PRJ70406 Chapter 4 SPSS Sample Taylor’s University

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This PRJ70406 Chapter 4 SPSS sample shows how a Chapter 4 (Analysis and Results) can be written for a quantitative group project of the PRJ70406 Business Management Project type at Taylor’s University. It follows the sub-section order the brief sets out, from 4.1 to 4.9, and ends with a short model for the Chapter 5 discussion. The topic, data and every number are illustrative: they were built to be internally consistent, not collected from real respondents. Your own Chapter 4 must report your own dataset. The original brief is on the PRJ70406 Business Management Project Assessment 1 question page, and the planning advice behind this layout is in our PRJ70406 Chapter 4 analysis and results guide.

Illustrative study set-up for the PRJ70406 Chapter 4 SPSS sample

4.1 Introduction

This chapter reports the analysis of the survey data in the order planned in Chapter 3. It describes the respondents, explains how the data were screened, tests reliability and normality, and then examines the four hypotheses using Pearson correlation and multiple linear regression in IBM SPSS Statistics. The sample size follows the power analysis of Faul et al. (2009), and the choice of tests follows Hair et al. (2019).

4.2 Demographic profile of respondents

Of the 189 usable responses, 55.0% came from women and 37.6% from the 25–34 age group. Most respondents held a bachelor’s degree, and 82.0% used an e-wallet at least weekly, so the sample matches the target population of active digital-payment users.

Table 4.1 Demographic profile of respondents (n = 189, illustrative)

4.3 Data preparation, missing values and coding

The online survey returned 205 questionnaires. Sixteen were removed before analysis: seven showed straight-lining (the same answer to all 20 items), five failed the attention-check item, and four came from people who had never used an e-wallet. That left 189 cases.

Six cases had one blank item each, which is 0.16% of all data cells. Little’s MCAR test was not significant, χ²(214) = 231.7, p = .19, so the values were treated as missing completely at random and replaced using the expectation-maximisation method. No item was reverse-worded. Responses were coded 1 to 5, and each construct score is the mean of its four items. No univariate outliers were found beyond z = ±3.29.

4.4 Reliability test

Cronbach’s alpha was computed for each construct. Following Nunnally and Bernstein (1994), a value of .70 or above was treated as acceptable, and Tavakol and Dennick (2011) were used to guard against over-high values that suggest redundant items.

Table 4.2 Reliability of constructs (n = 189, illustrative)

All alpha values exceed .70 and no item, if deleted, would raise its construct’s alpha by more than .01, so all 20 items were retained.

Need your own SPSS output turned into tables and wording like these? Our final year project helper team can review your Chapter 4 against the Appendix 1 structure.

4.5 Descriptive statistics

Mean scores were interpreted with a common banding convention: 1.00–2.33 low, 2.34–3.66 moderate and 3.67–5.00 high.

Table 4.3 Descriptive statistics of constructs (n = 189, illustrative)

Respondents reported the strongest agreement on adoption intention (M = 4.05), while social influence scored lowest (M = 3.64), which suggests peers matter less than usefulness in this sample.

4.6 Normality test

Normality was judged from skewness and kurtosis, histograms and Q-Q plots. With 189 cases, the Shapiro–Wilk test is sensitive to small departures, so it was reported but not used alone. Absolute skewness below 3 and kurtosis below 10 are acceptable for regression (Kline, 2016), and z-values within ±3.29 are acceptable for samples between 50 and 300 (Kim, 2013).

Table 4.4 Normality indicators (n = 189, illustrative)

The standard errors were 0.177 for skewness and 0.352 for kurtosis. Three Shapiro–Wilk results were significant, which is common for Likert composites, but every z-value stayed inside ±3.29 and the Q-Q plots followed the diagonal closely. The data were therefore treated as approximately normal and parametric tests were retained.

4.7 Inferential analysis

Pearson correlation

Table 4.5 Pearson correlation matrix (n = 189, illustrative)

All correlations were positive and significant at p < .001. No pair of predictors exceeded .85, so multicollinearity was not a concern at this stage.

Multiple linear regression

Adoption intention was regressed on the four predictors. The model was significant, F(4, 184) = 46.28, p < .001, and explained 50.2% of the variance (R² = .502, adjusted R² = .491). The Durbin–Watson value of 1.94 indicated independent residuals.

Table 4.6 Regression coefficients, dependent variable: adoption intention (illustrative)

All VIF values were far below 5 and tolerance values were above .20, confirming that multicollinearity was not present (Hair et al., 2019).

4.8 Hypotheses results

4.9 Summary of the chapter

Three of four hypotheses were supported. Performance expectancy was the strongest predictor, followed by perceived security, while promotional incentives did not significantly predict adoption intention once the other factors were controlled. These results feed directly into the Chapter 5 discussion.

Chapter 5 starter: how to write the discussion

Interpret before you compare. A model paragraph for H1 might read: “Performance expectancy was the strongest predictor of adoption intention (β = .408, p < .001). This is consistent with the unified theory of acceptance and use of technology, which positions expected performance as a core driver of intention (Venkatesh et al., 2003). In practice, wallet providers should show time savings at checkout rather than rely on generic cashback messages.”

For the unsupported H4, do not hide it. Give a reasoned explanation, such as incentives being short-lived and already common across providers, then state the practical implication and the limitation. Your own comparison must cite the recent Scopus or Web of Science articles from your Chapter 2, not only textbooks.

How this extract earns marks

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About the expert

Business strategy and market research questions like this are covered by Aina R., who holds a PhD in Marketing & Business Strategy from the University of Malaya. Her listed specialisms are Marketing Case Studies, SWOT & PESTEL Analysis, and Consumer Psychology & Market Research, and her profile notes she is trusted for coursework and MBA projects from UM, Taylor’s, and UCSI.

References

Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149–1160. https://doi.org/10.3758/BRM.41.4.1149

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning.

Kim, H.-Y. (2013). Statistical notes for clinical researchers: Assessing normal distribution (2) using skewness and kurtosis. Restorative Dentistry & Endodontics, 38(1), 52–54. https://doi.org/10.5395/rde.2013.38.1.52

Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). Guilford Press.

Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.

Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach’s alpha. International Journal of Medical Education, 2, 53–55. https://doi.org/10.5116/ijme.4dfb.8dfd

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540