apa.ezANOVA.table: Creates an ANOVA table in APA style based output of ezANOVA...
In apaTables: Create American Psychological Association (APA) Style Tables
Description
Usage
Arguments
Value
Examples
Description
Creates an ANOVA table in APA style based output of ezANOVA command from ez package
Usage
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apa.ezANOVA.table(
ez.output,
correction = "GG",
table.title = "",
filename,
table.number = NA
)
Arguments
ez.output
Output object from ezANOVA command from ez package
correction
Type of sphercity correction: "none", "GG", or "HF" corresponding to none, Greenhouse-Geisser and Huynh-Feldt, respectively.
table.title
String containing text for table title
filename
(optional) Output filename document filename (must end in .rtf or .doc only)
table.number
Integer to use in table number output line
Value
APA table object
Examples
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## Not run:
# ** Example 1: Between Participant Predictors
#
library(apaTables)
library(ez)
# See format where one row represents one PERSON
# Note that participant, gender, and alcohol are factors
print(goggles)
# Use ezANOVA
# Be sure use the options command, as below, to ensure sufficient digits
options(digits = 10)
goggles_results <- ezANOVA(data = goggles,
dv = attractiveness,
between = .(gender, alcohol),
participant ,
detailed = TRUE)
# Make APA table
goggles_table <- apa.ezANOVA.table(goggles_results,
filename="ex1_ez_independent.doc")
print(goggles_table)
#
# ** Example 2: Within Participant Predictors
#
library(apaTables)
library(tidyr)
library(forcats)
library(ez)
# See initial wide format where one row represents one PERSON
print(drink_attitude_wide)
# Convert data from wide format to long format where one row represents one OBSERVATION.
# Wide format column names MUST represent levels of each variable separated by an underscore.
# See vignette for further details.
drink_attitude_long <- gather(data = drink_attitude_wide,
key = cell, value = attitude,
beer_positive:water_neutral,
factor_key=TRUE)
drink_attitude_long <- separate(data = drink_attitude_long,
col = cell, into = c("drink","imagery"),
sep = "_", remove = TRUE)
drink_attitude_long$drink <- as_factor(drink_attitude_long$drink)
drink_attitude_long$imagery <- as_factor(drink_attitude_long$imagery)
# See new long format of data, where one row is one OBSERVATION.
# As well, notice that we have two columns (drink, imagery)
# drink, imagery, and participant are factors
print(drink_attitude_long)
# Set contrasts to match Field et al. (2012) textbook output
alcohol_vs_water <- c(1, 1, -2)
beer_vs_wine <- c(-1, 1, 0)
negative_vs_other <- c(1, -2, 1)
positive_vs_neutral <- c(-1, 0, 1)
contrasts(drink_attitude_long$drink) <- cbind(alcohol_vs_water, beer_vs_wine)
contrasts(drink_attitude_long$imagery) <- cbind(negative_vs_other, positive_vs_neutral)
# Use ezANOVA
# Be sure use the options command, as below, to ensure sufficient digits
options(digits = 10)
drink_attitude_results <- ezANOVA(data = drink_attitude_long,
dv = .(attitude), wid = .(participant),
within = .(drink, imagery),
type = 3, detailed = TRUE)
# Make APA table
drink_table <- apa.ezANOVA.table(drink_attitude_results,
filename="ex2_repeated_table.doc")
print(drink_table)
#
# ** Example 3: Between and Within Participant Predictors
#
library(apaTables)
library(tidyr)
library(forcats)
library(ez)
# See initial wide format where one row represents one PERSON
print(dating_wide)
# Convert data from wide format to long format where one row represents one OBSERVATION.
# Wide format column names MUST represent levels of each variable separated by an underscore.
# See vignette for further details.
dating_long <- gather(data = dating_wide,
key = cell, value = date_rating,
attractive_high:ugly_none,
factor_key = TRUE)
dating_long <- separate(data = dating_long,
col = cell, into = c("looks","personality"),
sep = "_", remove = TRUE)
dating_long$looks <- as_factor(dating_long$looks)
dating_long$personality <- as_factor(dating_long$personality)
