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Statistics By Jim

Making statistics intuitive

Factor Analysis Guide with an Example

By Jim Frost 19 Comments

What is Factor Analysis?

Factor analysis uses the correlation structure amongst observed variables to model a smaller number of unobserved, latent variables known as factors. Researchers use this statistical method when subject-area knowledge suggests that latent factors cause observable variables to covary. Use factor analysis to identify the hidden variables.

Analysts often refer to the observed variables as indicators because they literally indicate information about the factor. Factor analysis treats these indicators as linear combinations of the factors in the analysis plus an error. The procedure assesses how much of the variance each factor explains within the indicators. The idea is that the latent factors create commonalities in some of the observed variables.

For example, socioeconomic status (SES) is a factor you can’t measure directly. However, you can assess occupation, income, and education levels. These variables all relate to socioeconomic status. People with a particular socioeconomic status tend to have similar values for the observable variables. If the factor (SES) has a strong relationship with these indicators, then it accounts for a large portion of the variance in the indicators.

The illustration below illustrates how the four hidden factors in blue drive the measurable values in the yellow indicator tags.

Researchers frequently use factor analysis in psychology, sociology, marketing, and machine learning.

Let’s dig deeper into the goals of factor analysis, critical methodology choices, and an example. This guide provides practical advice for performing factor analysis.

Analysis Goals

Factor analysis simplifies a complex dataset by taking a larger number of observed variables and reducing them to a smaller set of unobserved factors. Anytime you simplify something, you’re trading off exactness with ease of understanding. Ideally, you obtain a result where the simplification helps you better understand the underlying reality of the subject area. However, this process involves several methodological and interpretative judgment calls. Indeed, while the analysis identifies factors, it’s up to the researchers to name them! Consequently, analysts debate factor analysis results more often than other statistical analyses.

While all factor analysis aims to find latent factors, researchers use it for two primary goals. They either want to explore and discover the structure within a dataset or confirm the validity of existing hypotheses and measurement instruments.

Exploratory Factor Analysis (EFA)

Researchers use exploratory factor analysis (EFA) when they do not already have a good understanding of the factors present in a dataset. In this scenario, they use factor analysis to find the factors within a dataset containing many variables. Use this approach before forming hypotheses about the patterns in your dataset. In exploratory factor analysis, researchers are likely to use statistical output and graphs to help determine the number of factors to extract.

Exploratory factor analysis is most effective when multiple variables are related to each factor. During EFA, the researchers must decide how to conduct the analysis (e.g., number of factors, extraction method, and rotation) because there are no hypotheses or assessment instruments to guide them. Use the methodology that makes sense for your research.

For example, researchers can use EFA to create a scale, a set of questions measuring one factor. Exploratory factor analysis can find the survey items that load on certain constructs.

Confirmatory Factor Analysis (CFA)

Confirmatory factor analysis (CFA) is a more rigid process than EFA. Using this method, the researchers seek to confirm existing hypotheses developed by themselves or others. This process aims to confirm previous ideas, research, and measurement and assessment instruments. Consequently, the nature of what they want to verify will impose constraints on the analysis.

Before the factor analysis, the researchers must state their methodology including extraction method, number of factors, and type of rotation. They base these decisions on the nature of what they’re confirming. Afterwards, the researchers will determine whether the model’s goodness-of-fit and pattern of factor loadings match those predicted by the theory or assessment instruments.

In this vein, confirmatory factor analysis can help assess construct validity. The underlying constructs are the latent factors, while the items in the assessment instrument are the indicators. Similarly, it can also evaluate the validity of measurement systems. Does the tool measure the construct it claims to measure?

For example, researchers might want to confirm factors underlying the items in a personality inventory. Matching the inventory and its theories will impose methodological choices on the researchers, such as the number of factors.

We’ll get to an example factor analysis in short order, but first, let’s cover some key concepts and methodology choices you’ll need to know for the example.

Learn more about Validity and Construct Validity .

In this context, factors are broader concepts or constructs that researchers can’t measure directly. These deeper factors drive other observable variables. Consequently, researchers infer the properties of unobserved factors by measuring variables that correlate with the factor. In this manner, factor analysis lets researchers identify factors they can’t evaluate directly.

Psychologists frequently use factor analysis because many of their factors are inherently unobservable because they exist inside the human brain.

For example, depression is a condition inside the mind that researchers can’t directly observe. However, they can ask questions and make observations about different behaviors and attitudes. Depression is an invisible driver that affects many outcomes we can measure. Consequently, people with depression will tend to have more similar responses to those outcomes than those who are not depressed.

For similar reasons, factor analysis in psychology often identifies and evaluates other mental characteristics, such as intelligence, perseverance, and self-esteem. The researchers can see how a set of measurements load on these factors and others.

Method of Factor Extraction

The first methodology choice for factor analysis is the mathematical approach for extracting the factors from your dataset. The most common choices are maximum likelihood (ML), principal axis factoring (PAF), and principal components analysis (PCA).

You should use either ML or PAF most of the time.

Use ML when your data follow a normal distribution. In addition to extracting factor loadings, it also can perform hypothesis tests, construct confidence intervals, and calculate goodness-of-fit statistics .

Use PAF when your data violates multivariate normality. PAF doesn’t assume that your data follow any distribution, so you could use it when they are normally distributed. However, this method can’t provide all the statistical measures as ML.

PCA is the default method for factor analysis in some statistical software packages, but it isn’t a factor extraction method. It is a data reduction technique to find components. There are technical differences, but in a nutshell, factor analysis aims to reveal latent factors while PCA is only for data reduction. While calculating the components, PCA doesn’t assess the underlying commonalities that unobserved factors cause.

PCA gained popularity because it was a faster algorithm during a time of slower, more expensive computers. If you’re using PCA for factor analysis, do some research to be sure it’s the correct method for your study. Learn more about PCA in, Principal Component Analysis Guide and Example .

There are other methods of factor extraction, but the factor analysis literature has not strongly shown that any of them are better than maximum likelihood or principal axis factoring.

Number of Factors to Extract

You need to specify the number of factors to extract from your data except when using principal component components. The method for determining that number depends on whether you’re performing exploratory or confirmatory factor analysis.

Exploratory Factor Analysis

In EFA, researchers must specify the number of factors to retain. The maximum number of factors you can extract equals the number of variables in your dataset. However, you typically want to reduce the number of factors as much as possible while maximizing the total amount of variance the factors explain.

