What Is SPSS Software? How to Read Output for Your Thesis

What Is SPSS Software

SPSS is a tool for quantitative data analysis, commonly used with survey data stored in .sav, .xlsx, or .csv files. You can use SPSS to clean data, produce descriptive statistics, assess scale reliability, run EFA, correlation, regression, t-tests, ANOVA, and several other tests.

The point to remember is that SPSS does not choose your research model for you. The software calculates results from the variables you enter and produces output tables. Your conclusions still need to follow your research questions, hypotheses, measurement approach, and the conditions under which each analysis is appropriate.

A basic workflow looks like this: enter the data, check the data, choose the analysis, read the statistics, compare them with the criteria, and write the results. When you run Cronbach's Alpha, for example, SPSS calculates the internal consistency among the items in the same scale. When you run regression, SPSS estimates the size and direction of the relationships between variables in the model.

If you have just opened your file, check the two main windows first. Data View contains each respondent's data in a row and each variable in a column. Variable View contains variable names, labels, data types, coded values, and measurement levels. A small error in Variable View, such as entering a quantitative variable as a string, can prevent the variable from appearing in an analysis dialog.

The Role of SPSS Software in Quantitative Research

In a quantitative thesis, SPSS usually sits in the middle of the workflow. You should already have a model, hypotheses, questionnaire, and survey data before opening the software. After running the analysis, you use the output to write the results and discussion chapters.

SPSS helps answer specific questions. What are the demographic characteristics of the sample. Do the items measure the same construct. Is the data suitable for EFA. Are the independent variables related to the dependent variable. How much variance does the regression model explain. Is a hypothesis supported according to the criteria you established.

Separate three tasks that are often mixed together. The first is the technical operation, such as selecting Analyze > Scale > Reliability Analysis. The second is checking the conditions, such as reviewing missing values, outliers, or VIF. The third is interpretation, which means turning a number into a statement connected to a hypothesis. Producing an output table completes only the first task.

SPSS is suitable when your topic requires common statistical analyses and your data are organised in rows and columns. If your model contains latent variables, several paths, and a need to assess the measurement model and structural model at the same time, SmartPLS may be more suitable. AMOS is commonly used for CB-SEM, while JASP and R offer greater flexibility or lower software costs, but require you to learn a different interface or coding workflow.

You also do not need to download an unclear version of SPSS just because you see the phrase “free SPSS download”. An unauthorised version can create risks for your computer, cause file loss, or leave you unable to explain your analysis procedure when questioned. Use a version provided by your university, a legal trial version, or an alternative tool that fits your supervisor's requirements.

What Counts as an Acceptable SPSS Result

The question “what SPSS value is acceptable” is usually a question about one statistic in the output. SPSS does not have one universal passing score. Cronbach's Alpha, KMO, factor loading, p-value, R², and VIF have different meanings, so identify the correct table before choosing a threshold.

The table below summarises criteria commonly used in quantitative theses. Read each threshold together with the research context, the number of items, and the type of model.

These figures are reference points, not automatic instructions for deleting items. A low Alpha, for example, may result from having only a few items or from inconsistent scale content. A low factor loading needs to be considered together with cross-loadings, theoretical meaning, and how the model changes after the item is removed.

For sample size, (Hair et al., 2010) gives a rule of 5 to 10 observations for each item. For regression, (Tabachnick and Fidell, 2013) gives a sample-size rule of 50 + 8m, where m is the number of independent variables. Explain how you determined the sample size in the methods chapter instead of simply writing that the data set is large enough.

How to Read SPSS Software Output

Each output table answers a different question. Do not read the tables in the order in which they appear and copy every number into your thesis. Start with the purpose of the analysis, then find the correct row and column.

The example below is illustrative output, not the result of a real study. Suppose you are checking the “Purchase Intention” scale, which contains three items, YD1, YD2, and YD3, using Analyze > Scale > Reliability Analysis.

In this example, the Alpha for the full scale is 0.812. Every corrected item-total correlation is above 0.30. The Cronbach's Alpha if Item Deleted column does not mean that an item must be deleted whenever the value is higher. Compare the Alpha after deletion with the current Alpha, and check the substantive meaning of the item at the same time.

If you run EFA, you will see the KMO and Bartlett's Test, Total Variance Explained, and Rotated Component Matrix tables. KMO indicates whether the data are suitable for factor analysis. Bartlett's Test checks whether the correlation matrix differs from an identity matrix. In the Rotated Component Matrix, review the factor loading of each item on each factor and pay attention to items that load strongly on more than one factor.

If you run regression, the important tables are usually Model Summary, ANOVA, and Coefficients. R Square in Model Summary shows the proportion of variance in the dependent variable explained by the model. The ANOVA table is commonly used to assess the overall significance of the model. The Coefficients table reports B, Beta, t, and Sig. for each independent variable.

When you write the chapter, you do not need to insert the entire output, which may run for dozens of pages. Select the necessary tables, give each table a clear title, and explain the number immediately after the table.

What to Do When SPSS Does Not Meet the Criteria

If a statistic does not meet the criterion, do not delete the item immediately just to make the table look better. Working through the checks in order helps you preserve the logic of the analysis and explain your decisions to your supervisor.

