

Name of categoryName of indexIndex valueAdequate fit
Absolute fit measureCMIN/Df4.346Less than 5
GFI0.948Greater than 0.90
AGFI0.896Greater than 0.90
RMSEA0.098Less than 0.10
Incremental fit measureNFI0.958Greater than 0.90
CFI0.958Greater than 0.90
TLI0.934Greater than 0.90
IFI0.958Greater than 0.90
Parsimonious fit measurePGFI0.474Greater than 0.50
PCFI0.616Greater than 0.50
PNFI0.608Greater than 0.50
Table 4: Modified model fitness indices of path analysis in SEM

In the above table, a majority of the indices in the ‘Absolute fit measure’ are within the required limit and even if AGFI is close to the desired value, a modified model is absolutely fit. For ‘incremental fit measure’, all indices are greater than 0.9. Thus, the model is incrementally fit. Lastly, for ‘parsimonious’ indices, the majority of indices value is within the desired limit, thus, the model is parsimoniously fit.

Thus, this model can now be used for examining the impact and understanding the influence of job satisfaction on organisational commitment.

Impact determination using SEM

After establishing the model’s fitness, reliability and validity, the next step is the final step. The hypothesis is tested to establish the relationship between employee satisfaction and organisational commitment. The hypothesis is as follows:

H01: Job satisfaction does not have a significant influence on the organizational commitment

HA1: Job satisfaction has a significant influence on the organizational commitment

The results of the hypothesis are shown below.

Dependent VariableIndependent VariableEstimateS.E.C.R.P-value
Organizational CommitmentJob Satisfaction0.890.0712.680.00
Table 5: Impact assessment using path analysis of SEM

Table 5 depicts that as the standard error (S.E.) value is 0.07 which is low, there are fewer biases present in the model to determine the relationship. Further, the p-value of the model is 0.00 < 0.05 and the CR value is 12.68 > 1.96 (z-value at 5% significance). Thus, the null hypothesis H0 is rejected. Hence, this study proves that there is an impact of job satisfaction on organizational commitment.

Why path analysis?

The path analysis model is the most widely used SEM model to examine the direct and indirect relationships, especially in the management field. This method enables the linkage building between multiple variables but does not have complex relationships. For instance, it cannot create a model with two mediators or a mediator and moderator. Thus most researchers adopt a path analysis model to assess a single impact of either direct or indirect nature. The above case study had only two constructs- job satisfaction and organizational commitment. The aim was to simply examine the direct impact. There were no complex constructs. Therefore path analysis was most suitable. Alternative methods such as MANOVA would have failed to derive the true impact because it does not take into consideration latent constructs. SPSS Amos is one of the simplest software for conducting SEM.

References

  • Alarcón, D., & Sánchez, J. A. (2015). Assessing convergent and discriminant validity in the ADHD-R IV rating scale : User-written commands for Average Variance Extracted ( AVE ), Composite Reliability ( CR ), and Heterotrait-Monotrait ratio of correlations ( HTMT ). Spanish STATA Meeting, 1–39.
  • Ćulibrk, J., Delić, M., Mitrović, S., & Ćulibrk, D. (2018). Job Satisfaction, Organizational Commitment and Job Involvement: The Mediating Role of Job Involvement. Frontiers in Psychology, February.
NOTES

Priya is the co-founder and Managing Partner of Project Guru, a research and analytics firm based in Gurgaon. She is responsible for the human resource planning and operations functions. Her expertise in analytics has been used in a number of service-based industries like education and financial services.

Her foundational educational is from St. Xaviers High School (Mumbai). She also holds MBA degree in Marketing and Finance from the Indian Institute of Planning and Management, Delhi (2008).

Some of the notable projects she has worked on include:

  • Using systems thinking to improve sustainability in operations: A study carried out in Malaysia in partnership with Universiti Kuala Lumpur.
  • Assessing customer satisfaction with in-house doctors of Jiva Ayurveda (a project executed for the company)
  • Predicting the potential impact of green hydrogen microgirds (A project executed for the Government of South Africa)

She is a key contributor to the in-house research platform Knowledge Tank.

She currently holds over 300 citations from her contributions to the platform.

She has also been a guest speaker at various institutes such as JIMS (Delhi), BPIT (Delhi), and SVU (Tirupati).

 

I am a master's in Economics from Amity University. Having a keen interest in Econometrics and data analysis, I was a part of the Innovation Project of Daulat Ram College, Delhi University. My core expertise and interest are in environment-related issues. Apart from academics, I love music and exploring new places.

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