Now that we’ve discussed important psychometrics concepts and…

Question Answered step-by-step Now that we’ve discussed important psychometrics concepts and… Now that we’ve discussed important psychometrics concepts and skills, such as reliability, standard error of measurement and exploratory factor analysis, we’re going to move on to more advanced psychometric methods, including confirmatory factor analysis and item response theory analysis. CFA is widely used in I/O fields and differs from EFA in a few ways.First of all, CFA assumes that you already know a hypothetical model structure for your data set based on previous research and theory. For example, let’s say you’re about to use an organization to climb a skill that has previously been validated. You want to make sure that the variables in your sample load onto the factors the same way they did in the original research.In other words, you have very clear expectations about what you will find in your own sample. You know the number of factors that you will encounter and which variables will load onto the factors. In contrast, as we will let you learn, your faith is not assume the prior knowledge or hypothesis about the underlying structure of data.And thus, explores all possible latent variable patterns to find the factors structure. Unlike EFA, we can use the CFA to evaluate psychometric validity and reliability, basic dimensionality and internal structure validity and conversion or discriminate validity evidence. We can also use it to detect any bias the items that were tests.Another big difference between EFA and CFA is that a CFA model is a far more detailed. We don’t have enough time to go in-depth on every CFA model. So we will focus on introductory model in this application. Item response theory is an advanced technique to analyze responses to tests or questionnaires with the goal of improving measurement accuracy and reliability.We can use IRT analysis to get information about items’ characteristics and person’s latent traits. IRT analysis have four major uses. The first, developing, evaluating, improving and scoring assessments. Second, identifying biased items or tests. Third, developing computerized adaptive testing. And number four, linking any creating parameter coefficients. There are many different RIT models out there.Some have a multidimensional traits while some have a unidimensional trait. Some used dichotomous responses while others used polytomous responses. We’ll again, focus on the simplest models and their applications. These models assume unidimensional traits, dichotomous responses and nonlinear item response curves. In addition to getting a conceptual introduction to CFA and IRT analysis, we’ll also learn how to conduct these analysis that using oral.We wont use SPSS program this week, because its ability to deal with the CFA and IRT analysis is pretty limited. [BLANK_AUDIO] Here’s a scenario. In the modern business world, more and more US companies are considering expanding their market overseas. This rapid globalization of organizations poses new challenges for applied psychologist.One particular challenge is in understanding how culture expectations influence employees responses for cognitive ability, personality and job attitude assessments. Multinational companies and their talent management programs need to ensure that instruments that they use are equivalent across the different cultures. With this in mind, imagine your staff segmentation for a multinational organization that has offices in the US and Brazil.Historically, the US office has developed and validated cognitive ability test in English for personal selection. The cognitive ability test is done translated into Portuguese and useful job selections as if the margin of properties are invariant. However, the recent question about the fairness of general mental ability tests has resulted in cross-national comparison of item and test level properties.Let’s assume this cognitive ability test was designed to evaluate four specific abilities, verbal, quantitative, analytic and spatial ability using distinct 10-item soft scales that correlates 0.2 to 0.5. You collect data for 400 employees from each country. In this scenario, how do you know if the test is biased or not? Questions: In this situation, How would you evaluate your test measures for different cognitive ability constructs? By conducting CFA analysis given situation, what other information would you obtain? How would you test whether respondents from different cultures interpret a given measure in a conceptually similar manner? If the measurement equivalence does not hold, what would be problems in the hiring decision?  Math Statistics and Probability PSYC 602 Share QuestionEmailCopy link Comments (0)