How to Run an Exploratory Factor Analysis in jamovi? (2026 Guide)

Exploratory factor analysis in jamovi lets you uncover hidden patterns in your data without writing a single line of code. Whether you are developing a psychometric scale, validating a survey, or trying to reduce a large set of variables into meaningful clusters, jamovi makes the process surprisingly approachable. I have used jamovi for factor analysis across multiple research projects, and the interface strikes a rare balance between simplicity and statistical rigor.

In this guide, I will walk you through the entire EFA workflow from start to finish. You will learn what exploratory factor analysis actually does, how it differs from PCA, which assumption checks to run, and every step needed to run and interpret EFA results in jamovi.

Here is a quick overview of the steps we will cover: (1) check your assumptions with KMO and Bartlett’s test, (2) open the Factor menu and select Exploratory Factor Analysis, (3) move your variables into the analysis box, (4) choose your extraction method and number of factors, (5) select a rotation method, and (6) interpret factor loadings, eigenvalues, and your scree plot. By the end of this article, you will be able to run, interpret, and write up an EFA with confidence.

What Is Exploratory Factor Analysis (EFA)?

Exploratory factor analysis is a statistical technique for identifying underlying latent factors from a set of observed variables. When you have a questionnaire with 30 items, EFA can tell you whether those items cluster into two, three, or four distinct constructs. The technique works by examining the correlations between your observed variables and grouping together variables that share common variance.

The core idea is that observed variables are influenced by unobserved latent variables. For example, if you have survey items about job satisfaction, some might load onto a “work environment” factor while others cluster around a “compensation” factor. EFA reveals these hidden structures mathematically rather than through guesswork.

Each variable in your analysis receives a factor loading for every factor extracted. A factor loading represents the correlation between that variable and the underlying latent factor. Loadings closer to 1 or -1 indicate a strong relationship, while loadings near 0 suggest the factor does not explain that variable well.

Researchers in psychology, education, marketing, and social sciences rely heavily on EFA. Common use cases include psychometric scale development, questionnaire validation, identifying underlying constructs, and data reduction before running further analyses. If you have ever wondered whether your survey actually measures what you think it measures, EFA is one of the tools that can answer that question.

It is worth noting that EFA is exploratory by nature. You do not specify a hypothesis about the factor structure beforehand. Instead, you let the data reveal the structure. This is what separates it from confirmatory factor analysis (CFA), where you test a pre-specified model against your data.

EFA vs PCA: Key Differences in jamovi

People often confuse exploratory factor analysis with principal component analysis, and jamovi presents both under the same Factor menu. While they look similar on the surface, they answer fundamentally different questions.

PCA is a data reduction technique. It compresses your variables into components that capture the maximum amount of variance. It does not distinguish between shared variance and unique variance. Every bit of variance, including error variance, gets folded into the components.

EFA, on the other hand, is a latent variable model. It separates shared variance (the part of a variable that correlates with other variables) from unique variance and error variance. EFA assumes that each observed variable is influenced by one or more latent factors plus measurement error. This makes EFA more appropriate when your goal is understanding underlying constructs rather than simply reducing dimensions.

Here are the key practical differences:

• Purpose: PCA reduces data; EFA identifies latent constructs.
• Variance modeled: PCA uses all variance; EFA uses only shared variance.
• Error term: PCA has no error term; EFA explicitly separates error variance from common factor variance.
• Assumptions: EFA assumes latent factors cause observed correlations; PCA makes no such causal assumption.
• Default in jamovi: Both are under Factor, but you select “Exploratory Factor Analysis” for EFA and “Principal Component Analysis” for PCA.

My rule of thumb is straightforward. If you want to understand the underlying structure of your variables and you are working with survey or questionnaire data, use EFA. If you just need to reduce 20 variables into 3 or 4 composite scores for downstream analysis and you do not care about latent constructs, PCA will do the job faster.

