Package {Keng}

Title:	Knock Errors Off Nice Guesses
Version:	2026.9.0
Description:	Miscellaneous functions and data used in psychological research and teaching. Keng currently has four built-in datasets, and could (1) scale a vector; (2) divide a vector into three groups, (3) compute the cut-off values of Pearson's r with known sample size; (4) test the significance and compute the post-hoc power for Pearson's r with known sample size; (5) conduct a priori power analysis and plan the sample size for Pearson's r; (6) compare lm()'s fitted outputs using R-squared, f_squared, post-hoc power, and PRE (Proportional Reduction in Error, also called partial R-squared or partial Eta-squared); (7) calculate PRE from partial correlation, Cohen's f, or f_squared; (8) conduct a priori power analysis and plan the sample size for one or a set of predictors in regression analysis; (9) conduct post-hoc power analysis for one or a set of predictors in regression analysis with known sample size; (10) randomly pick numbers for Chinese Super Lotto and Double Color Balls; (11) assess course objective achievement in Outcome-Based Education.
License:	CC BY 4.0
Language:	en-US
Encoding:	UTF-8
Imports:	stats
Suggests:	ggplot2, knitr, rmarkdown, car, effectsize, tidyr, testthat (≥ 3.0.0)
Config/testthat/edition:	3
URL:	https://github.com/qyaozh/Keng
BugReports:	https://github.com/qyaozh/Keng/issues
Depends:	R (≥ 3.5)
LazyData:	true
VignetteBuilder:	knitr
Config/roxygen2/version:	8.1.0
NeedsCompilation:	no
Packaged:	2026-09-01 07:59:00 UTC; Yao
Author:	Qingyao Zhang [aut, cre]
Maintainer:	Qingyao Zhang <qingyaozhang@outlook.com>
Repository:	CRAN
Date/Publication:	2026-09-01 11:40:23 UTC

Scale a vector

Description

Scale a vector

Usage

Scale(x, m = 0, sd = NULL)

Arguments

Details

To scale x, its mean, or standard deviation (unit), or both, could be changed. The missing values of x are removed if any.

If m = 0 (default), and sd = NULL(default), x would be mean-centered (default).

If m = NULL, the mean of x would not changed. If m is not NULL, the mean of x would be transformed to sd.

If m = NULL or 0, and sd = 1, x would be standardized to be its z-score with m = 0 and sd = 1. The mean and standardized score are not necessarily 0 and 1.

Value

The scaled vector.

Examples

(x <- rnorm(10, 5, 2))
# Mean-center x.
Scale(x)
Scale(x, m = 0)
# Transform the mean of x to 3.
Scale(x, m = 3)
# Transform x to its z-score.
Scale(x, sd = 1)
# Standardize x with m = 100 and sd = 15.
Scale(x, m = 100, sd = 15)

Assess course objective achievement

Description

Assess course objective achievement

Usage

assess_coa(data, session_weights, objective_weights1, ...)

Arguments

Value

A data.frame containing grades of each session, final grades, and achievements of each objective. This data.frame also has an attribute named "weights" that contains a list of session_weights, objective_weights_matrix, and weighted_objective_weights_matrix

Examples

data <- data.frame(
  session1 = 60 + sample.int(40, 100, 1),
  session2 = 60 + sample.int(40, 100, 1),
  session3 = 60 + sample.int(40, 100, 1)
)
session_weights    <- c(0.2, 0.3, 0.5)
objective_weights1 <- c(0.1, 0.4, 0.5)
objective_weights2 <- c(0.2, 0.2, 0.6)
objective_weights2 <- c(0.3,   0, 0.7)
coa <- assess_coa(
  data,
  session_weights,
  objective_weights1,
  objective_weights2,
  objective_weights2
)
head(coa)
attr(coa, "weights")
colMeans(coa[row.names(attr(coa, "weights")[[2]])])

Calculate PRE from Cohen's f, f_squared, or partial correlation

Description

Calculate PRE from Cohen's f, f_squared, or partial correlation

Usage

calc_PRE(f = NULL, f_squared = NULL, r_p = NULL)

Arguments

Value

A list including PRE, the absolute value of r_p (partial correlation), Cohen's f_squared, and f.

References

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Routledge.

Examples

calc_PRE(f = 0.1)
calc_PRE(f_squared = 0.02)
calc_PRE(r_p = 0.2)

Compare lm()'s fitted outputs using PRE and R-squared.

Description

Compare lm()'s fitted outputs using PRE and R-squared.

Usage

compare_lm(
  fitC = NULL,
  fitA = NULL,
  n = NULL,
  PC = NULL,
  PA = NULL,
  SSEC = NULL,
  SSEA = NULL
)

Arguments

Details

compare_lm() compares model A with model C using PRE (Proportional Reduction in Error) , R-squared, f_squared, and post-hoc power. PRE is partial R-squared (called partial Eta-squared in Anova). There are two ways of using compare_lm(). The 1st is giving compare_lm() fitC and fitA. The 2nd is giving n, PC, PA, SSEC, and SSEA. The 1st way is more convenient, and it minimizes precision loss by omitting copying-and-pasting. Note that the F-tests for PRE and that for R-squared change are equivalent. Please refer to Judd et al. (2017) for more details about PRE, and refer to Aberson (2019) for more details about f_squared and post-hoc power.

