1 Description

This is the codebook of “Study 1” from the manuscript http://dx.doi.org/10.23668/psycharchives.3364 .
Data is embeded in this file. To download the data as CSV click here.

Structure of the codebook:

  • Participants consecuatively received two abstracts (stimulus)
  • They answered the scales in response to one abstract (stimulus) at a time
  • The codebook reports each item and scale for the abstracts together (as in data set), therefore the reliability scores are not necessarily as reported in the manuscript
  • for analyses that distinguish between the two measurement times, download the data and separate by variable meas_rep

2 Metadata

rbt_ugr <- rio::import(here("9_data+codebooks/rbt_study1_undergrad.csv"))

# we need to invert items back to original format
inv7fct <- function(x) (8-as.numeric(x))

rbt_ugr <- rbt_ugr %>%
    mutate_at(vars(exp_1:exp_6, int_1:int_4, ben_1:ben_4),
              list(~inv7fct(.)))
metadata(rbt_ugr)$name <- "Journals’ Open Science Badges Foster Trust in Scientists. Study 1: Undergraduates Sample."
metadata(rbt_ugr)$description <- "Code book to manuscript"
metadata(rbt_ugr)$identifier <- ""
metadata(rbt_ugr)$datePublished <- "2021-07-12"
metadata(rbt_ugr)$creator <- list(
      "@type" = "Person",
      givenName = "Schneider", familyName = "Jürgen",
      email = "juergen.schneider@uni-tuebingen.de", 
      affiliation = list("@type" = "Organization",
        name = "University of Tübingen"))
metadata(rbt_ugr)$citation <- "Schneider, J. (2021). Journals’ Open Science Badges Foster Trust in Scientists. Codebook of Study 1: Undergraduates sample"
# add variable labels #########################################################################
var_label(rbt_ugr) <- list(
        treat = "Treatment condition, the participant was assigned to.",
        exp_1 = "competent - incompetent",
        exp_2 = "intelligent - unintelligent",
        exp_3 = "well educated -    poorly educated",
        exp_4 = "professional - unprofessional",
        exp_5 = "experienced - inexperienced",
        exp_6 = "qualified - unqualified",
        int_1 = "sincere - insincere",
        int_2 = "honest - dishonest",
        int_3 = "just - unjust",
        int_4 = "fair - unfair",
        ben_1 = "moral - immoral",
        ben_2 = "ethical - unethical",
        ben_3 = "responsible - irresponsible",
        ben_4 = "considerate - inconsiderate",
        meas_rep = "Measurement repetition, first and second measurement",
        tsm_1 = "The insights from the text are arbitrary.",
        tsm_2 = "The knowledge contained in the text cannot be generalized to other situations at all.",
        tsm_3 = "The opposite of the knowledge formulated in the text would be equally right/wrong.",
        tsm_4 = "The knowledge formulated in the text cannot claim validity for other situations.",
        tch_1 = "It is transparent which data form the basis of the study.",
        tch_2 = "Interested parties can have a close look at the questionnaire of the described study.",
        tch_3 = "The data collected in the study are publicly available.",
        tch_4 = "The authors make it easy for other researchers to understand their statistical analyses.",
        tch_5 = "If other researchers want to repeat the study, they have easy access to the questionnaires used.",
        semester = "How many semesters of teaching/education-related coursework are you in (counting bachelor's degrees)?",
        sex = "Sex",
        age = "Age"
)


# add value labels ###########################################################################
# Treatment
val_labels(rbt_ugr$treat) <- c("Greyed out badges (no adherence to Open Science standards)" = "GB",
                               "Control Condition (no badges)" = "CC",
                               "Colored out badges (adherence to Open Science standards)" = "CB")

