Grouping Error Statistics
Contents |
(1) that the characteristics of an individual group member are reflective of the group as a whole, or (2) that a group's decision outcome must reflect the preferences of individual group
Group Attribution Error Definition
members, even when information is available suggesting otherwise. The fundamental attribution error is group attribution error example similar in that it refers to the tendency to believe that an individual's actions are representative of the individual's group serving bias preferences, even when available information suggests that the actions were caused by outside forces. Contents 1 Type I 2 Type II 3 Limitation 4 See also 5 References 6 Further reading Type I[edit]
Ultimate Attribution Error
To demonstrate the first form of group attribution error, research participants are typically given case studies about individuals who are members of defined groups (such as members of a particular occupation, nationality, or ethnicity), and then take surveys to determine their views of the groups as a whole. Often the participants may be broken up into separate test groups, some of which are given statistics about
Affective Component
the group that directly contradict what they were presented in the case study. Others may even be told directly that the individual in the case study was atypical for the group as a whole. Researchers use the surveys to determine to what extent the participants allowed their views of the individual in the case study to influence their views of the group as a whole and also take note of how effective the statistics were in deterring this group attribution error. Ruth Hamill, Richard E. Nisbett, and Timothy DeCamp Wilson were the first to study this form of group attribution error in detail in their 1980 paper Insensitivity to Sample Bias: Generalizing From Atypical Cases. In their study, the researchers provided participants with a case study about an individual welfare recipient. Half of the participants were given statistics showing that the individual was typical for a welfare recipient and had been on the program for the typical amount of time, while the other half of participants were given statistics showing that the welfare recipient had been on the program much longer than normal. The results of the study revealed that participants did indeed draw extremel
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Correspondence Bias
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you may want to analyze your data based on categories or a grouping variable. One way that you could do this is to http://www.ats.ucla.edu/stat/spss/faq/catfaq.htm split the data file into different data files and conduct the same analyses on the two (or more) data sets. However, that is cumbersome and error prone. Several commands in SPSS will allow you to do separate analyses by category, and we will consider them below. Let's use the example data set below. You will notice that one of the independent variables, iv1, is a attribution error string variable. We will use this variable as our grouping variable to demonstrate how to use a string variable as the grouping variable. All of the techniques that will be shown can be used with a numeric categorical variable as well. data list list / sub * iv1 (A) iv2 * dv1 dv2. begin data 1 "1" 1 48 25 2 "1" 1 49 37 3 group attribution error "1" 1 50 55 4 "2" 1 17 19 5 "2" 1 20 38 6 "2" 2 23 48 7 "2" 2 28 44 8 "3" 2 28 68 9 "3" 2 30 30 10 "3" 2 32 37 end data. To begin with, suppose we wanted to find the mean and standard deviation for dv1 for groups one, two and three in iv1. We can use the means command to obtain simple descriptive statistics. means tables= dv1 by iv1. Case Processing Summary Cases Included Excluded Total N Percent N Percent N Percent DV1 * IV1 10 100.0% 0 .0% 10 100.0% Report DV1 IV1 Mean N Std. Deviation 1 49.0000 3 1.00000 2 22.0000 4 4.69042 3 30.0000 3 2.00000 Total 32.5000 10 12.25878 You could also use the examine command, as shown below. We will use the plot = none subcommand to suppress the stem-and-leaf and boxplots. examine dv1 by iv1 /plot = none. Case Processing Summary Cases Valid Missing Total N Percent N Percent N Percent DV1 10 100.0% 0 .0% 10 100.0% Descriptives Statistic Std. Error DV1 Mean 32.5000 3.87657 95% Confidence Interval for Mean Lower Bound 23.7306 Upper Bou