One pain point that comes up frequently on forums like the jamovi community board and Reddit’s r/statistics is that eigenvalues and results can differ between jamovi and SPSS or SAS. This happens because jamovi defaults to minimum residuals as its extraction method, while SPSS defaults to principal axis factoring or PCA. The methods are different algorithms, so differences are expected. I recommend being consistent within a study and noting your extraction method in your write-up.

Checking EFA Assumptions in jamovi

Before you run the actual factor analysis, you need to verify that your data is suitable for factor analysis. Two diagnostic tests are essential: the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and Bartlett’s test of sphericity. Jamovi makes both available right inside the EFA dialog.

Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy

The KMO test tells you whether your variables share enough common variance for factor analysis to be meaningful. It ranges from 0 to 1, and there are accepted benchmarks for interpretation:

• Above 0.90: Marvelous. Your data is excellent for factor analysis.
• 0.80 to 0.90: Meritorious. Good to proceed.
• 0.70 to 0.80: Middling. Acceptable but not ideal.
• 0.60 to 0.70: Mediocre. Factor analysis may work, but results should be interpreted cautiously.
• 0.50 to 0.60: Miserable. Consider revising your variable set.
• Below 0.50: Unacceptable. Factor analysis is not appropriate for this data.

Most published research reports a KMO of at least 0.70, and ideally 0.80 or above. If your KMO comes back low, you may need to remove variables that have low individual KMO values or collect more data.

Bartlett’s Test of Sphericity

Bartlett’s test checks whether your correlation matrix is significantly different from an identity matrix. In plain terms, it tests whether your variables correlate with each other at all. If they do not correlate, factor analysis cannot find meaningful factors.

You want Bartlett’s test to be significant, meaning a p-value below 0.05. A non-significant result suggests your variables are essentially uncorrelated and factor analysis is not appropriate. In my experience, Bartlett’s test is almost always significant with reasonably sized datasets, so pay closer attention to KMO.

Sample Size Considerations

Factor analysis is sensitive to sample size. A common rule of thumb is at least 10 participants per variable, though some methodologists recommend 20:1 for more stable solutions. At minimum, you should have 100 cases, and ideally 200 or more for most analyses.

Another consideration is the ratio of variables to factors. You generally want at least three to four variables loading cleanly on each factor. If you have 12 variables, extracting 4 factors means only 3 variables per factor, which is the bare minimum for a stable solution.

In jamovi, you can enable both KMO and Bartlett’s test by checking the corresponding boxes under “Assumption Checks” in the EFA panel. The results appear immediately in your output window, so you can decide whether to proceed before committing to a full analysis.

How to Run an Exploratory Factor Analysis in jamovi

Running exploratory factor analysis in jamovi is a straightforward process once you understand the interface. I will walk you through each step using a practical approach that works for most datasets. You can follow along with your own data in jamovi as you read.

Step 1: Load and Prepare Your Data

Open your dataset in jamovi by clicking the file menu and selecting your data file. Jamovi accepts CSV, Excel, and SPSS files. Make sure your variables are set to the correct data type. For factor analysis, your items should be coded as continuous variables. If jamovi has labeled them as ordinal or nominal, change the variable type in the data view by clicking the variable icon and selecting “Continuous.”

Clean your data before proceeding. Remove or address missing values, check for outliers, and make sure you have enough complete cases. Jamovi will use listwise deletion by default for EFA, meaning any case with missing data on any included variable gets dropped entirely.

Step 2: Navigate to the Factor Analysis Menu

Click the “Analyses” button at the top of the jamovi window, then select “Factor” from the ribbon. You will see two options: “Principal Component Analysis” and “Exploratory Factor Analysis.” Click “Exploratory Factor Analysis” to open the EFA panel.

The panel appears on the right side of the screen, and your results will populate below as you make selections. Everything updates live, which is one of the features I appreciate most about working in jamovi.

Step 3: Move Variables into the Analysis

Select all the variables you want to include in your factor analysis and move them into the “Variables” box on the right side of the panel. You can select multiple variables at once by holding Shift or Ctrl (Cmd on Mac) and clicking. Only include items that you believe might cluster together. Exclude demographic variables, ID numbers, and any other non-substantive variables.

