Analysis of questionnaires using SPSS: practical steps from data coding to interpreting the results

The analysis of the questionnaire does not begin by pressing a command within the SPSS program; it begins before that by reviewing the study questions, the way the instrument is constructed, and the type of variables. Many of the errors that appear in the final tables are caused by inaccurate encoding or the selection of a test that does not fit the research question. Therefore, the researcher needs a clear path that begins with the data file and ends with an explanation related to the assumptions, not just by copying the program numbers.

This guide explains the basic steps for analyzing questionnaires using SPSS, and what should be reviewed at each stage so that the results are understandable and discussable.

What should be determined before entering the data into SPSS?

Start by asking the same research question. Do you want to describe the views of participants? Compare two groups? Study the relationship between two variables? Or find out which variables are able to predict a specific outcome? The answer determines the type of analysis required, so it is not recommended to choose the statistical test before determining the dependent and independent variables and the level of measurement for each variable.

Also review the final questionnaire version, the number of axes, the reverse items, and the answer options. Having a clear copy of the tool helps you create variable names that are understood within SPSS and reduces the risk of confusion between items during analysis.

Data file processing and encoding of the answers

It is preferable that each participating row represents one participant, and that each column represents one variable or item. Use short and organized names for the variables, then add a clear description for each variable within the Label box so that it is easier to identify in the tables.

In Likert scale questions, the text responses are converted into numerical values in a fixed order. For example, responses can start with «I strongly disagree» and end with «I strongly agree». It is important that the coding direction remains consistent and that the reverse items are reviewed before calculating the overall score for the axis.

• A coding guide was created that explains the meaning of each value.
• Identify a clear symbol for the missing data when needed.
• Review the textual variations and the numbers that were entered in different formats.
• Keep an original copy of the data file before any modification.

Cleaning the data before starting the analysis

Data cleaning is not a formal step. Examine illogical values, repetitions, and percentages of missing data, and responses that appear incomplete. Repetition tables can be used in SPSS to detect values outside the range of responses, such as the appearance of the number 7 in a 1-to-5 choice question.

When missing data exists, there is no single appropriate decision for all studies. The way to handle it depends on the size, pattern, and type of the problem. Removing cases or replacing values without justification may change the result, so the decision and its reason must be recorded in the research methodology.

Test of the stability of the questionnaire axes

After verifying the validity of the coding, the researcher proceeds to examine the consistency of the items for each dimension. The Cronbach’s Alpha coefficient is often used for this purpose, but its value is not interpreted separately from the number of items, the nature of the measure, and the scientific field. Moreover, high stability does not in itself prove the validity of the instrument.

You can find a more detailed explanation in the article. Reliability and validity testing in SPSS. Before deleting any item, review its theoretical content and its relationship with the rest of the items, rather than relying on automatic changes in the Alpha value.

Descriptive statistics of the questionnaire

Descriptive statistics provide an initial picture of the sample and the responses. It may include repetitions and percentages for nominal variables, averages and standard deviations of quantitative scores when their use is appropriate. This stage helps to detect unexpected patterns before moving on to testing hypotheses.

It is not enough to present a large table for each item. Choose the tables that serve the purpose of the study question, and make it clear to the reader what the values mean rather than retyping every number that appears in the table.

Choosing the right statistical test

The choice of the test depends on the design of the study, the type of variables, the number of groups, and statistical assumptions. It is useful to divide the decision according to the objective:

Studying differences between groups

When the goal is to compare two or more groups, difference tests may be appropriate based on the nature of the data. And if the comparison involves more than one group, the manual can be reviewed. ANOVA Analysis of Variance in SPSS.

Studying the relationship between variables

If the question concerns the existence and direction of a relationship, the researcher may use appropriate correlation coefficients. The strength and direction of the relationship must be interpreted, avoiding the assumption that the correlation is sufficient evidence of causality.

Forecasting or interpreting change

When studying the ability of one or more variables to explain a dependent variable, regression models can be used if their conditions are appropriate. An article explains Linear regression analysis in SPSS How to read and interpret the basic tables.

How are the SPSS results interpreted within the research?

Good interpretation links the result to the research question. Mention the test used, the basic statistical value, and the level of significance; then explain whether the result supports or does not support the hypothesis. Next, discuss the scientific meaning of the result and the limitations of its interpretation.

Avoid statements that are not permitted by the design of the study. A statistically significant result does not always mean that the effect is large or practically important; a non-significant result does not necessarily prove the absence of any association. The context, sample size, and measurement quality are all elements that enter into the reading.

Common mistakes in questionnaire analysis

• Start the analysis before reviewing the coding and reverse items.
• Using a statistical test because its name is common, without linking it to the type of variables.
• Deleting the scale entries only for the purpose of increasing the coefficient of stability.
• Copy the complete SPSS output without selecting the tables associated with the assumptions.
• The confusion between statistical significance and the practical importance of the result.
• The interpretation of the association as a confirmed causal relationship.

When do you need help in analyzing the questionnaire?

You may need a specialized review when the questionnaire includes several sub-topics, missing data and extreme values, requires more than one test to determine the results, or when you need to formulate the results and tables in a way that complies with the university's guidelines.

The Club’s Scientific Academy offers Statistical Analysis Services for Researchers and Graduate Students Starting from reviewing the data file and selecting the tests, to interpreting the results and preparing the report. For researchers within the Kingdom, reviewing can be done Academic and Research Services in Saudi Arabia.

To obtain an initial assessment of the data and scope of work, Send the search requirements via WhatsApp.

Frequently asked questions

Can all the questionnaires be analyzed using SPSS?

SPSS can be used for a wide range of questionnaires, but the analysis method varies depending on the design of the tool, the type of variables, and the study questions.

Should the Cronbach’s Alpha be calculated for each questionnaire?

Stability is often reviewed for each axis, measuring a specific concept, rather than for a group of disparate items simply because they are included in a single questionnaire.

Are the averages enough to analyze the survey results?

The averages are part of the description, but they alone do not answer questions about differences, relationships, or predictions. The choice depends on the goal of the study.

What files are required to start the analysis?

Usually we need the data file, a copy of the questionnaire, the objectives of the study or its questions and assumptions, and any special requirements of the university or supervisor.