Check the Coding and Original Data

Open Variable View and check reverse-coded items, value labels, and data types. A reverse-coded item that has not been recoded can reduce Alpha substantially. You should also inspect blank cells, values outside the Likert range, and rows with incorrect codes.

Check the Items and Original Questionnaire

Review the corrected item-total correlation, factor loading, and the content of the item. If an item has both a low correlation and poor conceptual fit, deleting it has a stronger basis. Record the item code, the reason for deletion, and the result after rerunning the analysis.

Rerun the Analysis in a Controlled Way

After deleting one item, rerun the same analysis and save the new output. Do not keep deleting several items simply because you want Alpha to increase. If EFA produces a structure different from the one you expected, check cross-loadings and the number of factors before changing the model.

Report Limitations Instead of Hiding the Result

If the data still do not meet the criteria after reasonable checks, report the limitation and discuss it with your supervisor. Collecting more data is appropriate only when you still have time, the questionnaire remains valid, and the new sample follows the same selection criteria. Do not collect additional data simply to remove responses that are unfavourable to your hypothesis.

If the data contain extremely unusual values, you can read the guidance on outliers in quantitative data. For a model with a moderating variable, distinguish the procedure from moderating and moderator, because this is not a procedure for repairing an Alpha statistic.

Distinguishing SPSS from SmartPLS and Similar Tools

SPSS is menu-based statistical software, while SmartPLS focuses on PLS-SEM. Both can process survey data, but you should not mix the criteria from the two analytical frameworks.

In SPSS, you commonly combine items into representative variables and then run regression or a difference test. In SmartPLS, you assess outer loading, CR, AVE, HTMT, VIF, path coefficient, R², and bootstrapping for a PLS-SEM model. CFI, TLI, and RMSEA belong to CB-SEM and should not be included in a guide to evaluating a SmartPLS model.

SPSS is also easy to confuse with Excel. Excel is suitable for entering, checking, and performing simple data-table operations. SPSS has specialised statistical procedures, stores variable settings, and produces more structured output. Both tools still depend on correct input data and correct coding.

You can also read what is a scale if you are still distinguishing items, scales, and latent variables. If your topic includes a mediation mechanism, separate that concept from a moderating variable by reading mediator. Choose the software after identifying the model and type of hypothesis.

Common Mistakes When Using SPSS Software

The first mistake is entering data without creating a coding plan. Before opening SPSS, prepare a list of variable codes, scales, valid values, and reverse-coded items. Variable names should be short, contain no spaces, and remain consistent between the questionnaire and the data file.

The second mistake is running every analysis at once and then selecting the tables with the most attractive results. This can produce a model with weak logic. Save each file or output version in sequence: cleaning, descriptive statistics, reliability, EFA, correlation, regression, and hypothesis testing.

The third mistake is looking only at Sig. Even when the p-value is small, you still need to check the direction of the effect, the coefficient, the effect size, and the confidence interval when available. When the p-value is large, do not write that the independent variable has “absolutely no effect”. State that the hypothesis was not supported in the tested sample and model.

The fourth mistake is copying the output table word for word into Chapter 4. A long table full of abbreviations makes the results difficult to follow. Keep the statistic names as SPSS prints them, but reformat the necessary table, add the unit or interpretation, and state clearly that the figures come from your research sample.

Frequently asked questions

What is SPSS software and what is it used for?

SPSS is software for quantitative data analysis. In a thesis, it is commonly used to clean survey data, describe the sample, assess scales, run EFA, correlation, regression, and tests of differences.

Can I download SPSS for free?

Use a version provided by your university, a legal trial version, or a suitable alternative. An installation file from an unclear source can create security risks and make it difficult to explain your analysis procedure.

Where should a beginner start with SPSS?

Start with Variable View, Likert-scale coding, and opening .sav or .csv files. Then practise a small workflow consisting of descriptive statistics, Cronbach's Alpha, and reading the Reliability Statistics and Item-Total Statistics tables.

What SPSS value is acceptable?

There is no single number for all SPSS analyses. Identify the statistic you are reading, such as Alpha, KMO, factor loading, VIF, or Sig., and then compare it with the appropriate threshold and source. A threshold is only a reference point, so you still need to consider the conditions of use and the theory.

What should I do if SPSS does not show a variable in the dialog box?

Check the data type in Variable View, because string variables do not appear in some procedures. Then check the variable name, missing data, and the way you selected variables in Data View. If the problem remains, save a copy of the file and try opening the data in a new file to rule out file corruption.

Should I use SPSS or SmartPLS for my thesis?

SPSS is suitable for many statistical analyses and regression procedures commonly used in theses. SmartPLS is more suitable when you are building a PLS-SEM model with latent variables and need to assess the measurement model and structural model. Base the decision on the model, hypotheses, and your supervisor's requirements, rather than only on which software is easier to click through.

Open your data file now, check Variable View, write down the purpose of each analysis, and match each result to the correct output table before writing the findings. If you need to run the analysis on your own .sav or .csv file, see M4 data analysis from DoThesis.