That’s the notion of a parsimonious model in statistics. When adding factors, there are diminishing returns. At some point, you’ll find that an additional factor doesn’t substantially increase the explained variance. That’s when adding factors needlessly complicates the model. Go with the simplest model that explains most of the variance.

Fortunately, a simple statistical tool known as a scree plot helps you manage this tradeoff.

Use your statistical software to produce a scree plot. Then look for the bend in the data where the curve flattens. The number of points before the bend is often the correct number of factors to extract.

The scree plot below relates to the factor analysis example later in this post. The graph displays the Eigenvalues by the number of factors. Eigenvalues relate to the amount of explained variance.

The scree plot shows the bend in the curve occurring at factor 6. Consequently, we need to extract five factors. Those five explain most of the variance. Additional factors do not explain much more.

Some analysts and software use Eigenvalues > 1 to retain a factor. However, simulation studies have found that this tends to extract too many factors and that the scree plot method is better. (Costello & Osborne, 2005).

Of course, as you explore your data and evaluate the results, you can use theory and subject-area knowledge to adjust the number of factors. The factors and their interpretations must fit the context of your study.

Confirmatory Factor Analysis

In CFA, researchers specify the number of factors to retain using existing theory or measurement instruments before performing the analysis. For example, if a measurement instrument purports to assess three constructs, then the factor analysis should extract three factors and see if the results match theory.

Factor Loadings

In factor analysis, the loadings describe the relationships between the factors and the observed variables. By evaluating the factor loadings, you can understand the strength of the relationship between each variable and the factor. Additionally, you can identify the observed variables corresponding to a specific factor.

Interpret loadings like correlation coefficients . Values range from -1 to +1. The sign indicates the direction of the relations (positive or negative), while the absolute value indicates the strength. Stronger relationships have factor loadings closer to -1 and +1. Weaker relationships are close to zero.

Stronger relationships in the factor analysis context indicate that the factors explain much of the variance in the observed variables.

Related post : Correlation Coefficients

Factor Rotations

In factor analysis, the initial set of loadings is only one of an infinite number of possible solutions that describe the data equally. Unfortunately, the initial answer is frequently difficult to interpret because each factor can contain middling loadings for many indicators. That makes it hard to label them. You want to say that particular variables correlate strongly with a factor while most others do not correlate at all. A sharp contrast between high and low loadings makes that easier.

Rotating the factors addresses this problem by maximizing and minimizing the entire set of factor loadings. The goal is to produce a limited number of high loadings and many low loadings for each factor.

This combination lets you identify the relatively few indicators that strongly correlate with a factor and the larger number of variables that do not correlate with it. You can more easily determine what relates to a factor and what does not. This condition is what statisticians mean by simplifying factor analysis results and making them easier to interpret.

Graphical illustration

Let me show you how factor rotations work graphically using scatterplots .

Factor analysis starts by calculating the pattern of factor loadings. However, it picks an arbitrary set of axes by which to report them. Rotating the axes while leaving the data points unaltered keeps the original model and data pattern in place while producing more interpretable results.

To make this graphable in two dimensions, we’ll use two factors represented by the X and Y axes. On the scatterplot below, the six data points represent the observed variables, and the X and Y coordinates indicate their loadings for the two factors. Ideally, the dots fall right on an axis because that shows a high loading for that factor and a zero loading for the other.

For the initial factor analysis solution on the scatterplot, the points contain a mixture of both X and Y coordinates and aren’t close to a factor’s axis. That makes the results difficult to interpret because the variables have middling loads on all the factors. Visually, they’re not clumped near axes, making it difficult to assign the variables to one.

Rotating the axes around the scatterplot increases or decreases the X and Y values while retaining the original pattern of data points. At the blue rotation on the graph below, you maximize one factor loading while minimizing the other for all data points. The result is that the loads are high on one indicator but low on the other.

On the graph, all data points cluster close to one of the two factors on the blue rotated axes, making it easy to associate the observed variables with one factor.

Types of Rotations

Throughout these rotations, you work with the same data points and factor analysis model. The model fits the data for the rotated loadings equally as well as the initial loadings, but they’re easier to interpret. You’re using a different coordinate system to gain a different perspective of the same pattern of points.

There are two fundamental types of rotation in factor analysis, oblique and orthogonal.

Oblique rotations allow correlation amongst the factors, while orthogonal rotations assume they are entirely uncorrelated.

Graphically, orthogonal rotations enforce a 90° separation between axes, as shown in the example above, where the rotated axes form right angles.

Oblique rotations are not required to have axes forming right angles, as shown below for a different dataset.

Notice how the freedom for each axis to take any orientation allows them to fit the data more closely than when enforcing the 90° constraint. Consequently, oblique rotations can produce simpler structures than orthogonal rotations in some cases. However, these results can contain correlated factors.

In practice, oblique rotations produce similar results as orthogonal rotations when the factors are uncorrelated in the real world. However, if you impose an orthogonal rotation on genuinely correlated factors, it can adversely affect the results. Despite the benefits of oblique rotations, analysts tend to use orthogonal rotations more frequently, which might be a mistake in some cases.

When choosing a rotation method in factor analysis, be sure it matches your underlying assumptions and subject-area knowledge about whether the factors are correlated.

Factor Analysis Example

Imagine that we are human resources researchers who want to understand the underlying factors for job candidates. We measured 12 variables and perform factor analysis to identify the latent factors. Download the CSV dataset: FactorAnalysis

The first step is to determine the number of factors to extract. Earlier in this post, I displayed the scree plot, which indicated we should extract five factors. If necessary, we can perform the analysis with a different number of factors later.

For the factor analysis, we’ll assume normality and use Maximum Likelihood to extract the factors. I’d prefer to use an oblique rotation, but my software only has orthogonal rotations. So, we’ll use Varimax. Let’s perform the analysis!

Interpreting the Results

In the bottom right of the output, we see that the five factors account for 81.8% of the variance. The %Var row along the bottom shows how much of the variance each explains. The five factors are roughly equal, explaining between 13.5% to 19% of the variance. Learn about Variance .

The Communality column displays the proportion of the variance the five factors explain for each variable. Values closer to 1 are better. The five factors explain the most variance for Resume (0.989) and the least for Appearance (0.643).

In the factor analysis output, the circled loadings show which variables have high loadings for each factor. As shown in the table below, we can assign labels encompassing the properties of the highly loading variables for each factor.

In summary, these five factors explain a large proportion of the variance, and we can devise reasonable labels for each. These five latent factors drive the values of the 12 variables we measured.