# add value labels (without "don't know" option) 
add_likert_labels4 <- function(x) {
  val_labels(x) <- c("fully disagree" = 1, 
                     "[empty 1]" = 2, 
                     "[empty 2]" = 3,
                     "fully agree" = 4)
  x
}

rbt_ugr <- rbt_ugr %>%
  mutate_at(vars(tsm_1:tsm_4),  add_likert_labels4)


# add value labels (with "don't know" option)
add_likert_labels4dk <- function(x) {
  val_labels(x) <- c("fully disagree" = 1, 
                     "[empty 1]" = 2, 
                     "[empty 2]" = 3,
                     "fully agree" = 4,
                     "don't know" = -999)
  x
}

rbt_ugr <- rbt_ugr %>%
  mutate_at(vars(tch_1:tch_5),  add_likert_labels4dk)


# semantic differentials
add_semantic_diff <- function(x) {
  val_labels(x) <- c("1" = 1,
                     "2" = 2,
                     "3" = 3,
                     "4" = 4,
                     "5" = 5,
                     "6" = 6,
                     "7" = 7)
  x
}

rbt_ugr <- rbt_ugr %>%
  mutate_at(vars(exp_1:exp_6, int_1:int_4, ben_1:ben_4),  add_semantic_diff)

# sex
val_labels(rbt_ugr$sex) <- c("female" = 1,
                             "male" = 2,
                             "other" = 3)

# measurement time
val_labels(rbt_ugr$meas_rep) <- c("first measurement" = 1,
                                  "second measurement" = 2)
 

# Define scales ##########################################################################
rbt_ugr$exp <- rbt_ugr %>% 
  select(exp_1:exp_6) %>% 
  aggregate_and_document_scale()

rbt_ugr$int <- rbt_ugr %>% 
  select(int_1:int_4) %>% 
  aggregate_and_document_scale()

rbt_ugr$ben <- rbt_ugr %>% 
  select(ben_1:ben_4) %>% 
  aggregate_and_document_scale()

rbt_ugr$tsm <- rbt_ugr %>% 
  select(tsm_1:tsm_4) %>% 
  aggregate_and_document_scale()

rbt_ugr$tch <- rbt_ugr %>% 
  select(tch_1:tch_5) %>% 
  aggregate_and_document_scale()


# detect scales #########################################################################
rbt_ugr <- detect_scales(rbt_ugr, quiet = FALSE)

3 Codebook

3.0.1 Metadata

3.0.1.1 Description

Dataset name: Journals’ Open Science Badges Foster Trust in Scientists. Study 1: Undergraduates Sample.

Code book to manuscript

Metadata for search engines

  • Citation: Schneider, J. (2021). Journals’ Open Science Badges Foster Trust in Scientists. Codebook of Study 1: Undergraduates sample

  • Identifier:

  • Date published: 2021-07-12

  • Creator:

name value
@type Person
givenName Schneider
familyName Jürgen
email
affiliation Organization , University of Tübingen
x
session
treat
exp_1
exp_2
exp_3
exp_4
exp_5
exp_6
int_1
int_2
int_3
int_4
ben_1
ben_2
ben_3
ben_4
meas_rep
tsm_1
tsm_2
tsm_3
tsm_4
age
semester
sex
tch_1
tch_2
tch_3
tch_4
tch_5
exp
int
ben
tsm
tch

#Variables

3.0.2 treat

Treatment condition, the participant was assigned to.

3.0.2.1 Distribution

Distribution of values for treat

Distribution of values for treat

0 missing values.

3.0.2.2 Summary statistics

name label data_type n_missing complete_rate n_unique empty min median max whitespace n_value_labels
treat Treatment condition, the participant was assigned to. haven_labelled 0 1 3 0 2 NA 2 0 3

3.0.2.3 Value labels

Response choices
name value
1 GB
2 CC
3 CB

3.0.3 meas_rep

Measurement repetition, first and second measurement

3.0.3.1 Distribution

Distribution of values for meas_rep

Distribution of values for meas_rep

0 missing values.