Value

A matrix with 12 rows and 4 columns. The 1st column reports information for the baseline model (intercept-only model). the 2nd for model C, the third for model A, and the fourth for the change (model A vs. model C). SSE (Sum of Squared Errors), sample size n, df of SSE, and the number of parameters for baseline model, model C, model A, and change (model A vs. model C) are reported in rows 1-3. The information in the 4th column are all for the change; put differently, these results could quantify the effect of one or a set of new parameters model A has but model C doesn't. If fitC and fitA are not inferior to the intercept-only model, R-squared, Adjusted R-squared, PRE, PRE_adjusted, and f_squared for the full model (compared with the baseline model) are reported for model C and model A. If model C or model A has at least one predictor, F-test with p, and post-hoc power would be computed for the corresponding full model.

References

Aberson, C. L. (2019). Applied power analysis for the behavioral sciences. Routledge.

Judd, C. M., McClelland, G. H., & Ryan, C. S. (2017). Data analysis: A model Comparison approach to regression, ANOVA, and beyond. Routledge.

Examples

x1 <- rnorm(193)
x2 <- rnorm(193)
y <- 0.3 + 0.2*x1 + 0.1*x2 + rnorm(193)
dat <- data.frame(y, x1, x2)
# Fix the intercept to constant 1 using I().
fit1 <- lm(I(y - 1) ~ 0, dat)
# Free the intercept.
fit2 <- lm(y ~ 1, dat)
compare_lm(fit1, fit2)
# One predictor.
fit3 <- lm(y ~ x1, dat)
compare_lm(fit2, fit3)
# Fix the intercept to 0.3 using offset().
intercept <- rep(0.3, 193)
fit4 <- lm(y ~ 0 + x1 + offset(intercept), dat)
compare_lm(fit4, fit3)
# Two predictors.
fit5 <- lm(y ~ x1 + x2, dat)
compare_lm(fit2, fit5)
compare_lm(fit3, fit5)
# Fix the slope of x2 to 0.05 using offset().
fit6 <- lm(y ~ x1 + offset(0.05*x2), dat)
compare_lm(fit6, fit5)

Cut-off values of Pearson's correlation r with known sample size n.

Description

Cut-off values of Pearson's correlation r with known sample size n.

Usage

cut_r(n)

Arguments

Details

Given n and p, t and then r could be determined. The formula used could be found in test_r()'s documentation.

Value

A data.frame including the cut-off values of r at the significance levels of p = 0.1, 0.05, 0.01, 0.001. r with the absolute value larger than the cut-off value is significant at the corresponding significance level.

Examples

cut_r(193)

Three-wave data from the D research

Description

D refers to Depression. D research is a three-wave longitudinal survey research. Here are four data sets, depress1 only includes data collected at wave 1, depress2 at wave 2, and depress3 at wave 3. depress was created by joining depress1, depress2, and depress3 together. depress has done reverse-coding and has computed variable scores like depr1, which indicates the depression score at wave 1. depress serves to ease users' practicing plotting and statistical analyzing. depress1, depress2, and depress3 serves to support users' practicing data wrangling.

Usage

depress

Format

depress

A three-wave data frame with 185 rows and 323 columns:

date100

Date of the response. The 1 in the hundreds place indicates the 1st-wave survey. The rest variables also follow this naming rule.

id

Participant id

gender

Gender. 1 = boy, 2 = girl.

age

Age in year.

grade

Grade.

class

Class.

boarder

Boarder. 1 = this student lived at school during term time, 2 = this student did not live at school during term time.

cost

Cost in yuan per month.

fedu

Father education level. 0 = no formal school education, 1 = primary school, 2 = middle school, 3 = high school or equivalent, 4 = college or equivalent, 5 = master's degree, 6 = doctor's degree.

medu

Mother education level. The coding scheme is identical to that of fedu.

intervene

Intervene. 0 = control group, 1 = intervention group.

attr112

Attributional style questionnaire (Peterson, 1982). All items in this questionnaire were rated on a 7-point bipolar scale. The 1 in the tens place indicates the 1st situation, positive achievement situation. The 2 in the last place indicates the 2nd item, internal attribution. High scores indicate more internal attribution.

attr113

Attributional style questionnaire. The 3 in the last place indicates the 3rd item, stable attribution. High scores indicate more stable attribution.

attr114

Attributional style questionnaire. The 4 in the last place indicates the 4th item, global attribution. High scores indicate more global attribution.

attr115

Attributional style questionnaire. The 5 in the last place indicates the 5th item, the importance of this situation. High scores indicate more important.

attr122

Attributional style questionnaire. The 2 in the tens place indicates the 2nd situation, negative interpersonal relationship situation.

attr132

Attributional style questionnaire. The 3 in the tens place indicates the 3rd situation, positive interpersonal relationship situation.

attr142

Attributional style questionnaire. The 4 in the tens place indicates the 4th situation, negative achievement situation.

attr152

Attributional style questionnaire. The 5 in the tens place indicates the 5th situation, positive interpersonal relationship situation.

attr162

Attributional style questionnaire. The 6 in the tens place indicates the 6th situation, negative achievement situation.

attr172

Attributional style questionnaire. The 7 in the tens place indicates the 7th situation, positive achievement situation.

attr182

Attributional style questionnaire. The 8 in the tens place indicates the 8th situation, negative interpersonal relationship situation.