Hervé Abdi (2003), “Factor Rotations in Factor Analyses,” In: Lewis-Beck M., Bryman, A., Futing T. (Eds.) (2003). Encyclopedia of Social Sciences Research Methods . Thousand Oaks (CA): Sage.

Brown, Michael W., (2001) “ An Overview of Analytic Rotation in Exploratory Factor Analysis ,” Multivariate Behavioral Research , 36 (1), 111-150.

Costello, Anna B. and Osborne, Jason (2005) “ Best practices in exploratory factor analysis: four recommendations for getting the most from your analysis ,” Practical Assessment, Research, and Evaluation : Vol. 10 , Article 7.

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Reader Interactions

May 26, 2024 at 8:51 am

Good day Jim, I am running in troubles in terms of the item analysis on the 5 point Likert scale that I am trying to create. The thing is, is that my CFI is around 0.9 and TLI is around 0.8 which is good but my RMSEA and SRMR has a awful result as the RMSEA is around 0.1 and SRMR is 0.2. And it is a roadblock for me, I want to ask on how I can improve my RMSEA and SRMR? so that it would reach the cut off.

I hope that his message would reach you and thank you for taking the time and reading and responding to my troubled question.

May 15, 2024 at 11:27 am

Good day, Sir Jim. I am currently trying to create a 5-Likert scale that tries to measure National Identity Conformity in three ways: (1) Origin – (e.g., Americans are born in/from America), (2) Culture (e.g., Americans are patriotic) and (3) Belief (e.g., Americans embrace being Americans).

In the process of establishing the scale’s validity, I was told to use Exploratory Factor Analysis, and I would like to ask what methods of extraction and rotation can be best used to ensure that the inter-item validity of my scale is good. I would also like to understand how I can avoid crossloading or limit crossloading factors.

May 15, 2024 at 3:13 pm

I discuss those issues in this post. I’d recommend PAF as the method of extraction because your data being Likert scale won’t be normally distribution. Read the Method of Factor Extraction section for more information.

As for cross-loading, the method of rotation can help with that. The choice depends largely on subject-area knowledge and what works best for your data, so I can’t provide a suggested method. Read the Factor Rotations section for more information about that. For instance, if you get cross-loadings with orthogonal rotations, using an oblique rotation might help.

If factor rotation doesn’t sufficiently reduce cross-loading, you might need to rework your questions so they’re more distinct, remove problematic items, or increase your sample size (can provide more stable factor solutions and clearer patterns of loadings). In this scenario where changing rotations doesn’t help, you’ll need to determine whether the underlying issue is with your questions or having to small of a sample size.

I hope that helps!

March 6, 2024 at 10:20 pm

What does negative loadings mean? How to proceed further with these loadings?

March 6, 2024 at 10:44 pm

Loadings are like correlation coefficients and range from -1 to +1. More extreme positive and negative values indicate stronger relationships. Negative loadings indicate a negative relationship between the latent factors and observed variables. Highly negative values are as good as highly positive values. I discuss this in detail in the the Factor Loadings section of this post.

March 6, 2024 at 10:10 am

Good day Jim,

The methodology seems loaded with opportunities for errors. So often we are being asked to translate a nebulous English word into some sort of mathematical descriptor. As an example, in the section labelled ‘Interpreting the Results’, what are we to make of the words ‘likeability’ or ‘self-confidence’ ? How can we possibly evaluate those things…and to three significant decimal places ?

You Jim, understand and use statistical methods correctly. Yet, too often people who apply statistics fail to examine the language of their initial questions and end up doing poor analysis. Worse, many don’t understand the software they use.

On a more cheery note, keep up the great work. The world needs a thousand more of you.

March 6, 2024 at 5:08 pm

Thanks for the thoughtful comment. I agree with your concerns.

Ideally, all of those attributes are measured using validated measurement scales. The field of psychology is pretty good about that for terms that seem kind of squishy. For instance, they usually have thorough validation processes for personality traits, etc. However, your point is well taken, you need to be able to trust your data.

All statistical analyses depend on thorough subject-area knowledge, and that’s very true for factor analysis. You must have a solid theoretical understanding of these latent factors from extensive research before considering FA. Then FA can see if there’s evidence that they actually exist. But, I do agree with you that between the rotations and having to derive names to associate with the loadings, it can be a fairly subjective process.

Thanks so much for your kind words! I appreciate them because I do strive for accuracy.

March 2, 2024 at 8:44 pm

sir, i want to know that after successfully identifying my 3 factors with above give method now i want to regress on the data how to get single value for each factor rather than these number of values

February 28, 2024 at 7:48 am

Hello, Thanks for your effort on this post, it really helped me a lot. I want your recommendation for my case if you don’t mind.

I’m working on my research and I’ve 5 independent variables and 1 dependent variable, I want to use a factor analysis method in order to know which variable contributes the most in the dependent variable.

Also, what kind of data checks and preparations shall I make before starting the analysis.

Thanks in advance for your consideration.

February 28, 2024 at 1:46 pm

Based on the information you provided, I don’t believe factor analysis is the correct analysis for you.

Factor analysis is primarily used for understanding the structure of a set of variables and for reducing data dimensions by identifying underlying latent factors. It’s particularly useful when you have a large number of observed variables and believe that they are influenced by a smaller number of unobserved factors.

Instead, it sounds like you have the IVs and DV and want to understand the relationships between them. For that, I recommend multiple regression. Learn more in my post about When to Use Regression . After you settle on a model, there are several ways to Identify the Most Important Variables in the Model .

In terms of checking assumptions, familiarize yourself with the Ordinary Least Squares Regression Assumptions . Least squares regression is the most common and is a good place to start.

Best of luck with your analysis!

December 1, 2023 at 1:01 pm

What would be the eign value in efa

November 1, 2023 at 4:42 am

Hi Jim, this is an excellent yet succinct article on the topic. A very basic question, though: the dataset contains ordinal data. Is this ok? I’m a student in a Multivariate Statistics course, and as far as I’m aware, both PCA and common factor analysis dictate metric data. Or is it assumed that since the ordinal data has been coded into a range of 0-10, then the data is considered numeric and can be applied with PCA or CFA?

Sorry for the dumb question, and thank you.

November 1, 2023 at 8:00 pm

That’s a great question.

For the example in this post, we’re dealing with data on a 10 point scale where the differences between all points are equal. Consequently, we can treat discrete data as continuous data.

Now, to your question about ordinal data. You can use ordinal data with factor analysis however you might need to use specific methods.

For ordinal data, it’s often recommended to use polychoric correlations instead of Pearson correlations. Polychoric correlations estimate the correlation between two latent continuous variables that underlie the observed ordinal variables. This provides a more accurate correlation matrix for factor analysis of ordinal data.

