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PSY 121
Chapter 12 and Chapter 13
Term | Definition |
---|---|
effect size | general term that refers to the strength of association between variables; a type of correlation (pearson r is one indicator) general guide: 0.15 = small effects, 0.30 = medium effects, 0.40 or above = large effects |
standard deviation (SD) | average deviation of scores from the mean. SD = small; scores are close to mean; SD = large; scores are far from the mean. Used with interval and ratio scales |
68% rule | fall within the +/- 1 deviation units from the mean |
bar graph | use a separate bar for each piece of information. Used with nominal or ordinal data. When drawing bar graph, the bars should not touch each other |
polygon (line graph) | uses a line to represent frequencies, useful with interval and ratio scale variables |
pie chart | divide a whole circle into sections that represent relative percentages. Useful with nominal scale data |
scatterplot | used to visualize the relationship between the variables (correlation coefficient of +/- 1.00 - positive and negative) |
null hypothesis (Ho) | population means equal, the observed difference is due to random error. Logic: to be able to reject the null hypothesis --> good thing. Ho = mean of the treatment group = the mean of control group |
research hypothesis (H1 or Ha) | aka alternate hypothesis. Population means are not equal. The IV had an effect on the DV. Logic: to fail to reject (to accept) the research hypothesis. H1 = mean of treatment group will not equal the mean of the control group |
t-value in regions of rejection (ROR) | reject Ho, significant result |
t-value NOT in ROR | fail to reject Ho, not significant |
t test | used for single experiment (one IV and two levels - 2 groups). Used to examine whether 2 groups are significantly different from each other |
F test | ANOVA. More general and common than the t-test. Used to determine if there is a significant difference between 3 or more groups OR to evaluate the results of factorial designs |