# See new long format of data, where one row is one OBSERVATION.
# As well, notice that we have two columns (looks, personality)
# looks, personality, and participant are factors
print(dating_long)
# Set contrasts to match Field et al. (2012) textbook output
some_vs_none <- c(1, 1, -2)
hi_vs_av <- c(1, -1, 0)
attractive_vs_ugly <- c(1, 1, -2)
attractive_vs_average <- c(1, -1, 0)
contrasts(dating_long$personality) <- cbind(some_vs_none, hi_vs_av)
contrasts(dating_long$looks) <- cbind(attractive_vs_ugly, attractive_vs_average)
# Use ezANOVA
library(ez)
options(digits = 10)
dating_results <-ezANOVA(data = dating_long, dv = .(date_rating), wid = .(participant),
between = .(gender), within = .(looks, personality),
type = 3, detailed = TRUE)
# Make APA table
dating_table <- apa.ezANOVA.table(dating_results,
filename = "ex3_mixed_table.doc")
print(dating_table)
## End(Not run)
Example output
Registered S3 methods overwritten by 'lme4':
method from
cooks.distance.influence.merMod car
influence.merMod car
dfbeta.influence.merMod car
dfbetas.influence.merMod car
participant gender alcohol attractiveness
1 1 Female None 65
2 2 Female None 70
3 3 Female None 60
4 4 Female None 60
5 5 Female None 60
6 6 Female None 55
7 7 Female None 60
8 8 Female None 55
9 9 Female 2 Pints 70
10 10 Female 2 Pints 65
11 11 Female 2 Pints 60
12 12 Female 2 Pints 70
13 13 Female 2 Pints 65
14 14 Female 2 Pints 60
15 15 Female 2 Pints 60
16 16 Female 2 Pints 50
17 17 Female 4 Pints 55
18 18 Female 4 Pints 65
19 19 Female 4 Pints 70
20 20 Female 4 Pints 55
21 21 Female 4 Pints 55
22 22 Female 4 Pints 60
23 23 Female 4 Pints 50
24 24 Female 4 Pints 50
25 25 Male None 50
26 26 Male None 55
27 27 Male None 80
28 28 Male None 65
29 29 Male None 70
30 30 Male None 75
31 31 Male None 75
32 32 Male None 65
33 33 Male 2 Pints 45
34 34 Male 2 Pints 60
35 35 Male 2 Pints 85
36 36 Male 2 Pints 65
37 37 Male 2 Pints 70
38 38 Male 2 Pints 70
39 39 Male 2 Pints 80
40 40 Male 2 Pints 60
41 41 Male 4 Pints 30
42 42 Male 4 Pints 30
43 43 Male 4 Pints 30
44 44 Male 4 Pints 55
45 45 Male 4 Pints 35
46 46 Male 4 Pints 20
47 47 Male 4 Pints 45
48 48 Male 4 Pints 40
Coefficient covariances computed by hccm()
ANOVA results
Predictor df_num df_den SS_num SS_den F p ges
gender 1 42 168.75 3487.50 2.03 .161 .05
alcohol 2 42 3332.29 3487.50 20.07 .000 .49
gender x alcohol 2 42 1978.12 3487.50 11.91 .000 .36
Note. df_num indicates degrees of freedom numerator. df_den indicates degrees of freedom denominator.
SS_num indicates sum of squares numerator. SS_den indicates sum of squares denominator.
ges indicates generalized eta-squared.