I’ve also heard about categorical PCA and nonlinear Factor Analysis that use a monotonical transformation of ordinal data.

I hope that helps clarify it for you!

September 2, 2023 at 4:14 pm

Once identifying how much each variability the factors contribute, what steps could we take from here to make predictions about variables ?

September 2, 2023 at 6:53 pm

Hi Brittany,

Thanks for the great question! And thanks for you kind words in your other comment! 🙂

What you can do is calculate all the factor scores for each observation. Some software will do this for you as an option. Or, you can input values into the regression equations for the factor scores that are included in the output.

Then use these scores as the independent variables in regression analysis. From there, you can use the regression model to make predictions .

Ideally, you’d evaluate the regression model before making predictions and use cross validation to be sure that the model works for observations outside the dataset you used to fit the model.

September 2, 2023 at 4:13 pm

Wow! This was really helpful and structured very well for interpretation. Thank you!

October 6, 2022 at 10:55 am

I can imagine that Prof will have further explanations on this down the line at some point in future. I’m waiting… Thanks Prof Jim for your usual intuitive manner of explaining concepts. Funsho

September 26, 2022 at 8:08 am

Thanks for a very comprehensive guide. I learnt a lot. In PCA, we usually extract the components and use it for predictive modeling. Is this the case with Factor Analysis as well? Can we use factors as predictors?

September 26, 2022 at 8:27 pm

I have not used factors as predictors, but I think it would be possible. However, PCA’s goal is to maximize data reduction. This process is particularly valuable when you have many variables, low sample size and/or collinearity between the predictors. Factor Analysis also reduces the data but that’s not its primary goal. Consequently, my sense is that PCA is better for that predictive modeling while Factor Analysis is better for when you’re trying to understand the underlying factors (which you aren’t with PCA). But, again, I haven’t tried using factors in that way nor I have compared the results to PCA. So, take that with a grain of salt!

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Home » Factor Analysis – Steps, Methods and Examples

Factor Analysis – Steps, Methods and Examples

Table of Contents

Factor Analysis

Definition:

Factor analysis is a statistical technique that is used to identify the underlying structure of a relatively large set of variables and to explain these variables in terms of a smaller number of common underlying factors. It helps to investigate the latent relationships between observed variables.

Factor Analysis Steps

Here are the general steps involved in conducting a factor analysis:

1. Define the Research Objective:

Clearly specify the purpose of the factor analysis. Determine what you aim to achieve or understand through the analysis.

2. Data Collection:

Gather the data on the variables of interest. These variables should be measurable and related to the research objective. Ensure that you have a sufficient sample size for reliable results.

3. Assess Data Suitability:

Examine the suitability of the data for factor analysis. Check for the following aspects:

• Sample size: Ensure that you have an adequate sample size to perform factor analysis reliably.
• Missing values: Handle missing data appropriately, either by imputation or exclusion.
• Variable characteristics: Verify that the variables are continuous or at least ordinal in nature. Categorical variables may require different analysis techniques.
• Linearity: Assess whether the relationships among variables are linear.

4. Determine the Factor Analysis Technique:

There are different types of factor analysis techniques available, such as exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). Choose the appropriate technique based on your research objective and the nature of the data.

5. Perform Factor Analysis:

   a. Exploratory Factor Analysis (EFA):

• Extract factors: Use factor extraction methods (e.g., principal component analysis or common factor analysis) to identify the initial set of factors.
• Determine the number of factors: Decide on the number of factors to retain based on statistical criteria (e.g., eigenvalues, scree plot) and theoretical considerations.
• Rotate factors: Apply factor rotation techniques (e.g., varimax, oblique) to simplify the factor structure and make it more interpretable.
• Interpret factors: Analyze the factor loadings (correlations between variables and factors) to interpret the meaning of each factor.
• Determine factor reliability: Assess the internal consistency or reliability of the factors using measures like Cronbach’s alpha.
• Report results: Document the factor loadings, rotated component matrix, communalities, and any other relevant information.

   b. Confirmatory Factor Analysis (CFA):

• Formulate a theoretical model: Specify the hypothesized relationships among variables and factors based on prior knowledge or theoretical considerations.
• Define measurement model: Establish how each variable is related to the underlying factors by assigning factor loadings in the model.
• Test the model: Use statistical techniques like maximum likelihood estimation or structural equation modeling to assess the goodness-of-fit between the observed data and the hypothesized model.
• Modify the model: If the initial model does not fit the data adequately, revise the model by adding or removing paths, allowing for correlated errors, or other modifications to improve model fit.
• Report results: Present the final measurement model, parameter estimates, fit indices (e.g., chi-square, RMSEA, CFI), and any modifications made.

6. Interpret and Validate the Factors:

Once you have identified the factors, interpret them based on the factor loadings, theoretical understanding, and research objectives. Validate the factors by examining their relationships with external criteria or by conducting further analyses if necessary.

Types of Factor Analysis

Types of Factor Analysis are as follows:

Exploratory Factor Analysis (EFA)

EFA is used to explore the underlying structure of a set of observed variables without any preconceived assumptions about the number or nature of the factors. It aims to discover the number of factors and how the observed variables are related to those factors. EFA does not impose any restrictions on the factor structure and allows for cross-loadings of variables on multiple factors.

Confirmatory Factor Analysis (CFA)

CFA is used to test a pre-specified factor structure based on theoretical or conceptual assumptions. It aims to confirm whether the observed variables measure the latent factors as intended. CFA tests the fit of a hypothesized model and assesses how well the observed variables are associated with the expected factors. It is often used for validating measurement instruments or evaluating theoretical models.

Principal Component Analysis (PCA)

PCA is a dimensionality reduction technique that can be considered a form of factor analysis, although it has some differences. PCA aims to explain the maximum amount of variance in the observed variables using a smaller number of uncorrelated components. Unlike traditional factor analysis, PCA does not assume that the observed variables are caused by underlying factors but focuses solely on accounting for variance.

Common Factor Analysis

It assumes that the observed variables are influenced by common factors and unique factors (specific to each variable). It attempts to estimate the common factor structure by extracting the shared variance among the variables while also considering the unique variance of each variable.

Hierarchical Factor Analysis

Hierarchical factor analysis involves multiple levels of factors. It explores both higher-order and lower-order factors, aiming to capture the complex relationships among variables. Higher-order factors are based on the relationships among lower-order factors, which are in turn based on the relationships among observed variables.

Factor Analysis Formulas

Factor Analysis is a statistical method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors.

