Assessment, Statistics, and Research

  Scale name : Enneagram [Riso-Hudson Enneagram Type Indicator [ RHETI ® ] Scale overview: There is more than one version of the Enneagram, which purports to measure how an individual’s personality fits with nine types. The version referred to in this post is the  RHETI ® —see above for the full name. A study by Newgent et al. (2004) used the 144-item forced choice format. Authors: Don Richard Riso and Russ Hudson Response Type: Forced-choice format Subscales: There are nine types referred to by number and a label: 1 Reformer- principled, idealistic 2 Helper- caring, interpersonal 3 Achiever- adaptable, success-oriented 4. Individualist- romantic, introspective 5 Investigator- intense, cerebral 6 Loyalist- committed, security-oriented 7 Enthusiast- busy, productive 8 Challenger- powerful, dominating 9 Peacemaker- easy-going, self-effacing More detailed descriptions can be found at The Enneagram Institute Sample item: (Newgent, et al., 2004, p. 228)

Variance is a measure of the dispersion of values in a distribution of values.  In psychology and behavioral science statistics, the variance is typically a reference to the extent to which numerical values vary around the arithmetic mean of a data set. Theoretically, the values vary around a population mean but in most cases, researchers work with samples. In statistics, write sigma squared for the population variance σ 2 Write final form sigma squared for the sample variance  ς2 In reports, write VAR for variance. How it works If we have a set of different numerical values such as scores on a test we can calculate a mean, which is the average of all the scores divided by the number of scores. The difference of one score from the mean is a deviation score. X is a score and the Greek letter mu μ is the symbol for the population mean. In a sample, which is what we normally have in psychology, we subtract a score X from the sample mean M . Thus, X - M = the deviation score.  If a perso

  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