cope101

Coping inventory (Endler & Parker, 1994). All items in this inventory were rated on a 5-point Likert scale with 1 = rarely, 2 = seldom, 3 = moderately, 4 = often, 5 = very often. The "01" in the last two places indicates item 01. Item 01, as well as item 02, 06, 10, 16, 22, 25, 27, 28, 37, 40, 42, 43, 44, 47, and 48, measured task-oriented coping.

cope105

Coping inventory. The "05" in the last two places indicates item 05. Item 05, as well as item 07, 08, 14, 15, 17, 18, 20, 23, 26, 29, 31, 34, 35, 39, and 46, measured emotion-oriented coping.

cope103

Coping inventory. The "03" in the last two places indicates item 03. Item 03, as well as item 04, 09, 11, 12, 19, 21, 24, 30, 32, 33, 36, 38, 41, 45, and 49, measured avoidance-oriented coping.

cope113

Drinking. A coping strategy added by the D research to coping inventory.

cope150

Smoking. A coping strategy added by the D research to coping inventory.

cope201

Coping inventory, Wave 2, item 01.

cope302

Coping inventory, Wave 3, item 02.

depr101

Depression scale (Zung, et al., 1965). All items in this scale were rated on a 4-point Likert scale with 1 = rarely, 2 = seldom, 3 = often, 4 = always.

depr102

Depression scale. Item 02, 05, 06, 11, 12, 14, 16, 17, 18, and 20 has been reverse-coded. High scores indicate severe depression.

ecr101

Experience in close relationships scale (Fraley, et al., 2011). All items in this scale were rated on a 7-point Likert scale with 1 = strongly disagree, 2 = disagree, 3 = somewhat disagree, 4 = Neutral, 5 = somewhat agree, 6 = agree, 7 = strongly agree. Item 01, 03, 04, 06, 08, and 09 measured attachment avoidance.

ecr102

Experience in close relationships scale. Item 02. Item 02, 05, and 07 measured attachment anxiety.

ecr103

Experience in close relationships scale. Item 03. Item 03, 04, 06, and 09 has been reverse-coded.

na_waves

Not-available waves, ranging from 1 to 3.

attach_anx

Attachment anxiety, computed by averaging corresponding items.

attach_avo

Attachment aviodance, computed by averaging corresponding items.

internalP1

Internal attribution for positive situations at wave 1, computed by averaging corresponding items.

stableP1

Stable attribution for positive situations at wave 1, computed by averaging corresponding items.

globalP1

Global attribution for positive situations at wave 1, computed by averaging corresponding items.

internalN1

Internal attribution for negative situations at wave 1, computed by averaging corresponding items.

internalP2

Internal attribution for positive situations at wave 2, computed by averaging corresponding items.

cope_task1

Task-oriented coping at wave 1, computed by averaging corresponding items.

cope_emo1

Emotion-oriented coping at wave 1, computed by averaging corresponding items.

cope_avo1

Avoidance-oriented coping at wave 1, computed by averaging corresponding items.

cope_task2

Task-oriented coping at wave 2, computed by averaging corresponding items.

depr1

Depression at wave 1, computed by averaging corresponding items.

Source

Keng package.

The 1st wave data from the D research

Description

D refers to Depression. D research is a three-wave longitudinal survey research. Here are four data sets, depress1 only includes data collected at wave 1, depress2 at wave 2, and depress3 at wave 3. depress was created by joining depress1, depress2, and depress3 together. depress has done reverse-coding and has computed variable scores like depr1, which indicates the depression score at wave 1. depress serves to ease users' practicing plotting and statistical analyzing. depress1, depress2, and depress3 serves to support users' practicing data wrangling.

Usage

depress1

Format

depress1

A data frame with 174 rows and 122 columns at wave 1:

date

Date of the response.

id

Student id.

ecr01

Experience in close relationships scale, item 01.

ecr03

Experience in close relationships scale, item 03. Item 03, 04, 06, and 09 has NOT been reverse-coded in depress1.

attr12

Attribution style questionnaire, situation 1, item 2.

depr02

Depression Scale, item 02. Item 02, 05, 06, 11, 12, 14, 16, 17, 18, and 20 has NOT been reverse-coded in depress1.

Source

Keng package.

The 2nd wave data from the D research

Description

D refers to Depression. D research is a three-wave longitudinal survey research. Here are four data sets, depress1 only includes data collected at wave 1, depress2 at wave 2, and depress3 at wave 3. depress was created by joining depress1, depress2, and depress3 together. depress has done reverse-coding and has computed variable scores like depr1, which indicates the depression score at wave 1. depress serves to ease users' practicing plotting and statistical analyzing. depress1, depress2, and depress3 serves to support users' practicing data wrangling.

Usage

depress2

Format

depress2

A data frame with 173 rows and 104 columns at wave 2:

depr02

Depression Scale, item 02. Item 02, 05, 06, 11, 12, 14, 16, 17, 18, and 20 has NOT been reverse-coded in depress2.

Source

Keng package.

The 3rd wave data from the D research

Description

D refers to Depression. D research is a three-wave longitudinal survey research. Here are four data sets, depress1 only includes data collected at wave 1, depress2 at wave 2, and depress3 at wave 3. depress was created by joining depress1, depress2, and depress3 together. depress has done reverse-coding and has computed variable scores like depr1, which indicates the depression score at wave 1. depress serves to ease users' practicing plotting and statistical analyzing. depress1, depress2, and depress3 serves to support users' practicing data wrangling.