Here are some of the essential formulas and calculations used in factor analysis:

Correlation Matrix :

The first step in factor analysis is to create a correlation matrix, which calculates the correlation coefficients between pairs of variables.

Correlation coefficient (Pearson’s r) between variables X and Y is calculated as:

r(X,Y) = Σ[(xi – x̄)(yi – ȳ)] / [n-1] σx σy

where: xi, yi are the data points, x̄, ȳ are the means of X and Y respectively, σx, σy are the standard deviations of X and Y respectively, n is the number of data points.

Extraction of Factors :

The extraction of factors from the correlation matrix is typically done by methods such as Principal Component Analysis (PCA) or other similar methods.

The formula used in PCA to calculate the principal components (factors) involves finding the eigenvalues and eigenvectors of the correlation matrix.

Let’s denote the correlation matrix as R. If λ is an eigenvalue of R, and v is the corresponding eigenvector, they satisfy the equation: Rv = λv

Factor Loadings :

Factor loadings are the correlations between the original variables and the factors. They can be calculated as the eigenvectors normalized by the square roots of their corresponding eigenvalues.

Communality and Specific Variance :

Communality of a variable is the proportion of variance in that variable explained by the factors. It can be calculated as the sum of squared factor loadings for that variable across all factors.

The specific variance of a variable is the proportion of variance in that variable not explained by the factors, and it’s calculated as 1 – Communality.

Factor Rotation : Factor rotation, such as Varimax or Promax, is used to make the output more interpretable. It doesn’t change the underlying relationships but affects the loadings of the variables on the factors.

For example, in the Varimax rotation, the objective is to minimize the variance of the squared loadings of a factor (column) on all the variables (rows) in a factor matrix, which leads to more high and low loadings, making the factor easier to interpret.

Examples of Factor Analysis

Here are some real-time examples of factor analysis:

• Psychological Research: In a study examining personality traits, researchers may use factor analysis to identify the underlying dimensions of personality by analyzing responses to various questionnaires or surveys. Factors such as extroversion, neuroticism, and conscientiousness can be derived from the analysis.
• Market Research: In marketing, factor analysis can be used to understand consumers’ preferences and behaviors. For instance, by analyzing survey data related to product features, pricing, and brand perception, researchers can identify factors such as price sensitivity, brand loyalty, and product quality that influence consumer decision-making.
• Finance and Economics: Factor analysis is widely used in portfolio management and asset pricing models. By analyzing historical market data, factors such as market returns, interest rates, inflation rates, and other economic indicators can be identified. These factors help in understanding and predicting investment returns and risk.
• Social Sciences: Factor analysis is employed in social sciences to explore underlying constructs in complex datasets. For example, in education research, factor analysis can be used to identify dimensions such as academic achievement, socio-economic status, and parental involvement that contribute to student success.
• Health Sciences: In medical research, factor analysis can be utilized to identify underlying factors related to health conditions, symptom clusters, or treatment outcomes. For instance, in a study on mental health, factor analysis can be used to identify underlying factors contributing to depression, anxiety, and stress.
• Customer Satisfaction Surveys: Factor analysis can help businesses understand the key drivers of customer satisfaction. By analyzing survey responses related to various aspects of product or service experience, factors such as product quality, customer service, and pricing can be identified, enabling businesses to focus on areas that impact customer satisfaction the most.

Factor analysis in Research Example

Here’s an example of how factor analysis might be used in research:

Let’s say a psychologist is interested in the factors that contribute to overall wellbeing. They conduct a survey with 1000 participants, asking them to respond to 50 different questions relating to various aspects of their lives, including social relationships, physical health, mental health, job satisfaction, financial security, personal growth, and leisure activities.

Given the broad scope of these questions, the psychologist decides to use factor analysis to identify underlying factors that could explain the correlations among responses.

After conducting the factor analysis, the psychologist finds that the responses can be grouped into five factors:

• Physical Wellbeing : Includes variables related to physical health, exercise, and diet.
• Mental Wellbeing : Includes variables related to mental health, stress levels, and emotional balance.
• Social Wellbeing : Includes variables related to social relationships, community involvement, and support from friends and family.
• Professional Wellbeing : Includes variables related to job satisfaction, work-life balance, and career development.
• Financial Wellbeing : Includes variables related to financial security, savings, and income.

By reducing the 50 individual questions to five underlying factors, the psychologist can more effectively analyze the data and draw conclusions about the major aspects of life that contribute to overall wellbeing.

In this way, factor analysis helps researchers understand complex relationships among many variables by grouping them into a smaller number of factors, simplifying the data analysis process, and facilitating the identification of patterns or structures within the data.

When to Use Factor Analysis

Here are some circumstances in which you might want to use factor analysis:

• Data Reduction : If you have a large set of variables, you can use factor analysis to reduce them to a smaller set of factors. This helps in simplifying the data and making it easier to analyze.
• Identification of Underlying Structures : Factor analysis can be used to identify underlying structures in a dataset that are not immediately apparent. This can help you understand complex relationships between variables.
• Validation of Constructs : Factor analysis can be used to confirm whether a scale or measure truly reflects the construct it’s meant to measure. If all the items in a scale load highly on a single factor, that supports the construct validity of the scale.
• Generating Hypotheses : By revealing the underlying structure of your variables, factor analysis can help to generate hypotheses for future research.
• Survey Analysis : If you have a survey with many questions, factor analysis can help determine if there are underlying factors that explain response patterns.

Applications of Factor Analysis

Factor Analysis has a wide range of applications across various fields. Here are some of them:

• Psychology : It’s often used in psychology to identify the underlying factors that explain different patterns of correlations among mental abilities. For instance, factor analysis has been used to identify personality traits (like the Big Five personality traits), intelligence structures (like Spearman’s g), or to validate the constructs of different psychological tests.
• Market Research : In this field, factor analysis is used to identify the factors that influence purchasing behavior. By understanding these factors, businesses can tailor their products and marketing strategies to meet the needs of different customer groups.
• Healthcare : In healthcare, factor analysis is used in a similar way to psychology, identifying underlying factors that might influence health outcomes. For instance, it could be used to identify lifestyle or behavioral factors that influence the risk of developing certain diseases.
• Sociology : Sociologists use factor analysis to understand the structure of attitudes, beliefs, and behaviors in populations. For example, factor analysis might be used to understand the factors that contribute to social inequality.
• Finance and Economics : In finance, factor analysis is used to identify the factors that drive financial markets or economic behavior. For instance, factor analysis can help understand the factors that influence stock prices or economic growth.
• Education : In education, factor analysis is used to identify the factors that influence academic performance or attitudes towards learning. This could help in developing more effective teaching strategies.
• Survey Analysis : Factor analysis is often used in survey research to reduce the number of items or to identify the underlying structure of the data.
• Environment : In environmental studies, factor analysis can be used to identify the major sources of environmental pollution by analyzing the data on pollutants.