# A tibble: 20 x 10
participant beer_positive beer_negative beer_neutral wine_positive
<fct> <int> <int> <int> <int>
1 P1 1 6 5 38
2 P2 43 30 8 20
3 P3 15 15 12 20
4 P4 40 30 19 28
5 P5 8 12 8 11
6 P6 17 17 15 17
7 P7 30 21 21 15
8 P8 34 23 28 27
9 P9 34 20 26 24
10 P10 26 27 27 23
11 P11 1 -19 -10 28
12 P12 7 -18 6 26
13 P13 22 -8 4 34
14 P14 30 -6 3 32
15 P15 40 -6 0 24
16 P16 15 -9 4 29
17 P17 20 -17 9 30
18 P18 9 -12 -5 24
19 P19 14 -11 7 34
20 P20 15 -6 13 23
# … with 5 more variables: wine_negative <int>, wine_neutral <int>,
# water_positive <int>, water_negative <int>, water_neutral <int>
# A tibble: 180 x 4
participant drink imagery attitude
<fct> <fct> <fct> <int>
1 P1 beer positive 1
2 P2 beer positive 43
3 P3 beer positive 15
4 P4 beer positive 40
5 P5 beer positive 8
6 P6 beer positive 17
7 P7 beer positive 30
8 P8 beer positive 34
9 P9 beer positive 34
10 P10 beer positive 26
# … with 170 more rows
ANOVA results
Predictor df_num df_den Epsilon SS_num SS_den F p ges
(Intercept) 1.00 19.00 11218.01 1920.11 111.01 .000 .41
drink 1.15 21.93 0.58 2092.34 7785.88 5.11 .030 .12
imagery 1.49 28.40 0.75 21628.68 3352.88 122.56 .000 .58
drink x imagery 3.19 60.68 0.80 2624.42 2906.69 17.15 .000 .14
Note. df_num indicates degrees of freedom numerator. df_den indicates degrees of freedom denominator.
Epsilon indicates Greenhouse-Geisser multiplier for degrees of freedom,
p-values and degrees of freedom in the table incorporate this correction.
SS_num indicates sum of squares numerator. SS_den indicates sum of squares denominator.
ges indicates generalized eta-squared.
# A tibble: 20 x 11
participant gender attractive_high average_high ugly_high attractive_some
<fct> <fct> <int> <int> <int> <int>
1 P01 Male 86 84 67 88
2 P02 Male 91 83 53 83
3 P03 Male 89 88 48 99
4 P04 Male 89 69 58 86
5 P05 Male 80 81 57 88
6 P06 Male 80 84 51 96
7 P07 Male 89 85 61 87
8 P08 Male 100 94 56 86
9 P09 Male 90 74 54 92
10 P10 Male 89 86 63 80
11 P11 Female 89 91 93 88
12 P12 Female 84 90 85 95
13 P13 Female 99 100 89 80
14 P14 Female 86 89 83 86
15 P15 Female 89 87 80 83
16 P16 Female 80 81 79 86
17 P17 Female 82 92 85 81
18 P18 Female 97 69 87 95
19 P19 Female 95 92 90 98
20 P20 Female 95 93 96 79
# … with 5 more variables: average_some <int>, ugly_some <int>,
# attractive_none <int>, average_none <int>, ugly_none <int>
# A tibble: 180 x 5
participant gender looks personality date_rating
<fct> <fct> <fct> <fct> <int>
1 P01 Male attractive high 86
2 P02 Male attractive high 91
3 P03 Male attractive high 89
4 P04 Male attractive high 89
5 P05 Male attractive high 80
6 P06 Male attractive high 80
7 P07 Male attractive high 89
8 P08 Male attractive high 100
9 P09 Male attractive high 90
10 P10 Male attractive high 89
# … with 170 more rows
ANOVA results
Predictor df_num df_den Epsilon SS_num SS_den F
(Intercept) 1.00 18.00 846249.80 760.22 20036.90
gender 1.00 18.00 0.20 760.22 0.00
looks 1.92 34.62 0.96 20779.63 882.71 423.73
personality 1.87 33.62 0.93 23233.60 1274.04 328.25
gender x looks 1.92 34.62 0.96 3944.10 882.71 80.43
gender x personality 1.87 33.62 0.93 4420.13 1274.04 62.45
looks x personality 3.20 57.55 0.80 4055.27 1992.62 36.63
gender x looks x personality 3.20 57.55 0.80 2669.67 1992.62 24.12
p ges
.000 .99
.946 .00
.000 .81
.000 .83
.000 .45
.000 .47
.000 .45
.000 .35
Note. df_num indicates degrees of freedom numerator. df_den indicates degrees of freedom denominator.
Epsilon indicates Greenhouse-Geisser multiplier for degrees of freedom,
p-values and degrees of freedom in the table incorporate this correction.
SS_num indicates sum of squares numerator. SS_den indicates sum of squares denominator.
ges indicates generalized eta-squared.
apaTables documentation built on Jan. 13, 2021, 11:22 p.m.
