Alternatively, some use listwise deletion, also known as case-wise deletion, which only uses observations with no missing data. This involves computing correlation using all the non-missing data for the two variables. However, people more commonly use pairwise missing values (sometimes known as partial correlations). A best practice is usually to use multiple imputation. Various strategies exist for dealing with missing values when computing correlation matrixes. This can either be because we did not collect this data or don’t know the responses. The data that we use to compute correlations often contain missing values. Changes in codings tend to have little effect, except when extreme. However, other codings are possible, such as -4, -1, 0, 1, 4. For example, if respondents were given choices of Strongly Disagree, Somewhat Disagree, Neither Agree nor Disagree, Somewhat Agree, and Strongly Agree, you could assign codes of 1, 2, 3, 4, and 5, respectively (or, mathematically equivalent from the perspective of correlation, scores of -2, -1, 0, 1, and 2). If you also have data from a survey, you'll need to decide how to code the data before computing the correlations. Both of these are non-parametric correlations and less susceptible to outliers than r. It is also common to use Spearman’s Correlation and Kendall’s Tau-b. Most correlation matrixes use Pearson’s Product-Moment Correlation (r). For example, with linear regression, a high amount of correlations suggests that the linear regression estimates will be unreliable. As a diagnostic when checking other analyses.For example, people commonly use correlation matrixes as inputs for exploratory factor analysis, confirmatory factor analysis, structural equation models, and linear regression when excluding missing values pairwise. In our example above, the observable pattern is that all the variables highly correlate with each other. To summarize a large amount of data where the goal is to see patterns.There are three broad reasons for computing a correlation matrix: This matrix is symmetrical, with the same correlation is shown above the main diagonal being a mirror image of those below the main diagonal. The line of 1.00s going from the top left to the bottom right is the main diagonal, which shows that each variable always perfectly correlates with itself. This shows correlations between the stated importance of various things to people. Typically, a correlation matrix is “square”, with the same variables shown in the rows and columns. Key decisions to be made when creating a correlation matrix include: choice of correlation statistic, coding of the variables, treatment of missing data, and presentation.
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