Quantitative Data Analysis for Case Study Research Using SPSS
In business research, case studies are frequently supported by quantitative evidence derived from survey data. This assessment evaluates your ability to execute and interpret quantitative data analysis using SPSS in support of a case study–based research problem.
You will work with a Corporate Reputation survey dataset, which has been widely used in academic research and teaching materials. The dataset originates from empirical studies examining corporate reputation and its related constructs (such as perceived quality, trust, satisfaction, and loyalty) and is commonly used to illustrate relationships between multiple constructs within an integrated research model.
Although the dataset is often analysed using structural equation modelling techniques, in this assessment you are required to analyse it using SPSS, focusing on fundamental statistical procedures that are appropriate for case study research and managerial decision-making.
Dataset Access
The dataset for this assessment can be downloaded at mgm5113oct2025. You should then import the dataset into SPSS independently and use only the provided dataset for all analyses. For further background on the dataset, students may refer to the original study by Eberl and Schwaiger (2005) or the data article by Sarstedt et al. (2023.
MGM5113 Assessment Tasks
You are required to complete all tasks below and present your findings in a structured report:
Task 1: Data Import and Preparation
- Import the provided Excel dataset into SPSS.
- Check and define: variable labels, value labels, measurement levels (nominal, ordinal, scale).
- Identify missing values in the dataset.
- Apply an appropriate missing value imputation method.
- Briefly justify your chosen method.
Task 2: Descriptive Analysis
- Generate descriptive statistics (mean, standard deviation, minimum, maximum) for all measurement items.
- Produce at least one relevant graphical output (e.g. bar chart or histogram).
- Describe the general pattern of responses in relation to corporate reputation and its related constructs.
Task 3: Exploratory Factor Analysis
- Conduct exploratory factor analysis on the measurement items.
- Apply an appropriate extraction and rotation method.
- Identify and explain the resulting factor structure.
Task 4: Reliability Analysis
- Conduct reliability analysis for each construct identified in Task 3.
- Report Cronbach’s Alpha values.
- Comment on the internal consistency of each construct using accepted thresholds.
Task 5: Construct Score Computation
- Compute the mean score for each validated construct.
- Clearly label the newly created construct variables (e.g., CUSL_MEAN).
- Explain why construct means are used instead of individual items for subsequent analysis.
Task 6: Mean Comparison Analysis
- Using the CUSA construct, decide on a suitable cut-off value to divide respondents into two groups: ‘High Satisfaction’ and ‘Low Satisfaction’.
- Clearly explain and justify your chosen cut-off (e.g. mean, median, or theoretical midpoint).
- Conduct an independent samples t-test to compare at least one other construct (e.g. Corporate Reputation, Trust, or Loyalty) between the two satisfaction groups.
- Interpret the results in managerial terms.
Task 7: Multiple Regression Analysis
- Refer to the original corporate reputation research model implied by the dataset.
- Decide on a suitable dependent variable (e.g. COMP, LIKE, CUSA or CUSL).
- Select two or more independent constructs that theoretically explain the chosen dependent variable.
- Run a correlation analysis on the selected constructs.
- Run a normality test on the selected constructs.
- Conduct a multiple regression analysis.
- Interpret the results with reference to: model fit (R²), significance and direction of predictors, multicollinearity (VIF / Tolerance).
Format Requirements:
- Written assignment (1500–2000 words).
- Front cover includes student name, matric number and email address.
- Use APA references where applicable.
- Selected important SPSS outputs must be included in an Appendix.
- Interpretation must be written in business-oriented language, not purely statistical terms
Submit via e-pembelajaran platform before deadline 16.01.2026.
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