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Description Usage Arguments Value Examples
Description
Creates an ANOVA table in APA style based output of ezANOVA command from ez package
Usage
1 2 3 4 5 6 7 | apa.ezANOVA.table(
ez.output,
correction = "GG",
table.title = "",
filename,
table.number = NA
)
|
Arguments
ez.output |
Output object from ezANOVA command from ez package |
correction |
Type of sphercity correction: "none", "GG", or "HF" corresponding to none, Greenhouse-Geisser and Huynh-Feldt, respectively. |
table.title |
String containing text for table title |
filename |
(optional) Output filename document filename (must end in .rtf or .doc only) |
table.number |
Integer to use in table number output line |
Value
APA table object
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 | ## Not run:
# ** Example 1: Between Participant Predictors
#
library(apaTables)
library(ez)
# See format where one row represents one PERSON
# Note that participant, gender, and alcohol are factors
print(goggles)
# Use ezANOVA
# Be sure use the options command, as below, to ensure sufficient digits
options(digits = 10)
goggles_results <- ezANOVA(data = goggles,
dv = attractiveness,
between = .(gender, alcohol),
participant ,
detailed = TRUE)
# Make APA table
goggles_table <- apa.ezANOVA.table(goggles_results,
filename="ex1_ez_independent.doc")
print(goggles_table)
#
# ** Example 2: Within Participant Predictors
#
library(apaTables)
library(tidyr)
library(forcats)
library(ez)
# See initial wide format where one row represents one PERSON
print(drink_attitude_wide)
# Convert data from wide format to long format where one row represents one OBSERVATION.
# Wide format column names MUST represent levels of each variable separated by an underscore.
# See vignette for further details.
drink_attitude_long <- gather(data = drink_attitude_wide,
key = cell, value = attitude,
beer_positive:water_neutral,
factor_key=TRUE)
drink_attitude_long <- separate(data = drink_attitude_long,
col = cell, into = c("drink","imagery"),
sep = "_", remove = TRUE)
drink_attitude_long$drink <- as_factor(drink_attitude_long$drink)
drink_attitude_long$imagery <- as_factor(drink_attitude_long$imagery)
# See new long format of data, where one row is one OBSERVATION.
# As well, notice that we have two columns (drink, imagery)
# drink, imagery, and participant are factors
print(drink_attitude_long)
# Set contrasts to match Field et al. (2012) textbook output
alcohol_vs_water <- c(1, 1, -2)
beer_vs_wine <- c(-1, 1, 0)
negative_vs_other <- c(1, -2, 1)
positive_vs_neutral <- c(-1, 0, 1)
contrasts(drink_attitude_long$drink) <- cbind(alcohol_vs_water, beer_vs_wine)
contrasts(drink_attitude_long$imagery) <- cbind(negative_vs_other, positive_vs_neutral)
# Use ezANOVA
# Be sure use the options command, as below, to ensure sufficient digits
options(digits = 10)
drink_attitude_results <- ezANOVA(data = drink_attitude_long,
dv = .(attitude), wid = .(participant),
within = .(drink, imagery),
type = 3, detailed = TRUE)
# Make APA table
drink_table <- apa.ezANOVA.table(drink_attitude_results,
filename="ex2_repeated_table.doc")
print(drink_table)
#
# ** Example 3: Between and Within Participant Predictors
#
library(apaTables)
library(tidyr)
library(forcats)
library(ez)
# See initial wide format where one row represents one PERSON
print(dating_wide)
# Convert data from wide format to long format where one row represents one OBSERVATION.
# Wide format column names MUST represent levels of each variable separated by an underscore.
# See vignette for further details.
dating_long <- gather(data = dating_wide,
key = cell, value = date_rating,
attractive_high:ugly_none,
factor_key = TRUE)
dating_long <- separate(data = dating_long,
col = cell, into = c("looks","personality"),
sep = "_", remove = TRUE)
dating_long$looks <- as_factor(dating_long$looks)
dating_long$personality <- as_factor(dating_long$personality)