Step 4: Run Assumption Checks

Under the “Assumption Checks” dropdown in the panel, check both “KMO” and “Bartlett’s test of sphericity.” The results appear in your output window immediately. Verify that KMO is at least 0.60, preferably 0.70 or above, and that Bartlett’s test is significant (p less than 0.05) before proceeding.

Step 5: Choose Your Extraction Method

Under “Method,” you will see several extraction options. Jamovi defaults to minimum residuals, which is a solid choice for most applications. Other options include maximum likelihood and principal axis factoring. Maximum likelihood is useful if you want to compute model fit statistics and confidence intervals. Principal axis factoring is the traditional approach many researchers learned in graduate school.

I generally stick with the default minimum residuals unless I have a specific reason to switch. It tends to produce reliable solutions without distributional assumptions.

Step 6: Determine the Number of Factors

Under “Number of Factors,” you have several options. You can let jamovi decide based on the Kaiser criterion (eigenvalues greater than 1), or you can specify a fixed number yourself. I strongly recommend using parallel analysis, which you can enable under the same section.

Parallel analysis compares your actual eigenvalues to those generated from random data with the same number of variables and cases. You keep factors whose eigenvalues exceed the random data eigenvalues. This method is more accurate than the Kaiser criterion and is the approach most methodologists now recommend.

Check the “Scree plot” option as well. The scree plot visualizes eigenvalues in descending order and helps you identify the “elbow” point where adding more factors provides diminishing returns. You can use both parallel analysis and visual inspection of the scree plot to confirm your factor count decision.

Step 7: Select a Rotation Method

Rotation makes your factor solution easier to interpret by maximizing high loadings and minimizing low ones on each factor. Under “Rotation,” you will choose between orthogonal methods (which assume factors are uncorrelated) and oblique methods (which allow factors to correlate).

For most social science and psychology research, I recommend an oblique rotation like oblimin or promax. Real-world constructs usually correlate with each other, and forcing them to be independent (as orthogonal rotations do) can produce misleading results. If you are confident your factors should be uncorrelated, varimax is the most common orthogonal choice.

Forum discussions on jamovi.org and Reddit consistently favor oblimin for EFA in applied research. Users report that oblique rotations more accurately reflect the correlated nature of psychological constructs.

Step 8: Configure Additional Output Options

Under “Additional Output,” select the options that will help you interpret and report your results:

• Factor loadings: Essential. Shows how strongly each variable relates to each factor.
• Communality: Shows the proportion of each variable’s variance explained by the factors.
• Uniqueness: The complement of communality. Shows variance not explained by the factors.
• Variance explained: Shows the proportion and cumulative variance accounted for by each factor.
• Factor scores: Available if you want to save computed factor scores as new columns in your dataset.

Once you have configured everything, your results are already visible in the output panel. There is no “Run” button to press because jamovi updates in real time as you make changes.

Interpreting EFA Results in jamovi

After your analysis is configured, you need to interpret the output correctly. This is where many researchers get stuck, so I will break down each table and visual that jamovi produces.

Factor Loadings Table

The factor loadings table is the centerpiece of your output. Each row is a variable and each column is a factor. The numbers represent how strongly each variable loads on each factor. A general guideline is that loadings above 0.40 are considered meaningful, loadings above 0.60 are good, and loadings above 0.70 are excellent.

Look for variables that load strongly on one factor and weakly on all others. These are “clean” loadings. Variables that load at 0.32 or above on two or more factors are called cross-loaders. Cross-loadings complicate interpretation and may indicate that an item is ambiguous or that you need to adjust your factor structure.

If a variable has no loadings above 0.40 on any factor, consider removing it and re-running the analysis. These items are not well explained by your factor solution.

Eigenvalues and Variance Explained

Eigenvalues indicate how much variance each factor accounts for. An eigenvalue of 1 means the factor explains as much variance as a single original variable. The Kaiser criterion suggests keeping only factors with eigenvalues greater than 1, though parallel analysis is more reliable.