Usage

depress3

Format

depress3

A data frame with 172 rows and 72 columns at wave 3:

depr02

Depression Scale, item 02. Item 02, 05, 06, 11, 12, 14, 16, 17, 18, and 20 has NOT been reverse-coded in depress3.

Source

Keng package.

Divide a vector into three groups

Description

Divide a vector into three groups

Usage

divide(x, criterion = "1sd")

Arguments

Details

divide() divides a vector into three groups using the criterion of 1 SD, or proportions like 0.27. Differing from cut(), the medium interval of divide() does not include the left and right limits.

Value

An ordered factor with levels "Low < Medium < High".

Examples

x <- 1:100
divide(x)
divide(x, 0.27)

Pick Double Color Balls

Description

Pick Double Color Balls

Usage

pick_dcb(size = 1L, verbose = TRUE)

Arguments

Value

Print the numbers picked, and return the invisible balls list that stored these numbers.

Examples

pick_dcb(10)
out <- pick_dcb(10, verbose = FALSE)
out

Pick Super Lotto numbers

Description

Pick Super Lotto numbers

Usage

pick_sl(size = 1L, verbose = TRUE)

Arguments

Value

Print the numbers picked, and return the invisible balls list that stored these numbers.

Examples

# Example 1
pick_sl(10)
# Example 2
out <- pick_sl(10, verbose = FALSE)
out
# Example 3
# create an empty list
balls <- list(c(front = rep(NA, 5),
                back = rep(NA, 2))
)
luck <- list(c(front = c(10L, 13L, 14L, 19L, 27L),
               back = c(6L, 10L)))
# limit the max number of draws
max <- 9999
# try
# count the number of draws
i <- 0
while (!identical(balls, luck, max)) {
  i = i + 1
  balls = pick_sl(verbose = FALSE)
  if (identical(balls, luck)) {
    print(i)
    print(balls)
  }
  else
    if (i == max) {
      cat(i, "failed\n")
      break}
}

Plot the power against the sample size for the Keng_power class

Description

Plot the power against the sample size for the Keng_power class

Usage

## S3 method for class 'Keng_power'
plot(x, ...)

Arguments

Value

A plot of power against sample size.

Examples

plot(power_lm())
out <- power_r(0.2, n = 193)
plot(out)

Conduct a priori power analysis, and plan the sample size for regression analysis

Description

Conduct a priori power analysis, and plan the sample size for regression analysis

Usage

power_lm(
  PRE = 0.02,
  PC = 1L,
  PA = 2L,
  sig_level = 0.05,
  power = 0.8,
  n_ul = 1450000000L
)

Arguments

Details

n_ul determines the upper limit of the interval below which power_lm() searches for the minimum required sample size, hence the number of rows of the returned power table priori and the right limit of the horizontal axis of the returned power plot. When PRE is very small (e.g., less than 0.001) and power is larger than 0.8, a huge increase in sample size only brings about a trivial increase in power, which is cost-ineffective. To make power_lm() omit unnecessary searching, you could set n_ul to be a value less than 1.45e+09 (e.g., 10000).

Value

A Keng_power class, also a list. If sample size n is not given, the following results would be returned: ⁠[[1]]⁠ PRE; ⁠[[2]]⁠ f_squared, Cohen's f_squared derived from PRE; ⁠[[3]]⁠ PC; ⁠[[4]]⁠ PA; ⁠[[5]]⁠ sig_level, expected significance level for effects of focal predictors; ⁠[[6]]⁠ power, expected statistical power for effects of focal predictors; ⁠[[7]]⁠ n_ul, the upper limit of sample size; ⁠[[8]]⁠ root, the exact decimal n found by internal uniroot() to reach the expected power; ⁠[[9]]⁠ minimum, the minimum sample size required for focal predictors to reach the expected statistical power and significance level; ⁠[[10]]⁠ priori, a priori power table with increasing sample sizes (n_i), and corresponding df_A_C(the df of the numerator of the F-test, i.e., the difference of the dfs between model C and model A), df_A_i(the df of the denominator of the F-test, i.e., the df of the model A at the sample size n_i), F_i(the F-test of PRE at the sample size n_i), p_i(the p-value of F_i), lambda_i(the non-centrality parameter of the F-distribution for the alternative hypothesis, given PRE and n_i), power_i(the actual power of PRE at the sample size n_i).

By default, print() prints the primary but not all contents of the Keng_power class. To inspect more contents, use print.AsIs() or list extracting.

References

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Routledge.

Examples

power_lm()
print(power_lm())
plot(power_lm())

Conduct a priori power analysis, and plan the sample size for r.

Description

Conduct a priori power analysis, and plan the sample size for r.

Usage

power_r(r = 0.2, sig_level = 0.05, power = 0.8, n_ul = 1450000000L)

Arguments

Details

Power_r() follows Aberson (2019) approach to conduct power analysis. n_ul determines the upper limit of the interval below which power_r() searches for the minimum required sample size, hence the number of rows of the returned power table priori and the right limit of the horizontal axis of the returned power plot. When r is very small and power is larger than 0.8, a huge increase of sample size only brings about a trivial increase in power, which is cost-ineffective. To make power_r() omit unnecessary searching, you could set n_ul to be a value less than 1.45e+09 (e.g., 10000).

