Quantitative Data Analysis 2 (QDA2) super condensed summary of Andy Field's "Discovering Statistics Using IBM SPSS Statistics" (2017) and the lectures given by Roger Pruppers. The summary is 14 pages and includes the most important material covered in the course 6012B0423Y at UvA.
Week 7 – Principal Components Analysis & Reliability Analysis 12
, Quantitative Data Analysis 2
Week 1 – Conceptual Models & Analysis of Variance
P-value
• Probability of obtaining a result (or test-statistic value) equal to or “more extreme”
than what was actually observed, assuming that the null hypothesis is true.
• Looking for a small p-value so that the null hypothesis is unlikely
Moderator = variable moderates (changes) the relationship between two other variables
(One-Way Independent) ANOVA to test whether statistically significant differences exist in scores on a
quantitative OV between different levels (groups) of a categorical PV.
Number of Categories
2 2 or more
Different respondents
(independent samples) Independent Samples T-Test (One-Way Independent) ANOVA
(between subjects)
Independent Samples T-Test (One-Way Independent) ANOVA
• One OV quantitative • One OV quantitative
• One PV categorical • One PV categorical
o 2 categories o 2 or more categories
o Participants = different o Participants = different
ANOVA Assumptions Levene’s Test
• Homogeneity of group variances • Homogeneity of variances?
• Residuals are normally distributed • H0: assumed variances are equal
• Groups are roughly equally sized (equal • Therefore, we want a large p-value so we
cell sizes) do not reject H0
o p-value > α = 10%
Unequal Variances Robust tests of equality of means: Brown-Forsythe and/or Welch
F-Test: F-ratio = (explained variability / unexplained variability) = (between groups / within groups)
• H0 : μ1 = μ2 = … = μi
o No differences in OV mean across the different categories of PV
o No significant difference between group means
• H1: at least one μ differs
o There is at least one difference in OV mean score between PV categories
• (We want a big F-Ratio more explained variability)
ANOVA decomposes total variability observed in OV
• Variability caused by differences between groups explained variation; model sum of squares
• Variability caused by differences within groups unexplained variation; residual sum of squares
R2 : Proportion of total variance in our data that is “explained” by our model
• R2 = explained variability / total variability
• Percentage of variance in OV explained by PV(s)
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