3.0.3.2 Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
meas_rep Measurement repetition, first and second measurement haven_labelled 0 1 1 1 2 1.487666 0.5003228 2 ▇▁▁▁▁▁▁▇

3.0.3.3 Value labels

Response choices
name value
first measurement 1
second measurement 2

3.0.4 age

Age

3.0.4.1 Distribution

Distribution of values for age

Distribution of values for age

21 missing values.

3.0.4.2 Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
age Age character 21 0.9601518 4 0 3 5 0

3.0.5 semester

How many semesters of teaching/education-related coursework are you in (counting bachelor’s degrees)?

3.0.5.1 Distribution

Distribution of values for semester

Distribution of values for semester

21 missing values.

3.0.5.2 Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
semester How many semesters of teaching/education-related coursework are you in (counting bachelor’s degrees)? numeric 21 0.9601518 1 5 19 5.857708 3.679689 ▇▅▅▁▁

3.0.6 sex

Sex

3.0.6.1 Distribution

Distribution of values for sex

Distribution of values for sex

21 missing values.

3.0.6.2 Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
sex Sex haven_labelled 21 0.9601518 1 2 3 1.711463 0.4706716 3 ▃▁▁▇▁▁▁▁

3.0.6.3 Value labels

Response choices
name value
female 1
male 2
other 3

3.0.7 Scale: exp

3.0.7.1 Overview

Reliability: ωtotal [95% CI] = 0.94 [not computed].

Missing: 0.

Likert plot of scale exp items

Likert plot of scale exp items

Distribution of scale exp

Distribution of scale exp

3.0.7.2 Reliability details

3.0.7.2.1 Scale diagnosis
3.0.7.2.1.1 Reliability (internal consistency) estimates
3.0.7.2.1.1 Scale structure
3.0.7.2.1.1 Information about this scale
Dataframe: res$dat
Items: exp_1, exp_2, exp_3, exp_4, exp_5 & exp_6
Observations: 527
Positive correlations: 15
Number of correlations: 15
Percentage positive correlations: 100
3.0.7.2.1.1 Estimates assuming interval level
Omega (total): 0.94
Omega (hierarchical): 0.93
Revelle’s Omega (total): 0.96
Greatest Lower Bound (GLB): 0.94
Coefficient H: 0.94
Coefficient Alpha: 0.94

(Estimates assuming ordinal level not computed, as the polychoric correlation matrix has missing values.)

Note: the normal point estimate and confidence interval for omega are based on the procedure suggested by Dunn, Baguley & Brunsden (2013) using the MBESS function ci.reliability, whereas the psych package point estimate was suggested in Revelle & Zinbarg (2008). See the help (‘?ufs::scaleStructure’) for more information.

3.0.7.2.1.2 Eigen values

4.585, 0.376, 0.32, 0.274, 0.229 & 0.216

3.0.7.2.1.3 Factor analysis (reproducing only shared variance)
ML1
exp_1 0.886
exp_2 0.789
exp_3 0.863
exp_4 0.838
exp_5 0.824
exp_6 0.878
3.0.7.2.1.4 Component analysis (reproducing full covariance matrix)
PC1
exp_1 0.900
exp_2 0.833
exp_3 0.885
exp_4 0.870
exp_5 0.860
exp_6 0.895
3.0.7.2.1.5 Item descriptives
mean median var sd IQR se min q1 q3 max skew kurt dip n NA valid
exp_1 2.575 2 1.6745 1.294 1 0.0564 1 1 3 7 0.8751 0.4237 0.111 527 0 527
exp_2 2.5636 2 1.3301 1.1533 1 0.0502 1 1 3 6 0.5347 -0.1871 0.1376 527 0 527
exp_3 2.6186 2 1.5786 1.2564 1 0.0547 1 1 4 6 0.6334 -0.1285 0.1157 527 0 527
exp_4 2.6907 2 1.754 1.3244 2 0.0577 1 1 4 7 0.7399 0.0837 0.1053 527 0 527
exp_5 2.8956 3 1.9187 1.3852 2 0.0603 1 2 4 7 0.6057 -0.1 0.1262 527 0 527
exp_6 2.5712 2 1.5382 1.2402 1 0.054 1 1 4 7 0.6827 0.0469 0.111 527 0 527
3.0.7.2.1.6 Scattermatrix
Scatterplot