Value

A Keng_power class, also a list. If n is not given, the following results would be returned: ⁠[[1]]⁠ r, the given r; ⁠[[2]]⁠ d, Cohen's d derived from r; Cohen (1988) suggested >=0.2, >=0.5, and >=0.8 as cut-off values of d for small, medium, and large effect sizes, respectively; ⁠[[3]]⁠ sig_level, the expected significance level; ⁠[[4]]⁠ power, the expected power; ⁠[[5]]⁠ n_ul, the upper limit of sample size; ⁠[[6]]⁠ root, the exact decimal n found by internal uniroot() to reach the expected power; ⁠[[7]]⁠ minimum, the minimum required sample size. ⁠[[8]]⁠ priori, a priori power table with increasing sample sizes (n_i), and corresponding df_i (the df of t-test at the sample size n_i, df_i = n_i - 2), SE_i (the SE of r at the sample size n_i), t_i (the t-test of r), p_i (the p-value of t_i), delta_i (the non-centrality parameter of the t-distribution for the alternative hypothesis, given r and n_i), power_i (the actual power of r at the sample size n_i);.

If sample size n is given, the following results would also be returned: Integer n, the t_test of r at the sample size n with df, SE of r, p (the p-value of t-test), and the post-hoc power analysis with delta_post (the non-centrality parameter of the t-distribution for the alternative hypothesis), and power_post (the post-hoc power of r at the sample size n).

By default, print() prints the primary but not all contents of the Keng_power class. To inspect more contents, use print.AsIs() or list extracting.

References

Aberson, C. L. (2019). Applied power analysis for the behavioral sciences. Routledge.

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Routledge.

Examples

power_r(0.2)
print(power_r(0.04))
plot(power_r(0.04))

Compute lm's post-hoc power

Description

Compute lm's post-hoc power

Usage

powered_lm(PRE = 0.04, PC = 1L, PA = 2L, n = 200L, sig_level = 0.05)

Arguments

Value

Integer n, the F_test of PRE at the sample size n with df_A_C, df_A (the df of the model A at the sample size n), F (the F-test of PRE at the sample size n), p (the p-value of F-test at the sample size n), and the post-hoc power analysis with lambda (the non-centrality parameter of F at the sample size n), and power (the post-hoc power at the sample size n).

Examples

powered_lm()

Compute r's post-hoc power

Description

Compute r's post-hoc power

Usage

powered_r(r = 0.2, n = 200L, sig_level = 0.05)

Arguments

Value

Integer n, the t_test of r at the sample size n with df, SE of r, p (the p-value of t-test), and the post-hoc power analysis with delta (the non-centrality parameter of the t-distribution for the alternative hypothesis), and power (the post-hoc power of r at the sample size n).

References

Aberson, C. L. (2019). Applied power analysis for the behavioral sciences. Routledge.

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Routledge.

Examples

powered_r()

Print primary but not all contents of the Keng_power class

Description

Print primary but not all contents of the Keng_power class

Usage

## S3 method for class 'Keng_power'
print(x, ...)

Arguments

Value

None (invisible NULL).

Examples

power_lm()
power_lm(n = 200)
print(power_lm(n = 200))
x <- power_r(0.2, n = 193)
x

Test the significance, analyze the power, and plan the sample size for r.

Description

Test the significance, analyze the power, and plan the sample size for r.

Usage

test_r(r = NULL, n = NULL, sig_level = 0.05, power = 0.8)

Arguments

Details

To test the significance of the r using the one-sample t-test, the SE of r is determined by the following formula: SE = sqrt((1 - r^2)/(n - 2)). Another way is transforming r to Fisher's z using the following formula: fz = atanh(r) with the SE of fz being sqrt(n - 3). Fisher's z is commonly used to compare two Pearson's correlations from independent samples. Fisher's transformation is presented here only to satisfy the curiosity of users who are interested in the difference between t-test and Fisher's transformation.

The post-hoc power of r's t-test is computed through the way of Aberson (2019). Other software and R packages like SPSS and pwr give different power estimates due to underlying different formulas. Keng adopts Aberson's approach because this approach guarantees the equivalence of r and PRE.

Value

A list with the following results: ⁠[[1]]⁠ r, the given r; ⁠[[2]]⁠ d, Cohen's d derived from r; Cohen (1988) suggested >=0.2, >=0.5, and >=0.8 as cut-off values of d for small, medium, and large effect sizes, respectively. ⁠[[3]]⁠ Integer n; ⁠[[4]]⁠ t-test of r (incl., r, df of r, SE_r, t, p_r), 95% CI of r based on t -test (LLCI_r_t, ULCI_r_t), and post-hoc power of r (incl., delta_post, power_post); ⁠[[5]]⁠ Fisher's z transformation (incl., fz of r, z-test of fz [SE_fz, z, p_fz], and 95% CI of r derived from fz.

Note that the returned CI of r may be out of r's valid range [-1, 1]. This "error" is deliberately left to users, who should correct the CI manually in reports.

References

Aberson, C. L. (2019). Applied power analysis for the behavioral sciences. Routledge.

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Routledge.

Examples

test_r(0.2, 193)

# compare the p-values of t-test and Fisher's transformation
for (i in seq(30, 200, 10)) {
cat(c("n = ", i, ", difference between ps = ",
       format(
        abs(test_r(0.2, i)[["t_test"]]["p_r"] - test_r(0.2, i)[["Fisher_z"]]["p_fz"]),
        nsmall = 12,
        scientific = FALSE)),
      sep = "",
      fill = TRUE)
}

Three-wave data from the W research

Description

W refers to well-being. W research is a three-wave longitudinal survey research. Here are four data sets, well1 only includes data collected at wave 1, well2 at wave 2, and well3 at wave 3. well was created by joining well1, well2, and well3 together. well has done reverse-coding and has computed variable scores like flourish1, which indicates the flourish score at wave 1. well serves to ease users' practicing plotting and statistical analyzing. well1, well2, and well3 serves to support users' practicing data wrangling.

