Factor Analysis of Likert Scale Data Part 2 | Demonstration in SPSS

Welcome to my video that explores factor analysis with Likert scale data Part 2. Factor analysis is a statistical technique that allows us to understand the underlying structure or dimensions in a set of variables. It helps answer the question: "What are the latent factors that influence the responses on a Likert scale?" A Likert scale is a commonly used rating scale in surveys, typically containing several statements, and participants rate their level of agreement or disagreement on a scale ranging from strongly agree to strongly disagree. While Likert scale data is insightful, it can be challenging to interpret due to the multidimensionality of responses. Factor analysis comes to the rescue! It helps us reduce the complexity of the data by identifying the unique factors that explain the relationships within the variables. By grouping similar variables together, we can uncover those hidden dimensions that contribute to the overall pattern of responses. Factor analysis can also aid in validating the construct validity of a survey instrument, ensuring that the measured variables indeed represent the intended underlying concepts. Now, let's dive into the steps involved in factor analysis. First, we collect data through a Likert scale survey. Then, we analyze the data using specialized software, which computes the factors and their loadings for each variable. Once we obtain the factor loadings, we can interpret them to understand the relationship between the variables and the factors. High loadings indicate a strong relationship, while low loadings suggest a weak or no relationship. The factors themselves represent the hidden dimensions that influence the responses. It is important to note that factor analysis is an exploratory technique, meaning that the identified factors are based on the data pattern itself. Therefore, it is crucial to interpret the results cautiously and consider the underlying theoretical framework. Factor analysis with Likert scale data offers immense potential for researchers, as it allows for a deeper understanding of the underlying dimensions influencing respondents' answers. In conclusion, factor analysis is a powerful tool for unravelling hidden dimensions in Likert scale data. By identifying the underlying factors, we can gain valuable insights into the relationships between variables and the latent structure of responses in surveys. Thank you for watching this video! If you found it informative, make sure to hit the subscribe button and stay tuned for more content on data analysis and statistical techniques.
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[@maniganesan3892]

Good presentation

[@Dr.Okolie]

Thank you very much.

[@farheenfareed5946]

i want to need some practice data set can you send some practice data set.

[@LuxCarAdda]

Hello Sir My Kmo is just above 0.5 is it fine or should I make some changes in the data.

[@LuxCarAdda]

Can you provide me an email id I have some doubts or need some more clarity.