The variance explained table shows both the raw percentage of variance and the cumulative variance across all retained factors. A good EFA solution typically accounts for at least 50 to 60 percent of the total variance. If you are barely reaching 40 percent, you may have too many factors or items that do not share much common variance.

One common source of confusion is why eigenvalues differ between jamovi and SPSS. As forum users on r/learnrstats frequently note, this is because the software uses different extraction algorithms. SPSS defaults to PCA-based eigenvalues, while jamovi uses minimum residuals. The difference is methodological, not a bug.

Scree Plot

The scree plot displays eigenvalues in descending order. You are looking for the point where the curve flattens out, often called the “elbow.” Factors before the elbow contribute meaningful variance; factors after the elbow are essentially noise.

When reading a scree plot, imagine a line drawn through the later, flatter points. The factors above that imaginary line are worth keeping. If you enabled parallel analysis, you will see additional reference points on the plot. Factors above the random-data line should be retained.

Do not rely on the scree plot alone. Always cross-check with parallel analysis results and the Kaiser criterion. When all three methods agree, you can be confident in your factor count.

Communality and Uniqueness

Communality tells you how much of each variable’s variance is explained by your factor solution. A communality of 0.60 means 60 percent of that variable’s variance is shared with the factors. Variables with communalities below 0.30 are weakly represented and may need to be removed.

Uniqueness is simply 1 minus communality. A uniqueness of 0.40 means 40 percent of the variable’s variance is not explained by the common factors, including both specific variance and measurement error. Low communality values across many variables suggest your factor model does not fit the data well.

Rotation Choice and Pattern Matrix

If you selected an oblique rotation, jamovi will show both a pattern matrix and a structure matrix. The pattern matrix shows the unique contribution of each variable to each factor, controlling for correlations between factors. This is the matrix you should focus on when interpreting loadings.

The structure matrix shows the raw correlations between variables and factors, including the influence of factor correlations. Use it as supplementary information but rely on the pattern matrix for your main interpretation.

For orthogonal rotations, only one matrix appears because factors are by definition uncorrelated. The pattern and structure matrices are identical in that case.

Factor Correlation Matrix (Oblique Rotations Only)

If you used oblimin or promax, jamovi shows the correlations between your factors. This is valuable information. If factors correlate above 0.85, they may be measuring essentially the same construct and you might consider merging them. If correlations are near zero, an orthogonal rotation might have been appropriate after all.

Writing Up Your EFA Results

Writing up factor analysis results follows a fairly standard structure in academic research. Whether you are following APA style or another format, your write-up should include specific details that allow others to evaluate and replicate your analysis.

Start by describing your sample and variables. Report the number of participants, the number of variables analyzed, and what those variables represent. Then state your extraction method, rotation method, and how you determined the number of factors.

Report your assumption check results explicitly. Include the KMO value and Bartlett’s test statistic with degrees of freedom and significance level. For example: “The KMO measure of sampling adequacy was 0.84, and Bartlett’s test of sphericity was significant, chi-square (df = 66) = 1,245.30, p less than 0.001.”

Describe the factor solution in detail. Report the number of factors extracted, the total variance explained, and the eigenvalues for each factor. Present the factor loadings in a table, ideally sorted by magnitude within each factor. Only show loadings above your suppression threshold (commonly 0.40 or 0.32).

Name each factor based on the variables that load most strongly on it. Explain your reasoning and cite theoretical support if available. This is the interpretive heart of your write-up and where you demonstrate that your factors are meaningful, not just statistical artifacts.

Finally, acknowledge limitations. Note that EFA is exploratory and that your findings should be confirmed with CFA on an independent sample. This is standard practice and strengthens the credibility of your work.

FAQs

Conclusion

Running an exploratory factor analysis in jamovi is a manageable process when you break it into clear steps. Start with your assumption checks, choose the right extraction and rotation methods, use parallel analysis to determine factor count, and interpret your loadings carefully. Whether you are developing a new scale or validating an existing questionnaire, this workflow will help you understand how to run an exploratory factor analysis in jamovi with confidence. The next step after EFA is often a confirmatory factor analysis on a separate sample to verify your factor structure holds.