Usage

well

Format

well

A three-wave data frame with 200 rows and 413 columns:

id

Participant id

gender100

Gender. 0 = boy, 1 = girl. This variable ends with "100". The 1st digit indicates the wave number, and the last two digits indicate the item number. The rest variables also follow this naming rule.

age100

Age in years, wave 1.

grade100

Grade, wave 1.

income100

Monthly income (RMB) of the family, wave1.

loan300

National student loan in the past year. Reported at wave 3. 0 = Not received, 1 = Received.

cost100

Monthly cost of the participant, wave1. "1" = "<500RMB", "2" = "500-1000RMB", "3" = "1000-1500RMB","4" = "1500-2000RMB","5" = "2000-2500RMB", "6" = ">2500RMB".

SSES100

Subjective Socioeconomic Status, wave1. SSES was measured through the 10-step social ladder, with high scores indicating high status.

heema101

HEEMA scale, wave 1, item 01. HEEMA = The Hedonic, Eudaimonic, and Extrinsic Motivations for Activities (Lefebvre & Huta, 2021). HEEMA was rated on a 7-point scale (1 = not at all, 7 = very much). HEEMA measures three kind of motivations: Hedonic (item 01, 04, 06, 07, 09, 16), eudaimonic (item 02, 03, 05, 08, 10), and Extrinsic (item 11, 12, 13, 14, 15).

cssp101

Career Social Support from Parents, wave 1, item 01. CSS investigates four kind of support using a 5-point scale (1 = almost none, 5 = very much; Hou et al, 2010): material (item 01, 02, 03), advice (item 04, 05, 06, 07, 08, 09), emotional (item 10, 11, 12, 13), and information (14, 15, 16, 17, 18, 19, 20).

csss101

Career Social Support from Siblings, wave 1, item 01.

csst101

Career Social Support from Teachers, wave 1, item 01.

cssf101

Career Social Support from Friends, wave 1, item 01.

cssr101

Career Social Support from Relatives, wave 1, item 01.

grit101

Grit scale (Wei & Hu, 2017), wave 1, item 01. Grit is rated on a 5-point scale (1 = not at all like me, 5 = very much like me). Item 1, 3, 5, and 6 should be reverse-scored. Grit has two dimensions: perseverance (item 02, 04, 07, 08) and passion (item 01, 03, 05, 06). After reverse scoring, high scores indicate high perseverance and passion.

CET4100

CET4 score, wave 1. CET4 = College English Test Band 4. 0 = participants had not taken CET4 test.

flourish101

Flourish scale, wave 1, item 01. Flourish scale (Tong & Wang, 2017) was rated on a 5-point scale (1 = very disagree, 7 = very agree), with high scores indicating high eudaimonic well-being.

spne101

SPNE scale, wave 1, item 01. SPNE = Scale of Positive and Negative Experience (Tong & Wang, 2017). SPNE measures positive (item 01, 03, 05, 07, 10, 12) and negative (item 02, 04, 06, 08, 9, 11) emotions on a 5-point scale (1 = very rarely or never, 5 = very often or always). High positive emotion and low negative emotion indicate high hedonic well-being.

flourish1

Flourish, wave 1. All scale scores and dimension scores in this data are computed by averaging corresponding items.

posEmo1

Positive Emotions, wave 1.

negEmo1

Negative Emotions, wave 1.

grit1

Grit, wave 1.

perseverance1

Perseverance, wave 1.

passion1

Passion, wave 1.

hedonic1

Hedonic well-being motivation, wave 1.

eudaimonic1

Eudaimonic well-being motivation, wave 1.

extrinsic1

Extrinsic well-being motivation, wave 1.

csspm1

Career Social Support, Parents, Material, wave 1.

csspa1

Career Social Support, Parents, Advice, wave 1.

csspe1

Career Social Support, Parents, Emotional, wave 1.

csspi1

Career Social Support, Parents, Information, wave 1.

csssm1

Career Social Support, Siblings, Material, wave 1.

csstm1

Career Social Support, Teachers, Material, wave 1.

cssfm1

Career Social Support, Friends, Material, wave 1.

cssrm1

Career Social Support, Relatives, Material, wave 1.

cssp1

Career Social Support, Parents, wave 1.

cssm1

Career Social Support, Material, wave 1.

Source

Keng package.

References

Lefebvre, A., & Huta, V. (2021). Age and gender differences in eudaimonic, hedonic, and extrinsic motivations. Journal of Happiness Studies, 22, 2299-2321.

Hou, Z., Bai, R., & Yao, Y. (2010). Development of career social support inventory for Chinese college students. Chinese Journal of Clinical Psychology, 18(4), 439-442.

Wei, Y., & Hu, J. (2017). Grit: Concept, structure, influencing factors, and consequences. Psychology: Techniques and Applications, 5(1), 52-61.

Tong, K. K. & Wang, Y. Y. (2017). Validation of the Flourishing Scale and Scale of Positive and Negative Experience in a Chinese Community Sample. PLUS ONE, 12(8), e0181616.