Advantages of Factor Analysis

Advantages of Factor Analysis are as follows:

• Data Reduction : Factor analysis can simplify a large dataset by reducing the number of variables. This helps make the data easier to manage and analyze.
• Structure Identification : It can identify underlying structures or patterns in a dataset that are not immediately apparent. This can provide insights into complex relationships between variables.
• Construct Validation : Factor analysis can be used to validate whether a scale or measure accurately reflects the construct it’s intended to measure. This is important for ensuring the reliability and validity of measurement tools.
• Hypothesis Generation : By revealing the underlying structure of your variables, factor analysis can help generate hypotheses for future research.
• Versatility : Factor analysis can be used in various fields, including psychology, market research, healthcare, sociology, finance, education, and environmental studies.

Disadvantages of Factor Analysis

Disadvantages of Factor Analysis are as follows:

• Subjectivity : The interpretation of the factors can sometimes be subjective, depending on how the data is perceived. Different researchers might interpret the factors differently, which can lead to different conclusions.
• Assumptions : Factor analysis assumes that there’s some underlying structure in the dataset and that all variables are related. If these assumptions do not hold, factor analysis might not be the best tool for your analysis.
• Large Sample Size Required : Factor analysis generally requires a large sample size to produce reliable results. This can be a limitation in studies where data collection is challenging or expensive.
• Correlation, not Causation : Factor analysis identifies correlational relationships, not causal ones. It cannot prove that changes in one variable cause changes in another.
• Complexity : The statistical concepts behind factor analysis can be difficult to understand and require expertise to implement correctly. Misuse or misunderstanding of the method can lead to incorrect conclusions.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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What is factor analysis and how does it simplify research findings.

17 min read There are many forms of data analysis used to report on and study survey data. Factor analysis is best when used to simplify complex data sets with many variables.

What is factor analysis?

Factor analysis is the practice of condensing many variables into just a few, so that your research data is easier to work with.

For example, a retail business trying to understand customer buying behaviours might consider variables such as ‘did the product meet your expectations?’, ‘how would you rate the value for money?’ and ‘did you find the product easily?’. Factor analysis can help condense these variables into a single factor, such as ‘customer purchase satisfaction’.

The theory is that there are deeper factors driving the underlying concepts in your data, and that you can uncover and work with them instead of dealing with the lower-level variables that cascade from them. Know that these deeper concepts aren’t necessarily immediately obvious – they might represent traits or tendencies that are hard to measure, such as extraversion or IQ.

Factor analysis is also sometimes called “dimension reduction”: you can reduce the “dimensions” of your data into one or more “super-variables,” also known as unobserved variables or latent variables. This process involves creating a factor model and often yields a factor matrix that organises the relationship between observed variables and the factors they’re associated with.

As with any kind of process that simplifies complexity, there is a trade-off between the accuracy of the data and how easy it is to work with. With factor analysis, the best solution is the one that yields a simplification that represents the true nature of your data, with minimum loss of precision. This often means finding a balance between achieving the variance explained by the model and using fewer factors to keep the model simple.

Factor analysis isn’t a single technique, but a family of statistical methods that can be used to identify the latent factors driving observable variables. Factor analysis is commonly used in  market research , as well as other disciplines like technology, medicine, sociology, field biology, education, psychology and many more.

What is a factor?

In the context of factor analysis, a factor is a hidden or underlying variable that we infer from a set of directly measurable variables.

Take ‘customer purchase satisfaction’ as an example again. This isn’t a variable you can directly ask a customer to rate, but it can be determined from the responses to correlated questions like ‘did the product meet your expectations?’, ‘how would you rate the value for money?’ and ‘did you find the product easily?’.

While not directly observable, factors are essential for providing a clearer, more streamlined understanding of data. They enable us to capture the essence of our data’s complexity, making it simpler and more manageable to work with, and without losing lots of information.

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Key concepts in factor analysis

These concepts are the foundational pillars that guide the application and interpretation of factor analysis.

Central to factor analysis, variance measures how much numerical values differ from the average. In factor analysis, you’re essentially trying to understand how underlying factors influence this variance among your variables. Some factors will explain more variance than others, meaning they more accurately represent the variables they consist of.

The eigenvalue expresses the amount of variance a factor explains. If a factor solution (unobserved or latent variables) has an eigenvalue of 1 or above, it indicates that a factor explains more variance than a single observed variable, which can be useful in reducing the number of variables in your analysis. Factors with eigenvalues less than 1 account for less variability than a single variable and are generally not included in the analysis.

Factor score

A factor score is a numeric representation that tells us how strongly each variable from the original data is related to a specific factor. Also called the component score, it can help determine which variables are most influenced by each factor and are most important for each underlying concept.

Factor loading

Factor loading is the correlation coefficient for the variable and factor. Like the factor score, factor loadings give an indication of how much of the variance in an observed variable can be explained by the factor. High factor loadings (close to 1 or -1) mean the factor strongly influences the variable.

When to use factor analysis

Factor analysis is a powerful tool when you want to simplify complex data, find hidden patterns, and set the stage for deeper, more focused analysis.

It’s typically used when you’re dealing with a large number of interconnected variables, and you want to understand the underlying structure or patterns within this data. It’s particularly useful when you suspect that these observed variables could be influenced by some hidden factors.

For example, consider a business that has collected extensive  customer feedback through surveys . The survey covers a wide range of questions about product quality, pricing, customer service and more. This huge volume of data can be overwhelming, and this is where factor analysis comes in. It can help condense these numerous variables into a few meaningful factors, such as ‘product satisfaction’, ‘customer service experience’ and ‘value for money’.

Factor analysis doesn’t operate in isolation – it’s often used as a stepping stone for further analysis. For example, once you’ve identified key factors through factor analysis, you might then proceed to a  cluster analysis  – a method that groups your customers based on their responses to these factors. The result is a clearer understanding of different customer segments, which can then guide targeted marketing and product development strategies.

By combining factor analysis with other methodologies, you can not only make sense of your data but also gain valuable insights to drive your business decisions.

Factor analysis assumptions

Factor analysis relies on several assumptions for accurate results. Violating these assumptions may lead to factors that are hard to interpret or misleading.