# See new long format of data, where one row is one OBSERVATION.
# As well, notice that we have two columns (looks, personality)
# looks, personality, and participant are factors
print(dating_long)
# Set contrasts to match Field et al. (2012) textbook output
some_vs_none <- c(1, 1, -2)
hi_vs_av <- c(1, -1, 0)
attractive_vs_ugly <- c(1, 1, -2)
attractive_vs_average <- c(1, -1, 0)
contrasts(dating_long$personality) <- cbind(some_vs_none, hi_vs_av)
contrasts(dating_long$looks) <- cbind(attractive_vs_ugly, attractive_vs_average)
# Use ezANOVA
library(ez)
options(digits = 10)
dating_results <-ezANOVA(data = dating_long, dv = .(date_rating), wid = .(participant),
between = .(gender), within = .(looks, personality),
type = 3, detailed = TRUE)
# Make APA table
dating_table <- apa.ezANOVA.table(dating_results,
filename = "ex3_mixed_table.doc")
print(dating_table)
## End(Not run)
|
Example output
Registered S3 methods overwritten by 'lme4':
method from
cooks.distance.influence.merMod car
influence.merMod car
dfbeta.influence.merMod car
dfbetas.influence.merMod car
participant gender alcohol attractiveness
1 1 Female None 65
2 2 Female None 70
3 3 Female None 60
4 4 Female None 60
5 5 Female None 60
6 6 Female None 55
7 7 Female None 60
8 8 Female None 55
9 9 Female 2 Pints 70
10 10 Female 2 Pints 65
11 11 Female 2 Pints 60
12 12 Female 2 Pints 70
13 13 Female 2 Pints 65
14 14 Female 2 Pints 60
15 15 Female 2 Pints 60
16 16 Female 2 Pints 50
17 17 Female 4 Pints 55
18 18 Female 4 Pints 65
19 19 Female 4 Pints 70
20 20 Female 4 Pints 55
21 21 Female 4 Pints 55
22 22 Female 4 Pints 60
23 23 Female 4 Pints 50
24 24 Female 4 Pints 50
25 25 Male None 50
26 26 Male None 55
27 27 Male None 80
28 28 Male None 65
29 29 Male None 70
30 30 Male None 75
31 31 Male None 75
32 32 Male None 65
33 33 Male 2 Pints 45
34 34 Male 2 Pints 60
35 35 Male 2 Pints 85
36 36 Male 2 Pints 65
37 37 Male 2 Pints 70
38 38 Male 2 Pints 70
39 39 Male 2 Pints 80
40 40 Male 2 Pints 60
41 41 Male 4 Pints 30
42 42 Male 4 Pints 30
43 43 Male 4 Pints 30
44 44 Male 4 Pints 55
45 45 Male 4 Pints 35
46 46 Male 4 Pints 20
47 47 Male 4 Pints 45
48 48 Male 4 Pints 40
Coefficient covariances computed by hccm()
ANOVA results
Predictor df_num df_den SS_num SS_den F p ges
gender 1 42 168.75 3487.50 2.03 .161 .05
alcohol 2 42 3332.29 3487.50 20.07 .000 .49
gender x alcohol 2 42 1978.12 3487.50 11.91 .000 .36
Note. df_num indicates degrees of freedom numerator. df_den indicates degrees of freedom denominator.
SS_num indicates sum of squares numerator. SS_den indicates sum of squares denominator.
ges indicates generalized eta-squared.