The 1st wave data from the W research

Description

W refers to well-being. W research is a three-wave longitudinal survey research. Here are four data sets, well1 only includes data collected at wave 1, well2 at wave 2, and well3 at wave 3. well was created by joining well1, well2, and well3 together. well has done reverse-coding and has computed variable scores like flourish1, which indicates the flourish score at wave 1. well serves to ease users' practicing plotting and statistical analyzing. well1, well2, and well3 serves to support users' practicing data wrangling.

Usage

well1

Format

well1

The 1st wave data frame with 200 rows and 152 columns:

id

Participant id

gender

Gender. 0 = boy, 1 = girl.

age

Age in years.

grade

Grade.

income

Monthly income of the family.

cost

Monthly cost of the participant.

SSES

Subjective Socioeconomic Status. SSES was measured through the 10-step social ladder, with high scores indicating high status.

heema01

HEEMA scale, item 01. HEEMA = The Hedonic, Eudaimonic, and Extrinsic Motivations for Activities (Lefebvre & Huta, 2021). HEEMA was rated on a 7-point scale (1 = not at all, 7 = very much). HEEMA measures three kind of motivations: Hedonic (item 01, 04, 06, 07, 09, 16), eudaimonic (02, 03, 05, 08, 10), and Extrinsic (item 11, 12, 13, 14, 15).

cssp01

Career Social Support from Parents, item 01. CSS investigates four kind of support using a 5-point scale (1 = almost none, 5 = very much; Hou et al, 2010): material (item 01, 02, 03), advice (item 04, 05, 06, 07, 08, 09), emotional (item 10, 11, 12, 13), and information (14, 15, 16, 17, 18, 19, 20).

csss01

Career Social Support from Siblings, item 01.

csst01

Career Social Support from Teachers, item 01.

cssf01

Career Social Support from Friends, item 01.

cssr01

Career Social Support from Relatives, item 01.

grit01

Grit scale, item 01. Grit (Wei & Hu, 2017) is rated on a 5-point scale (1 = not at all like me, 5 = very much like me). Item 1, 3, 5, and 6 should be reverse-scored. Grit has two dimensions: perseverance (item 02, 04, 07, 08) and passion (item 01, 03, 05, 06). After reverse scoring, high scores indicate high perseverance and passion.

CET4

CET4 score. CET4 = College English Test Band 4. CET4 = 0 indicates paticipants had not taken CET4 test.

flourish01

Flourish scale, item 01. Flourish scale (Tong & Wang, 2017) was rated on a 5-point scale (1 = very disagree, 5 = very agree), with high scores indicating high eudaimonic well-being.

spne01

SPNE scale, item 01. SPNE = Scale of Positive and Negative Experience (Tong & Wang, 2017). SPNE measures positive (item 01, 03, 05, 07, 10, 12) and negative (item 02, 04, 06, 08, 9, 11) emotions on a 5-point scale (1 = very rarely or never, 5 = very often or always). High positive emotion and low negative emotion indicate high hedonic well-being.

Source

Keng package.

References

Lefebvre, A., & Huta, V. (2021). Age and gender differences in eudaimonic, hedonic, and extrinsic motivations. Journal of Happiness Studies, 22, 2299-2321.

Hou, Z., Bai, R., & Yao, Y. (2010). Development of career social support inventory for Chinese college students. Chinese Journal of Clinical Psychology, 18(4), 439-442.

Wei, Y., & Hu, J. (2017). Grit: Concept, structure, influencing factors, and consequences. Psychology: Techniques and Applications, 5(1), 52-61.

Tong, K. K. & Wang, Y. Y. (2017). Validation of the Flourishing Scale and Scale of Positive and Negative Experience in a Chinese Community Sample. PLUS ONE, 12(8), e0181616.

The 2nd wave data from the W research

Description

W refers to well-being. W research is a three-wave longitudinal survey research. Here are four data sets, well1 only includes data collected at wave 1, well2 at wave 2, and well3 at wave 3. well was created by joining well1, well2, and well3 together. well has done reverse-coding and has computed variable scores like flourish1, which indicates the flourish score at wave 1. well serves to ease users' practicing plotting and statistical analyzing. well1, well2, and well3 serves to support users' practicing data wrangling.

Usage

well2

Format

well

The 2nd wave data frame with 200 rows and 152 columns:

id

Participant id

gender

Gender. 0 = boy, 1 = girl. This variable ends with "100". The 1st digit indicates the wave number, and the last two digits indicate the item number. The rest variables also follow this naming rule.

age

Age in years.

grade

Grade.

income

Monthly income of the family.

cost

Monthly cost of the participant.