Linear relationships between variables

This ensures that changes in the values of your variables are consistent.

Sufficient variables for each factor

Because if only a few variables represent a factor, it might not be identified accurately.

Adequate sample size

The larger the ratio of cases (respondents, for instance) to variables, the more reliable the analysis.

No perfect multicollinearity and singularity

No variable is a perfect linear combination of other variables, and no variable is a duplicate of another.

Relevance of the variables

There should be some correlation between variables to make a factor analysis feasible.

Types of factor analysis

There are two main factor analysis methods: exploratory and confirmatory. Here’s how they are used to add value to your research process.

Confirmatory factor analysis

In this type of analysis, the researcher starts out with a hypothesis about their data that they are looking to prove or disprove. Factor analysis will confirm – or not – where the latent variables are and how much variance they account for.

Principal component analysis (PCA) is a popular form of confirmatory factor analysis. Using this method, the researcher will run the analysis to obtain multiple possible solutions that split their data among a number of factors. Items that load onto a single particular factor are more strongly related to one another and can be grouped together by the researcher using their conceptual knowledge or pre-existing research.

Using PCA will generate a range of solutions with different numbers of factors, from simplified 1-factor solutions to higher levels of complexity. However, the fewer number of factors employed, the less variance will be accounted for in the solution.

Exploratory factor analysis

As the name suggests, exploratory factor analysis is undertaken without a hypothesis in mind. It’s an investigatory process that helps researchers understand whether associations exist between the initial variables, and if so, where they lie and how they are grouped.

How to perform factor analysis: A step-by-step guide

Performing a factor analysis involves a series of steps, often facilitated by statistical software packages like SPSS, Stata and the  R programming language . Here’s a simplified overview of the process.

Prepare your data

Start with a dataset where each row represents a case (for example, a survey respondent), and each column is a variable you’re interested in. Ensure your data meets the assumptions necessary for factor analysis.

Create an initial hypothesis

If you have a theory about the underlying factors and their relationships with your variables, make a note of this. This hypothesis can guide your analysis, but keep in mind that the beauty of factor analysis is its ability to uncover unexpected relationships.

Choose the type of factor analysis

The most common type is exploratory factor analysis, which is used when you’re not sure what to expect. If you have a specific hypothesis about the factors, you might use confirmatory factor analysis.

Form your correlation matrix

After you’ve chosen the type of factor analysis, you’ll need to create the correlation matrix of your variables. This matrix, which shows the correlation coefficients between each pair of variables, forms the basis for the extraction of factors. This is a key step in building your factor analysis model.

Decide on the extraction method

Principal component analysis is the most commonly used extraction method. If you believe your factors are correlated, you might opt for principal axis factoring, a type of factor analysis that identifies factors based on shared variance.

Determine the number of factors

Various criteria can be used here, such as Kaiser’s criterion (eigenvalues greater than 1), the scree plot method or parallel analysis. The choice depends on your data and your goals.

Interpret and validate your results

Each factor will be associated with a set of your original variables, so label each factor based on how you interpret these associations. These labels should represent the underlying concept that ties the associated variables together.

Validation can be done through a variety of methods, like splitting your data in half and checking if both halves produce the same factors.

How factor analysis can help you

As well as giving you fewer variables to navigate, factor analysis can help you understand grouping and clustering in your input variables, since they’ll be grouped according to the latent variables.

Say you ask several questions all designed to explore different, but closely related, aspects of customer satisfaction:

• How satisfied are you with our product?
• Would you recommend our product to a friend or family member?
• How likely are you to purchase our product in the future?

But you only want one variable to represent a customer satisfaction score. One option would be to average the three question responses. Another option would be to create a factor dependent variable. This can be done by running a principal component analysis (PCA) and keeping the first principal component (also known as a factor). The advantage of a PCA over an average is that it automatically weights each of the variables in the calculation.

Say you have a list of questions and you don’t know exactly which responses will move together and which will move differently; for example, purchase barriers of potential customers. The following are possible barriers to purchase:

• Price is prohibitive
• Overall implementation costs
• We can’t reach a consensus in our organisation
• Product is not consistent with our business strategy
• I need to develop an ROI, but cannot or have not
• We are locked into a contract with another product
• The product benefits don’t outweigh the cost
• We have no reason to switch
• Our IT department cannot support your product
• We do not have sufficient technical resources
• Your product does not have a feature we require
• Other (please specify)

Factor analysis can uncover the trends of how these questions will move together. The following are loadings for 3 factors for each of the variables.

Notice how each of the principal components have high weights for a subset of the variables. Weight is used interchangeably with loading, and high weight indicates the variables that are most influential for each principal component. +0.30 is generally considered to be a heavy weight.

The first component displays heavy weights for variables related to cost, the second weights variables related to IT, and the third weights variables related to organisational factors. We can give our new super variables clever names.

If we were to cluster the customers based on these three components, we can see some trends. Customers tend to be high in cost barriers or organisational barriers, but not both.

The red dots represent respondents who indicated they had higher organisational barriers; the green dots represent respondents who indicated they had higher cost barriers

Considerations when using factor analysis

Factor analysis is a tool, and like any tool its effectiveness depends on how you use it. When employing factor analysis, it’s essential to keep a few key considerations in mind.

Oversimplification

While factor analysis is great for simplifying complex data sets, there’s a risk of oversimplification when grouping variables into factors. To avoid this you should ensure the reduced factors still accurately represent the complexities of your variables.

Subjectivity

Interpreting the factors can sometimes be subjective, and requires a good understanding of the variables and the context. Be mindful that multiple analysts may come up with different names for the same factor.

Supplementary techniques

Factor analysis is often just the first step. Consider how it fits into your broader research strategy and which other techniques you’ll use alongside it.

Examples of factor analysis studies

Factor analysis, including PCA, is often used in tandem with  segmentation studies . It might be an intermediary step to reduce variables before using KMeans to make the segments.

Factor analysis provides simplicity after reducing variables. For long studies with large blocks of  Matrix Likert scale  questions, the number of variables can become unwieldy. Simplifying the data using factor analysis helps analysts focus and clarify the results, while also reducing the number of dimensions they’re clustering on.

Sample questions for factor analysis

Choosing exactly which questions to perform factor analysis on is both an art and a science. Choosing which variables to reduce takes some experimentation, patience and creativity. Factor analysis works well on Likert scale questions and Sum to 100 question types.