# A tibble: 20 x 10
participant beer_positive beer_negative beer_neutral wine_positive
<fct> <int> <int> <int> <int>
1 P1 1 6 5 38
2 P2 43 30 8 20
3 P3 15 15 12 20
4 P4 40 30 19 28
5 P5 8 12 8 11
6 P6 17 17 15 17
7 P7 30 21 21 15
8 P8 34 23 28 27
9 P9 34 20 26 24
10 P10 26 27 27 23
11 P11 1 -19 -10 28
12 P12 7 -18 6 26
13 P13 22 -8 4 34
14 P14 30 -6 3 32
15 P15 40 -6 0 24
16 P16 15 -9 4 29
17 P17 20 -17 9 30
18 P18 9 -12 -5 24
19 P19 14 -11 7 34
20 P20 15 -6 13 23
# … with 5 more variables: wine_negative <int>, wine_neutral <int>,
# water_positive <int>, water_negative <int>, water_neutral <int>
# A tibble: 180 x 4
participant drink imagery attitude
<fct> <fct> <fct> <int>
1 P1 beer positive 1
2 P2 beer positive 43
3 P3 beer positive 15
4 P4 beer positive 40
5 P5 beer positive 8
6 P6 beer positive 17
7 P7 beer positive 30
8 P8 beer positive 34
9 P9 beer positive 34
10 P10 beer positive 26
# … with 170 more rows
ANOVA results
Predictor df_num df_den Epsilon SS_num SS_den F p ges
(Intercept) 1.00 19.00 11218.01 1920.11 111.01 .000 .41
drink 1.15 21.93 0.58 2092.34 7785.88 5.11 .030 .12
imagery 1.49 28.40 0.75 21628.68 3352.88 122.56 .000 .58
drink x imagery 3.19 60.68 0.80 2624.42 2906.69 17.15 .000 .14
Note. df_num indicates degrees of freedom numerator. df_den indicates degrees of freedom denominator.
Epsilon indicates Greenhouse-Geisser multiplier for degrees of freedom,
p-values and degrees of freedom in the table incorporate this correction.
SS_num indicates sum of squares numerator. SS_den indicates sum of squares denominator.
ges indicates generalized eta-squared.
# A tibble: 20 x 11
participant gender attractive_high average_high ugly_high attractive_some
<fct> <fct> <int> <int> <int> <int>
1 P01 Male 86 84 67 88
2 P02 Male 91 83 53 83
3 P03 Male 89 88 48 99
4 P04 Male 89 69 58 86
5 P05 Male 80 81 57 88
6 P06 Male 80 84 51 96
7 P07 Male 89 85 61 87
8 P08 Male 100 94 56 86
9 P09 Male 90 74 54 92
10 P10 Male 89 86 63 80
11 P11 Female 89 91 93 88
12 P12 Female 84 90 85 95
13 P13 Female 99 100 89 80
14 P14 Female 86 89 83 86
15 P15 Female 89 87 80 83
16 P16 Female 80 81 79 86
17 P17 Female 82 92 85 81
18 P18 Female 97 69 87 95
19 P19 Female 95 92 90 98
20 P20 Female 95 93 96 79
# … with 5 more variables: average_some <int>, ugly_some <int>,
# attractive_none <int>, average_none <int>, ugly_none <int>
# A tibble: 180 x 5
participant gender looks personality date_rating
<fct> <fct> <fct> <fct> <int>
1 P01 Male attractive high 86
2 P02 Male attractive high 91
3 P03 Male attractive high 89
4 P04 Male attractive high 89
5 P05 Male attractive high 80
6 P06 Male attractive high 80
7 P07 Male attractive high 89
8 P08 Male attractive high 100
9 P09 Male attractive high 90
10 P10 Male attractive high 89
# … with 170 more rows
ANOVA results
Predictor df_num df_den Epsilon SS_num SS_den F
(Intercept) 1.00 18.00 846249.80 760.22 20036.90
gender 1.00 18.00 0.20 760.22 0.00
looks 1.92 34.62 0.96 20779.63 882.71 423.73
personality 1.87 33.62 0.93 23233.60 1274.04 328.25
gender x looks 1.92 34.62 0.96 3944.10 882.71 80.43
gender x personality 1.87 33.62 0.93 4420.13 1274.04 62.45
looks x personality 3.20 57.55 0.80 4055.27 1992.62 36.63
gender x looks x personality 3.20 57.55 0.80 2669.67 1992.62 24.12
p ges
.000 .99
.946 .00
.000 .81
.000 .83
.000 .45
.000 .47
.000 .45
.000 .35
Note. df_num indicates degrees of freedom numerator. df_den indicates degrees of freedom denominator.
Epsilon indicates Greenhouse-Geisser multiplier for degrees of freedom,
p-values and degrees of freedom in the table incorporate this correction.
SS_num indicates sum of squares numerator. SS_den indicates sum of squares denominator.
ges indicates generalized eta-squared.
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