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Final

# Final Lecture notes

13 Pages
86 Views

School
University of Toronto Scarborough
Department
Psychology
Course
PSYB01H3
Professor
Anna Nagy
Semester
Fall

Description
Dec. 6 , 2010 Lecture Ch.11 Scales of measurement: A review 1. Nominal No numerical, quantitative properties Levels represent different categories or groups 2. Ordinal minimal quantitative distinctions Order the levels from lowest to highest 3. Interval quantitative properties Intervals between levels are equal in size Can be summarized using means No absolute zero 4. Ratio detailed quantitative properties Equal intervals Absolute zero Can be summarized using mean Analyzing the Results of Research InvestigationsWhat to do with data? Three basic ways of describing the results: 1. Comparing Group Percentages -used for nominal scale variables Example: Ask boys and girls whether they like school. -Like or dislike is nominal (categorical variable) -Ask 100 boys and 100 girls -Find that 60 boys and 75 girls like school -What would you report? -Perform statistical analysis to determine if difference between groups is significant. *Here, we are thinking about ways to become familiar with data, and to describe it. Comparing the number of people in each nominal group (in the form of a percent score) is one way of presenting the information, and of relating our findings to our research question. 2. Correlating Individual Scores -Obtain pairs of observations from each subject (each individual has two scores; one from each of the variables) -Ask whether variables go together in a systematic fashion by calculating Pearson r correlation coefficient (Determines strength and direction of relationship) Another way of describing data is to state how the scores are related to each other (i.e. do the variables, as operationalized by the scores obtained) vary systematically together. ***What types of data may be used? only ratio and interval data may be used www.notesolution.com Notice the variability in the data. The linear relationships in these examples are quite realistic. Individuals vary in their responses, yet we can still see a pattern of how the variables are related to each other. 3. Comparing Group Means For Experimental Designs -Compare the mean (average) response of experimental group with the mean response of control group -in experiments, remember that we are sampling from a population, and then randomly assigning to our control and experimental groups (2 groups in most simple design; may have more). When we collect data (in the form of scores) within each of the groups, the data is pooled within each group and then compared across groups to see if there are differences in the overall scores. This is done by calculating the mean score within each group. *Think about how variability within each group might affect these pooled scores. Example: Want to study the effect that stroking a dog has on resting heartrate How might we design this experiment?? 20 subjects; have 1 very cuddly dog named Kaija. assign to experimental and control group. Have them do everything the same except patting the dog for 20 minutes in the experimental group.(your independent variable is the contact condition: no contact or dog patting) Sample Data: (Resting Heart Rate) www.notesolution.com
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