Chapter 12 Correlational designs

Correlational designs (designs with two continuous variables, which may or may not be observational)

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## cols(
##   rating = col_double(),
##   complaints = col_double(),
##   privileges = col_double(),
##   learning = col_double(),
##   raises = col_double(),
##   critical = col_double(),
##   advance = col_double()
## )

12.1 Description

12.1.1 Descriptive table

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##   tibble::lst(mean, median)
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##   list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
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	advance	complaints	critical	learning	privileges	raises	rating
Mean	42.93	66.60	74.77	56.37	53.13	64.63	64.63
Std.Dev	10.29	13.31	9.89	11.74	12.24	10.40	12.17
Min	25.00	37.00	49.00	34.00	30.00	43.00	40.00
Median	41.00	65.00	77.50	56.50	51.50	63.50	65.50
Max	72.00	90.00	92.00	75.00	83.00	88.00	85.00
N.Valid	30.00	30.00	30.00	30.00	30.00	30.00	30.00
Pct.Valid	100.00	100.00	100.00	100.00	100.00	100.00	100.00

12.1.2 Correlation matrix (numerical)

Correlations between key variables
Kendall's τ
	rating	complaints	privileges	learning	raises	critical	advance
rating		0.655	0.356	0.449	0.444	0.031	0.132
complaints	0.655		0.386	0.451	0.488	0.078	0.172
privileges	0.356	0.386		0.355	0.335	0.087	0.273
learning	0.449	0.451	0.355		0.511	0.085	0.391
raises	0.444	0.488	0.335	0.511		0.201	0.395
critical	0.031	0.078	0.087	0.085	0.201		0.181
advance	0.132	0.172	0.273	0.391	0.395	0.181

12.2 Visualization

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Figure 12.1: Scatterplots and Pearson correlations between key variables.

Figure 12.2: Pearson correlations between key variables.