Factor Analysis in Jamovi

1. Go to “Factor” → “Principal Component Analysis”.
2. Move your items (variables) into the analysis box.
3. Check Scree plot.
4. Under Rotation, select Varimax.
5. Number of Components->Based on eigenvalue
6. HIDE LOADINGS BELOW->1
7. In the Loadings table, look for items with loadings above .5 on a factor — these are your strong items.

Describe the results of the exploratory factor analysis:

1. How many factors were extracted
2. List the factors in a table
3. Can you subjectively make sense of how the items loaded on each factor (what does each factor mean?)
4. After examining the data, how many factors you would retain in your final solution?  Explain your reasoning for including or excluding each factor.
   

   Struggling with where to start this assignment? Follow this guide to tackle your assignment easily!

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   Step 1: Set Up Your Factor Analysis in Jamovi

   Before you begin the analysis, ensure that you have all the necessary variables (items) ready for your factor analysis.

   Navigate to Factor Analysis:

   • Open Jamovi and load your dataset.

   • Go to the “Factor” menu at the top of the window.

   • Select “Principal Component Analysis” from the dropdown menu.

   Select Your Variables:

   • In the analysis box, move all the items (variables) you want to analyze.

   Step 2: Configure Your Analysis Options

   Make sure you configure the settings correctly to get the most meaningful results.

   1. Check Scree Plot:

      • This is a graphical representation of the eigenvalues of the factors. The scree plot will help you determine how many factors to retain.

   2. Rotation Settings:

      • Under Rotation, select Varimax. This is an orthogonal rotation method that helps to maximize the variance of the squared loadings of a factor across variables, making the interpretation easier.

   3. Number of Components:

      • Choose “Based on eigenvalue”. This ensures that factors with an eigenvalue greater than 1 are retained, which is a common rule of thumb for factor extraction.

   4. Hide Loadings Below 1:

      • Check the “HIDE LOADINGS BELOW 1” option. This will ensure that only loadings greater than 1 are displayed, making it easier to interpret the factors.

   Step 3: Analyze the Loadings

   After running the analysis, you’ll see the Loadings Table, which shows how each item (variable) correlates with the extracted factors.

   Look for Strong Items:

   • In the Loadings Table, focus on items with loadings above .5. These are considered “strong” items on a factor, meaning they have a strong relationship with that factor.

   Examine the Scree Plot:

   • The Scree Plot will help you determine how many factors to retain. Look for an “elbow” in the plot, where the eigenvalues start to level off. This indicates the number of factors that explain the most variance in your data.

   Step 4: Interpret the Results of the Factor Analysis

   Number of Factors Extracted:

   • Based on the scree plot and the eigenvalue rule (eigenvalue > 1), determine how many factors were extracted. Typically, the number of factors retained corresponds to the number of components that explain significant variance in your data.

   List the Factors:

   Create a table to list the factors. For example:

   Factor	Items
   Factor 1	Item 1, Item 3, Item 5
   Factor 2	Item 2, Item 4

   Subjective Interpretation of Each Factor:

   Look at the items that loaded highly on each factor and try to make sense of what they represent. Consider the following:

   • Factor 1 might include items related to employee motivation, if the items are about things like job satisfaction, personal growth, and rewards.

   • Factor 2 might relate to communication or organizational culture if the items include aspects like team collaboration, communication styles, and leadership support.

   Retaining Factors:

   • After examining the data, you need to decide how many factors to retain in your final solution. Factors with strong loadings (e.g., above 0.5) are usually retained because they explain significant variance in your data.

   • Explain your reasoning for including or excluding each factor. For example, you may decide to exclude a factor if it doesn’t explain a significant portion of the variance or if it doesn’t make theoretical sense in the context of your research.

   Step 5: Summarize Your Findings

   Write a clear summary of your analysis, including:

   • The number of factors that were extracted.

   • A table listing the factors and the items that loaded onto each factor.

   • An interpretation of the factors, making sure you describe what each factor represents based on the items that loaded onto it.

   • Your final decision on how many factors to retain, explaining why certain factors were excluded based on their eigenvalues, scree plot, and theoretical relevance.