Variance:<\/strong><\/p>\n

\n
• Represents the amount of variability in the data<\/li>\n
• Three main methods of representing variability o Range (smallest and largest data points) o Standard deviation o Variance<\/li>\n<\/ul>\n

  Correlation:<\/strong><\/p>\n

  \n
  • Standardised representation of the associations between two variables<\/li>\n
  • Can range from -1.0 to 1.0<\/li>\n
  • Coefficient of determination is the correlation squared<\/li>\n<\/ul>\n

    Composite variables:<\/strong><\/p>\n

    \n
    • Test scores tend to be based on the sum of two or more items<\/li>\n
    • g. the Beck Depression inventory consists of 21 items:<\/li>\n<\/ul>\n

      o Items are scored on an ordinal scale from 0-3 o Thus the range of scores possible is 0-63<\/p>\n

      \n
      • Such \u2018sum scores\u2019 are known as \u2018composite scores\u2019.<\/li>\n<\/ul>\n

        Composite variables and variance:<\/strong><\/p>\n

        \n
        • The variance of a composite score is a function of:<\/li>\n<\/ul>\n

          o The variance associated with the individual items o The correlation amongst the items<\/p>\n

          \n
          • A positive correlation between two variables increases the variance associated with the composite derived from those two variables.<\/li>\n<\/ul>\n

            o i.e. as correlation increases (positive), the magnitude of the corresponding composite score variance also increases.<\/p>\n

            Binary items- Variance:<\/strong><\/p>\n

            \n
            • Most frequently encountered in achievement type tests, e.g. dichotomous items such as exam or intelligent test items<\/li>\n
            • All other things equal, having more variance is typically better than having less variance with respect to some important psychometric characteristics<\/li>\n
            • Calculating the variance of a composite based on dichotomous items works the same way as demonstrated for ordinal\/continuous items<\/li>\n<\/ul>\n

              Interpreting test scores:<\/strong><\/p>\n

              Most scores produced by psychological tests are not clearly interpretable in their own right<\/p>\n

              \n
              • Two of the most common psychological test score interpretations are (1) relative and (2) abstract<\/li>\n
              • Relative interpretations are based on the analysis of data<\/li>\n
              • Abstract interpretations are based on theoretically relevant characteristics of the body of research which supports the test scores as valid indicators of a psychological construct<\/li>\n<\/ul>\n

                Relative interpretations:<\/strong><\/p>\n

                \n
                • From the relative perspective, to interpret an individual\u2019s score we need to:<\/li>\n<\/ul>\n

                  o Make reference to an entire distribution of scores on the test and o Identify where the individual falls in the distribution<\/p>\n

                  \n
                  • In practical terms this involves knowing the mean distribution of scores and the standard deviation of the distribution of scores<\/li>\n<\/ul>\n

                    Raw scores- limitations:<\/strong><\/p>\n

                    \n
                    • Although knowledge of the mean and standard deviation associated with a group of scores allows us to interpret a particular score in a relative way, it is not a precise method unless you are a human calculator.<\/li>\n
                    • Instead raw scores can be converted into standardised scores which incorporate information about the mean and standard deviation- z-scores.<\/li>\n<\/ul>\n

                      Z-scores:<\/strong><\/p>\n

                      \n
                      • Have a mean of zero and a SD of one<\/li>\n
                      • To convert raw scores into z-scores: z = X – \u03bc \/ \u03c3<\/li>\n
                      • By framing the meaning of a score in terms of \u2018distance from the mean\u2019, the z score frees us from worrying about the units of the original test score<\/li>\n
                      • Can be used to compare scores across tests that are on different sized units<\/li>\n<\/ul>\n

                        Converted standard scores:<\/strong><\/p>\n

                        \n
                        • Ideally don\u2019t want to use negative numbers (in z scores), so you can easily convert them into alternated standard scores<\/li>\n
                        • Accomplished by rescaling the scores so that the converted scores have a different mean and standard deviation- T scores.<\/li>\n
                        • With t-scores the mean is always 50 and the standard deviation is always 10<\/li>\n<\/ul>\n

                          T-scores:<\/strong><\/p>\n

                          \n
                          • Convert raw scores to z scores<\/li>\n<\/ul>\n

                            Convert z scores using the formula:\u00a0 T=z(10) +50<\/p>\n","protected":false},"excerpt":{"rendered":"

                            Variance: Represents the amount of variability in the data Three main methods of representing variability o Range (smallest and largest data points) o Standard deviation o Variance Correlation: Standardised… Continue Reading Differences in correlations<\/span><\/a><\/p>\n","protected":false},"author":4,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[107],"tags":[],"_links":{"self":[{"href":"https:\/\/www.amyork.ca\/academic\/zz\/wp-json\/wp\/v2\/posts\/4271"}],"collection":[{"href":"https:\/\/www.amyork.ca\/academic\/zz\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.amyork.ca\/academic\/zz\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.amyork.ca\/academic\/zz\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/www.amyork.ca\/academic\/zz\/wp-json\/wp\/v2\/comments?post=4271"}],"version-history":[{"count":1,"href":"https:\/\/www.amyork.ca\/academic\/zz\/wp-json\/wp\/v2\/posts\/4271\/revisions"}],"predecessor-version":[{"id":4872,"href":"https:\/\/www.amyork.ca\/academic\/zz\/wp-json\/wp\/v2\/posts\/4271\/revisions\/4872"}],"wp:attachment":[{"href":"https:\/\/www.amyork.ca\/academic\/zz\/wp-json\/wp\/v2\/media?parent=4271"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.amyork.ca\/academic\/zz\/wp-json\/wp\/v2\/categories?post=4271"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.amyork.ca\/academic\/zz\/wp-json\/wp\/v2\/tags?post=4271"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}