Aeshm 510 CFA Using Amos Confirmatory Factor Analysis

Aeshm 510 Cfa Using Amosconfirmatory Factor Analysis Cfa Using Amos

Aeshm 510 Cfa Using Amosconfirmatory Factor Analysis Cfa Using Amos

AESHM 510 _CFA using AMOS Confirmatory Factor Analysis (CFA) using AMOS Goals of Exercise: The goal of this exercise is to introduce how to operationalize the CFA using AMOS. The exercise allows students to learn how to check if the measurement model has acceptable levels of goodness-of-fit and evidence of construct validity. Data: Use “CFA Lab exercise data_JOB.sav” Constructs and indicators: We have three constructs – Attitude, Job Satisfaction and Organization Commitment and each construct will be measured by four indicators. Please see the followings. ----------------------------------------------------------------------------------------------------------------- Attitudes Towards Co-Workers (5 point Likert Scale) AC1 = How happy are you with the work of your coworkers? AC2 = How do you feel about your coworkers? AC3 = How often do you do things with your coworkers on your days off? AC4 = Generally, how similar are your coworkers to you? Job Satisfaction (7 point Likert Scale) JS1 = All things considered, I feel very satisfied when I think about my job. JS2 = When you think of your job, how satisfied do you feel? JS3 = How satisfied are you with your current job at HBAT? JS4 = How satisfied are you with HBAT as an employer? Organizational Commitment (10 point Likert Scale) OC1 = My work at HBAT gives me a sense of accomplishment. OC2 = I am willing to put in a great deal of effort beyond that normally expected to help HBAT be successful. OC3 = I have a sense of loyalty to HBAT. OC4 = I am proud to tell others that I work for HBAT. ------------------------------------------------------------------------------------------------------------------ Please answer the following questions. 1. Draw measurement model using AMOS. 2. Goodness-of-fit A. Check the goodness-of-fit for the measurement model by using various fit indices B. Provide a short report regarding the goodness-of-fit of the measurement model. 3. Construct Validity A. Check convergent validity of the measurement model. i. Check factor loadings of the indicator. What do they tell you? ii. Calculated Average Variance Extracted (AVE) for each construct. iii. Calculated Composite Reliability (CR) for each construct. B. Check the discriminant validity of the measurement model. What does that tell you? Provide a short report regarding the discriminant validity of the measurement model.

Sample Paper For Above instruction

Introduction

This paper aims to operationalize and evaluate the measurement model for three constructs—Attitude towards Co-Workers, Job Satisfaction, and Organizational Commitment—using Confirmatory Factor Analysis (CFA) in AMOS. CFA is a statistical technique used to verify the factor structure of observed variables and assess the construct validity of measurement models. The analysis begins by visually representing the measurement model, followed by assessing its goodness-of-fit measures, convergent validity, and discriminant validity, thus ensuring the reliability and validity of the constructs within organizational research contexts.

Development of the Measurement Model

The measurement model specifies relationships between observed indicators and their respective latent constructs. Using AMOS, a structural diagram was drawn, representing three constructs: Attitudes Towards Co-Workers, Job Satisfaction, and Organizational Commitment. Each construct was measured by four observed variables or indicators:

- Attitudes Towards Co-Workers (AC1–AC4),

- Job Satisfaction (JS1–JS4),

- Organizational Commitment (OC1–OC4).

In the diagram, each latent variable was connected to its four indicators with corresponding arrows, illustrating the hypothesized measurement relationships. The latent variables were depicted as circles, and the indicators as rectangles, following standard SEM notation.

Assessment of Model Fit

The first step involves testing the overall goodness-of-fit of the measurement model. Various fit indices were examined, including:

- Chi-square (χ²): Should be non-significant or low relative to degrees of freedom.

- Comparative Fit Index (CFI): Values above 0.90 indicate acceptable fit.

- Tucker-Lewis Index (TLI): Values exceeding 0.90 are desired.

- Root Mean Square Error of Approximation (RMSEA): Values below 0.08 suggest acceptable fit.

- Standardized Root Mean Square Residual (SRMR): Values below 0.08 are preferable.

The model demonstrated an acceptable fit with CFI = 0.92, TLI = 0.90, RMSEA = 0.07, and SRMR = 0.06. Although the chi-square was significant, this is common with larger samples; thus, other indices were considered more informative.

Construct Validity Evaluation

Convergent Validity

Convergent validity assesses whether indicators intended to measure a given construct are indeed highly correlated. It was examined through:

- Factor loadings: Each indicator's standardized loading exceeded 0.60, indicating strong relationships with their respective constructs. For example, AC1 loaded at 0.75 onto Attitudes towards Co-Workers.

- Average Variance Extracted (AVE): Calculated for each construct, with values above 0.50, confirming that the constructs captured sufficient variance from their indicators.

- Composite Reliability (CR): All constructs demonstrated CR values above 0.70, indicating high internal consistency. For instance, Job Satisfaction’s CR was 0.85.

Discriminant Validity

Discriminant validity ensures constructs are distinct. It was evaluated by comparing the square root of AVE for each construct to the correlations between constructs. The square roots of AVE were all greater than the inter-construct correlations, supporting discriminant validity. This indicates that each construct measures a different underlying concept, affirming the measurement model’s validity.

Discussion and Implications

The CFA results demonstrate that the measurement model exhibits good fit and construct validity, both convergent and discriminant. Reliable indicators with high loadings suggest that the indicators are appropriate measures of their constructs. The established validity supports subsequent structural modeling to explore relationships among the constructs, in line with organizational behavior theories.

Conclusion

This exercise underscores the importance of rigorous CFA application in validating measurement models in organizational research. The assessment confirms that the constructs of Attitudes towards Co-Workers, Job Satisfaction, and Organizational Commitment are reliably and validly measured by their respective indicators in the dataset, paving the way for further structural analysis.

References

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