multigroup coding and analysis.doc - Multiple-Group Data Coding Analysis Most of the data analyses behavioral scientists analyze are 1 non-experimental

multigroup coding and analysis.doc - Multiple-Group Data...

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Multiple-Group Data Coding & Analysis Most of the data analyses behavioral scientists analyze are: 1) non-experimental 2) imbedded within much larger data sets, and 3) require some sort of “data preparation” before we can analyze it. That’s what we are going to practice now – data coding and analysis with large non-experimental data sets. BG Analysis – Walk-Through Does the number of siblings relate to how much you like people? The interpersonal dataset includes the following variable. 16 -- Liking People Scale (LPS) -- single scale score This 15-item instrument measures one aspect of interpersonal orientation, the general liking of other people. Interpersonal orientation plays a significant role in one’s social development and adjustment. The theoretical point of departure of the LPS is that the degree of liking of people influences whether one approaches or avoids social interaction. The instrument has utility, then, for monitoring intervention in cases of social isolation, shyness, and antisocial behavior. The researcher wanted to know if people from 2-parent families with different numbers of siblings differed as to how much they like people. Specifically, the hypothesis was that those with siblings will like people more than those without siblings. To do this we must select the correct participants from the sample, construct any necessary variables, and then perform the proper statistical analysis. Step 1: Following the handout, select only those respondents from 2-parent families (family = 2) from the dataset for analysis Step 2: Make a new variable called numsibs starting with “siblings” and recode it so that: No siblings is coded 1 1 sibling is coded 2 2 or more siblings is coded 3 Step 3: Perform a BG ANOVA to see if there are mean Liking People differences for the different sibling groups Omnibus BG ANOVA No siblings 1 sibling 2+ siblings Mean 55.08 55.54 53.86 Std 14.3 11.25 12.41 F = . 83 df = 2 , 385 MS error = 147.95 p = .44 N = 388 k = 3 n = 129.33 LSD, Pairwise Comparisons, Effect Sizes & Statistical Decision Errors Do we need to perform LSD pairwise comparisons to test the RH? Why or why not? No because there was no significant difference detected by the Omnibus F. With no significant omnibus F, we do not have to follow up Do the LSD analysis anyway LSDmmd = 2.98 (if the mean diff is greater than the LSDmmd, then it is significant between that pair) Pairwise comparison No vs. 1 sibling No vs. 2+ sibling 1 vs. 2+ siblings Mean diference .46 1.23 1.68 LSD result If < we use = because there is no difference = = = Type of Stat Error risked Type II Type II Type II Pairwise effect size .02 .05 .07 Power Problem? No because the effect size is so No because we had a nonsignificant p No because we had a non-
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small that it is not a significant power problem and small effect size to care about a power problem significant p and small effect size to care about a power problem RH: Testing RH1: Those with no siblings will have lower average LPS score than those who have 1 sibling Is this RH: fully, partially or not supported? Explain your answer.
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