Assessment, Statistics, and Research

  Factor analysis (FA)  is a statistical method of reducing a large set of data to a smaller set by identifying patterns in the data that have common characteristics. Factor analysis is sometimes called data reduction or dimension reduction. The original numerical values in the data set are observed variables (also called manifest variables)  such as the items in a large survey or test. Factor analysis may find patterns characterized by a shared statistical relationship representing a factor, which is also called a dimension . A researcher examines the content of the items linked to this factor and chooses a factor label such as verbal skills for related items on an intelligence test. The factors may be treated as variables in additional research. These are secondary variables. Because they are created from the observed variables, they are considered latent variables. For example, if 5 items on a personality test are associated with one factor labeled "agreeableness" then agr

  What makes a test valid? is a tricky question.  The short, and rather obnoxious response is, “nothing.”  Like reliability , validity is a property of test scores  rather than tests but more accurately, an interpretation of the scores. But it is important to take the question seriously when test-takers and users are wondering how much confidence to place in a test score. As with many aspects of science, the answers can be simply stated but there is a complicated backstory. Validity Traditions For many, the traditional views of test score validity will be sufficient. Tests measure constructs. Scientific constructs are ideas that have features that can be measured like reading comprehension, dominance, short-term memory, and verbal intelligence. Construct validity is not a single entity but rather the current state of knowledge about how a test instrument’s scores have functioned in many settings and in relation to criteria. Construct vali