A Step-by-Step Guide for Dissertation Statistical Analysis using SPSS

What is SPSS?

SPSS is an abbreviation of Statistical Package for Social Sciences and is also known as IBM SPSS Statistics. SPSS is a software package used for statistical analysis of data. Although SPSS was intended to be used in social sciences, its use has expanded to other domains such as business, marketing, healthcare and education research.

Check out our data analysis examples to learn how a data analysis is conducted in a dissertation among various disciplines.

Conducting a statistical analysis using SPSS can be daunting, especially if a person is using it for the first time. However, with a step-by-step approach, one can conduct statistical analysis effectively and draw meaningful inferences. Here is a step-by-step guide for dissertation statistical analysis:

Step 1- Preparation of Data

Also called data preprocessing, data preparation involves cleaning the data by removing any duplicate content and checking for missing values.
Organize the data into SPSS-friendly formats by converting them into CSV and Excel formats.
Import the data in SPSS by going to “File”> “Open”>” Data”.

Step 2- Descriptive Statistics

After importing the data, navigate to “Analyze”, then choose “Descriptive Statistics” and “Frequencies”.
After selecting the variables, click “Ok”.
SPSS will create descriptive statistics such as measures of central tendency (mean, median), measures of variability (standard deviation, range), and distribution for each variable.

Step 3- Data visualization

Create graphical representations like charts and graphs to gain insights.
Make your graphs more informative and visually appealing by changing their appearance.

Step 4- Hypothesis Testing

Inferential statistics may be required depending on the research questions.
To conduct t-tests and ANOVA, go to “Analyze”>” Compare means”. For regression analysis, go to “Analyze”> “Regression”.
Choose your dependent and independent variables, then modify your settings based on your hypotheses.
SPSS will now generate outputs for hypothesis testing like p-values, confidence intervals and effect sizes.

Step 5- Interpretation of data

Analyze the results obtained from the statistical tests carefully.
The p-values should be interpreted to assess the significance of your findings. Statistical significance is often indicated by a p-value less than 0.05 (=0.05).
Examine effect sizes to determine the practical importance of your findings.
Consider the limitations of the research as well as any possible confounding variables.

Step 6- Advanced analysis

Use advanced statistical studies such as factor analysis, cluster analysis, or MANOVA if necessary.
Continue in the same manner as before, selecting relevant analysis methods depending on your research questions.
Step 7- Reporting and Visualization

Organize your findings into a logical and clear structure.
To present your findings, use clear and visually appealing tables, graphs, and charts.
Include any essential explanations and interpretations in your dissertation content to give context.

Step 8- Review and Validation

Assess your analysis for consistency and accuracy.
To validate your findings and interpretations, ask for feedback from your professor.
Based on the feedback, make any necessary changes.

Following these steps will enable students to conduct dissertation statistical analysis. However, it is crucial for students to be acquainted with the basics of SPSS to improve the efficiency of their statistical analysis.

To know more about how statistical analysis is conducted in management studies, check out our example of Examining the impact of workplace social media usage on business.

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