Mastering SPSS for SDG Research: CCC8013 Data Analysis Tutorial

Introduction to SPSS for SDG Research

In the CCC8013 The Process of Science course, you are required to produce a research report that connects scientific inquiry with the United Nations Sustainable Development Goals (SDGs). One of the most powerful tools for quantitative data analysis is SPSS (Statistical Package for the Social Sciences). This tutorial will guide you through the essential steps of using SPSS to analyze data for your assignment, from data entry to interpreting results. Whether you are examining survey responses on climate action (SDG 13) or analyzing health data for SDG 3, SPSS can help you uncover meaningful patterns.

Why SPSS Matters for Your CCC8013 Assignment

Your assignment requires you to apply quantitative/data literacy and critical thinking skills. SPSS allows you to move beyond simple averages and explore relationships between variables. For example, if you are investigating how study habits affect academic performance among university students, SPSS can help you test hypotheses and present findings in tables and figures. This aligns with the intended learning outcomes of CCC8013, particularly ILO 2 (apply intellectual and practical skills) and ILO 3 (integrate learning in new settings).

Step 1: Data Collection and Entry in SPSS

Before using SPSS, you need to collect data. For your report, you might use a questionnaire with Likert scales (e.g., 1 = Strongly Disagree to 5 = Strongly Agree) or multiple-choice questions. Suppose you are studying the impact of remote learning on student well-being (SDG 3: Good Health and Well-being). You survey 100 students and record their responses in an Excel file. Import this data into SPSS by going to File > Open > Data and selecting your Excel file. Ensure each row represents a participant and each column a variable (e.g., age, gender, satisfaction score).

Variable View Setup

In SPSS, switch to Variable View to define your variables. For a Likert-scale question, set Type to Numeric, Measure to Ordinal, and add value labels (e.g., 1 = 'Strongly Disagree'). This step is crucial for accurate analysis and clear output.

Step 2: Descriptive Statistics

Descriptive statistics summarize your data. To compute them, click Analyze > Descriptive Statistics > Frequencies for categorical variables (e.g., gender) or Descriptives for continuous variables (e.g., age). For example, you might find that 60% of respondents are female and the average satisfaction score is 3.8 (SD = 0.9). These numbers help you describe your sample and check for data entry errors.

Create a table in SPSS by selecting Analyze > Tables > Custom Tables. Drag variables into rows and columns, then click OK. Copy the table into your report with a clear caption, such as Table 1: Demographic Characteristics of Participants (N=100).

Step 3: Inferential Statistics – t-Tests and Correlations

Your assignment may require you to compare groups or examine relationships. For instance, you might ask: Do students who use online learning platforms have higher well-being scores than those who do not? Use an independent samples t-test (Analyze > Compare Means > Independent-Samples T Test). The output will show a p-value. If p < 0.05, the difference is statistically significant. Report the t-statistic, degrees of freedom, and p-value in text: t(98) = 2.34, p = 0.021.

To explore relationships between two continuous variables (e.g., hours of study per week and GPA), use Pearson correlation (Analyze > Correlate > Bivariate). A positive correlation (e.g., r = 0.45, p < 0.001) indicates that more study hours are associated with higher GPA. Present this as a scatterplot with a trendline, which you can create in SPSS by selecting Graphs > Chart Builder and choosing Scatter/Dot.

Step 4: Visualizing Your Data

Figures are mandatory (3–5 per report). SPSS offers bar charts, histograms, and boxplots. For example, to compare well-being scores across age groups, create a bar chart: Graphs > Legacy Dialogs > Bar. Select 'Clustered' and define category axis and variable. After generating the chart, double-click it to open the Chart Editor and customize colors, labels, and titles. Save the chart as an image (e.g., PNG) and insert it into your report with a caption like Figure 1: Mean Well-Being Scores by Age Group.

Step 5: Interpreting Results for Your Discussion

Your discussion should link findings to the SDG you chose. For example, if your data shows that students with access to mental health resources have higher well-being, you can argue that universities should invest in such resources to support SDG 3. Compare your results with existing literature, citing at least five academic sources. Use in-text citations like (Smith, 2022). Remember to include a reference list in APA or another consistent style.

Common SPSS Pitfalls to Avoid

Missing data: Check for blanks before analysis. Use Analyze > Descriptive Statistics > Frequencies to identify missing values.
Incorrect variable types: Ensure categorical variables (e.g., gender) are set as Nominal, not Scale.
Ignoring assumptions: For t-tests, check homogeneity of variances using Levene's test (provided in SPSS output). If violated, use the 'Equal variances not assumed' row.

Trend Example: Applying SPSS to Analyze Vaccine Hesitancy (SDG 3)

Imagine you collected survey data in June 2026 on COVID-19 vaccine hesitancy among college students. Using SPSS, you could run a binary logistic regression to predict which factors (e.g., misinformation exposure, trust in science) increase hesitancy. This mirrors real-world efforts by public health officials to design targeted campaigns. Such analysis not only fulfills assignment requirements but also develops skills relevant to careers in data science, public health, and policy.

Conclusion

Mastering SPSS for your CCC8013 assignment is a valuable skill that extends beyond the classroom. By following these steps—data entry, descriptive statistics, inferential tests, and visualization—you can produce a compelling research report that demonstrates scientific inquiry and quantitative literacy. Remember to choose an SDG topic you are passionate about, collect data ethically, and present your findings clearly. Good luck!

References

Field, A. (2024). Discovering Statistics Using IBM SPSS Statistics (6th ed.). Sage Publications.
United Nations. (2024). The 17 Goals. https://sdgs.un.org/goals
Pallant, J. (2023). SPSS Survival Manual: A Step by Step Guide to Data Analysis Using IBM SPSS (8th ed.). Open University Press.
Smith, J. (2022). The impact of remote learning on student mental health. Journal of Educational Psychology, 114(3), 456–470.
World Health Organization. (2025). Vaccine hesitancy: A growing challenge. https://www.who.int/news-room/spotlight/vaccine-hesitancy