Factor analysis works well on matrix blocks of the following question genres:

Psychographics (Agree/Disagree):

• I value family
• I believe brand represents value

Behavioural (Agree/Disagree):

• I purchase the cheapest option
• I am a bargain shopper

Attitudinal (Agree/Disagree):

• The economy is not improving
• I am pleased with the product

Activity-Based (Agree/Disagree):

• I love sports
• I sometimes shop online during work hours

Behavioural and psychographic questions are especially suited for factor analysis.

Sample output reports

Factor analysis simply produces weights (called loadings) for each respondent. These loadings can be used like other responses in the survey.

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Oil Market Report - July 2024

About this report

The IEA Oil Market Report (OMR) is one of the world's most authoritative and timely sources of data, forecasts and analysis on the global oil market – including detailed statistics and commentary on oil supply, demand, inventories, prices and refining activity, as well as oil trade for IEA and selected non-IEA countries.

• World oil demand continues to decelerate, with 2Q24 growth easing to 710 kb/d year-on-year – the slowest quarterly increase since 4Q22. Chinese consumption contracted, as the country's post-pandemic rebound has run its course. Global gains are forecast to average just below 1 mb/d in 2024 and 2025, as subpar economic growth, greater efficiencies and vehicle electrification act as headwinds.
• Global supply rose 150 kb/d to 102.9 mb/d in June as field maintenance eased and biofuels rose, offsetting a significant drop in Saudi flows. Solid monthly gains pushed 2Q24 output 910 kb/d higher q-o-q. Growth of 770 kb/d is seen for 3Q24 with non-OPEC+ providing 600 kb/d of the gains. Annual increases of 770 kb/d are forecast in 2024 with gains of 1.8 mb/d next year.
• Global refinery throughputs are forecast to rise by 950 kb/d to 83.4 mb/d in 2024, and by 630 kb/d to 84 mb/d next year. Weak demand and poor margins pressured Chinese and European crude processing in May. Margins declined in June in the Atlantic Basin and are close to multi-year lows. In Asia, they rebounded modestly in June, as run cuts eased regional crude market tensions.
• Crude oil prices recovered from six-month lows in June, with Brent futures rising by $5/bbl to $86/bbl. Falling crude stocks, investor short covering and renewed Middle East geopolitical tensions contributed to the price strength, with fund positions recovering from historically low levels.
• Global observed oil inventories rose for a fourth consecutive month in May, by 23.9 mb. Offshore inventories drew by 17.3 mb while on land stocks built by 41.3 mb to a 30-month high. OECD industry stocks rose by 27.8 mb to 2 845 mb but remained 69 mb below their five-year average. Preliminary data show global oil stocks falling by 18.1 mb in June, dominated by crude while products built.

Summer heat

Benchmark crude oil prices bounced back from six-month lows over the course of June after OPEC+ officials stated that unwinding voluntary production cuts would depend on market conditions – and as geopolitical risks remained high. ICE Brent futures rose by $5/bbl to $86/bbl by end-month.

Oil prices increased in June despite mounting concerns over the health of the Chinese economy and slowing oil demand growth. Global observed inventories were up in May for the fourth month in a row, reaching their highest level since August 2021. Offshore inventories moved ashore at a brisk pace, with oil on water down sharply, while on land stocks rose to a 30-month high ahead of the seasonal uptick in refinery activity. OECD industry stocks built for a second consecutive month after having declined for the previous six months. Preliminary data suggest global oil stocks fell 18.1 mb in June, led by a 1 mb/d draw in crude.

World oil demand growth slowed to only 710 kb/d in 2Q24, its lowest quarterly increase in over a year. Oil consumption in China, long the engine of global oil demand growth, contracted in both April and May, and is now assessed marginally below year earlier levels in 2Q24. That stands in stark contrast to annual gains of 1.5 mb/d in 2023 and 740 kb/d in 1Q24. Demand for industrial fuels and petrochemical feedstocks was particularly weak. By contrast, second-quarter delivery data of gasoil and naphtha for OECD economies came in higher than expected, potentially signalling a budding recovery in Europe’s ailing manufacturing sector. While the bounce temporarily pushed quarterly OECD demand growth back into positive territory, non-OECD countries will account for all this year’s global gains. World oil demand growth expectations for the 2024 and 2025 are largely unchanged at 970 kb/d and 980 kb/d, respectively.

At the same time, global oil supply trended higher, with 2Q24 production up 910 kb/d from 1Q24, led by the United States. Output is forecast to rise by another 770 kb/d in 3Q24 with non-OPEC+ providing 600 kb/d of the gains. For 2024 as a whole, global oil supply growth is forecast to average 770 kb/d, which will boost oil supply to a record 103 mb/d. Non-OPEC+ output is expected to rise by 1.5 mb/d, while OPEC+ production will fall by 740 kb/d year-on-year if existing voluntary cuts are maintained. Global supply growth in 2025 is projected at a much stronger 1.8 mb/d, with non-OPEC+, mainly in the United States, Canada, Guyana and Brazil, leading gains for a third consecutive year, adding 1.5 mb/d.

In early June, OPEC+ laid out a roadmap for unwinding extra voluntary supply reductions of up to 2.2 mb/d from 4Q24 through 3Q25. Given the bloc’s assurances that the production increase can be paused or reversed subject to market conditions, we will adjust our OPEC+ supply numbers when such a decision is confirmed. The OPEC+ Joint Ministerial Monitoring Committee is meanwhile due to meet on 1 August to review global oil market conditions and production levels. Our current non-OPEC+ supply and global demand forecasts show the call on OPEC+ crude at 42.2 mb/d in 3Q24 and 41.8 mb/d in 4Q24 – roughly 800 kb/d and 400 kb/d above its June output, respectively. For next year, the call on OPEC+ crude tumbles to 41.1 mb/d as demand growth continues to slow and non-OPEC+ output continues to expand. After the hot summer, cooler trends are set to prevail.

OPEC+ crude oil production 1 million barrels per day

1. Includes extra voluntary curbs where announced. 2. Capacity levels can be reached within 90 days and sustained for an extended period. 3. Excludes shut in Iranian, Russian crude. 4. Angola left OPEC effective 1 Jan 2024. 5. Iran, Libya, Venezuela exempt from cuts. 6. Mexico excluded from OPEC+ compliance. 7. Bahrain, Brunei, Malaysia, Sudan and South Sudan.

Oil Market Report Documentation

Definitions of key terms used in the OMR.

For more info on the methodology, download the PDF below.

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• Download the methodology PDF circle-arrow

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IEA (2024), Oil Market Report - July 2024 , IEA, Paris https://www.iea.org/reports/oil-market-report-july-2024

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