SSES

Subjective Socioeconomic Status. SSES was measured through the 10-step social ladder, with high scores indicating high status.

heema01

HEEMA scale, item 01. HEEMA = The Hedonic, Eudaimonic, and Extrinsic Motivations for Activities (Lefebvre & Huta, 2021). HEEMA was rated on a 7-point scale (1 = not at all, 7 = very much). HEEMA measures three kind of motivations: Hedonic (item 01, 04, 06, 07, 09, 16), eudaimonic (02, 03, 05, 08, 10), and Extrinsic (item 11, 12, 13, 14, 15).

cssp01

Career Social Support from Parents, item 01. CSS investigates four kind of support using a 5-point scale (1 = almost none, 5 = very much; Hou et al, 2010): material (item 01, 02, 03), advice (item 04, 05, 06, 07, 08, 09), emotional (item 10, 11, 12, 13), and information (14, 15, 16, 17, 18, 19, 20).

csss01

Career Social Support from Siblings, item 01.

csst01

Career Social Support from Teachers, item 01.

cssf01

Career Social Support from Friends, item 01.

cssr01

Career Social Support from Relatives, item 01.

grit01

Grit scale, item 01. Grit (Wei & Hu, 2017) is rated on a 5-point scale (1 = not at all like me, 5 = very much like me). Item 1, 3, 5, and 6 should be reverse-scored. Grit has two dimensions: perseverance (item 02, 04, 07, 08) and passion (item 01, 03, 05, 06). After reverse scoring, high scores indicate high perseverance and passion.

CET4

CET4 score. CET4 = College English Test Band 4. CET4 = 0 indicates paticipants had not taken CET4 test.

flourish01

Flourish scale, item 01. Flourish scale (Tong & Wang, 2017) was rated on a 5-point scale (1 = very disagree, 5 = very agree), with high scores indicating high eudaimonic well-being.

spne01

SPNE scale, item 01. SPNE = Scale of Positive and Negative Experience (Tong & Wang, 2017). SPNE measures positive (item 01, 03, 05, 07, 10, 12) and negative (item 02, 04, 06, 08, 9, 11) emotions on a 5-point scale (1 = very rarely or never, 5 = very often or always). High positive emotion and low negative emotion indicate high hedonic well-being.

Source

Keng package.

References

Lefebvre, A., & Huta, V. (2021). Age and gender differences in eudaimonic, hedonic, and extrinsic motivations. Journal of Happiness Studies, 22, 2299-2321.

Hou, Z., Bai, R., & Yao, Y. (2010). Development of career social support inventory for Chinese college students. Chinese Journal of Clinical Psychology, 18(4), 439-442.

Wei, Y., & Hu, J. (2017). Grit: Concept, structure, influencing factors, and consequences. Psychology: Techniques and Applications, 5(1), 52-61.

Tong, K. K. & Wang, Y. Y. (2017). Validation of the Flourishing Scale and Scale of Positive and Negative Experience in a Chinese Community Sample. PLUS ONE, 12(8), e0181616.

The 3rd wave data from the W research

Description

W refers to well-being. W research is a three-wave longitudinal survey research. Here are four data sets, well1 only includes data collected at wave 1, well2 at wave 2, and well3 at wave 3. well was created by joining well1, well2, and well3 together. well has done reverse-coding and has computed variable scores like flourish1, which indicates the flourish score at wave 1. well serves to ease users' practicing plotting and statistical analyzing. well1, well2, and well3 serves to support users' practicing data wrangling.

Usage

well3

Format

well

The 3rd wave data frame with 200 rows and 152 columns:

id

Participant id

gender

Gender. 0 = boy, 1 = girl. This variable ends with "100". The 1st digit indicates the wave number, and the last two digits indicate the item number. The rest variables also follow this naming rule.

age

Age in years.

grade

Grade.

loan

Received national student loan in the past year.

heema01

HEEMA scale, item 01. HEEMA = The Hedonic, Eudaimonic, and Extrinsic Motivations for Activities (Lefebvre & Huta, 2021). HEEMA was rated on a 7-point scale (1 = not at all, 7 = very much). HEEMA measures three kind of motivations: Hedonic (item 01, 04, 06, 07, 09, 16), eudaimonic (02, 03, 05, 08, 10), and Extrinsic (item 11, 12, 13, 14, 15).

grit01

Grit scale, item 01. Grit (Wei & Hu, 2017) is rated on a 5-point scale (1 = not at all like me, 5 = very much like me). Item 1, 3, 5, and 6 should be reverse-scored. Grit has two dimensions: perseverance (item 02, 04, 07, 08) and passion (item 01, 03, 05, 06). After reverse scoring, high scores indicate high perseverance and passion.

CET4

CET4 score. CET4 = College English Test Band 4. CET4 = 0 indicates paticipants had not taken CET4 test.

flourish01

Flourish scale, item 01. Flourish scale (Tong & Wang, 2017) was rated on a 5-point scale (1 = very disagree, 5 = very agree), with high scores indicating high eudaimonic well-being.

spne01

SPNE scale, item 01. SPNE = Scale of Positive and Negative Experience (Tong & Wang, 2017). SPNE measures positive (item 01, 03, 05, 07, 10, 12) and negative (item 02, 04, 06, 08, 9, 11) emotions on a 5-point scale (1 = very rarely or never, 5 = very often or always). High positive emotion and low negative emotion indicate high hedonic well-being.

Source

Keng package.

References

Lefebvre, A., & Huta, V. (2021). Age and gender differences in eudaimonic, hedonic, and extrinsic motivations. Journal of Happiness Studies, 22, 2299-2321.

Hou, Z., Bai, R., & Yao, Y. (2010). Development of career social support inventory for Chinese college students. Chinese Journal of Clinical Psychology, 18(4), 439-442.

Wei, Y., & Hu, J. (2017). Grit: Concept, structure, influencing factors, and consequences. Psychology: Techniques and Applications, 5(1), 52-61.

Tong, K. K. & Wang, Y. Y. (2017). Validation of the Flourishing Scale and Scale of Positive and Negative Experience in a Chinese Community Sample. PLUS ONE, 12(8), e0181616.