Scatterplot

3.0.7.3 Summary statistics

name label data_type value_labels n_missing complete_rate min median max mean sd n_value_labels hist
exp_1 competent - incompetent haven_labelled 1. 1,
2. 2,
3. 3,
4. 4,
5. 5,
6. 6,
7. 7		 0 1 1 2 7 2.574953 1.294026 7 ▅▇▅▂▁▂▁▁
exp_2 intelligent - unintelligent haven_labelled 1. 1,
2. 2,
3. 3,
4. 4,
5. 5,
6. 6,
7. 7		 0 1 1 2 6 2.563567 1.153290 7 ▅▇▁▆▃▁▁▁
exp_3 well educated - poorly educated haven_labelled 1. 1,
2. 2,
3. 3,
4. 4,
5. 5,
6. 6,
7. 7		 0 1 1 2 6 2.618596 1.256419 7 ▅▇▁▆▃▁▁▁
exp_4 professional - unprofessional haven_labelled 1. 1,
2. 2,
3. 3,
4. 4,
5. 5,
6. 6,
7. 7		 0 1 1 2 7 2.690702 1.324373 7 ▅▇▅▃▁▂▁▁
exp_5 experienced - inexperienced haven_labelled 1. 1,
2. 2,
3. 3,
4. 4,
5. 5,
6. 6,
7. 7		 0 1 1 3 7 2.895636 1.385188 7 ▅▇▇▅▁▂▁▁
exp_6 qualified - unqualified haven_labelled 1. 1,
2. 2,
3. 3,
4. 4,
5. 5,
6. 6,
7. 7		 0 1 1 2 7 2.571157 1.240233 7 ▅▇▅▃▁▁▁▁

3.0.8 Scale: int

3.0.8.1 Overview

Reliability: ωordinal [95% CI] = 0.92 [0.9;0.93].

Missing: 0.

Likert plot of scale int items

Likert plot of scale int items

Distribution of scale int

Distribution of scale int

3.0.8.2 Reliability details

3.0.8.2.1 Scale diagnosis
3.0.8.2.1.1 Reliability (internal consistency) estimates
3.0.8.2.1.1 Scale structure
3.0.8.2.1.1 Information about this scale
Dataframe: res$dat
Items: int_1, int_2, int_3 & int_4
Observations: 527
Positive correlations: 6
Number of correlations: 6
Percentage positive correlations: 100
3.0.8.2.1.1 Estimates assuming interval level
Omega (total): 0.90
Omega (hierarchical): 0.85
Revelle’s Omega (total): 0.93
Greatest Lower Bound (GLB): 0.93
Coefficient H: 0.90
Coefficient Alpha: 0.90

3.0.8.2.1.1 Confidence intervals

Omega (total): [0.88; 0.91]
Coefficient Alpha: [0.88; 0.91]
3.0.8.2.1.1 Estimates assuming ordinal level
Ordinal Omega (total): 0.92
Ordinal Omega (hierarch.): 0.91
Ordinal Coefficient Alpha: 0.92

3.0.8.2.1.1 Confidence intervals

Ordinal Omega (total): [0.9; 0.93]
Ordinal Coefficient Alpha: [0.91; 0.93]

Note: the normal point estimate and confidence interval for omega are based on the procedure suggested by Dunn, Baguley & Brunsden (2013) using the MBESS function ci.reliability, whereas the psych package point estimate was suggested in Revelle & Zinbarg (2008). See the help (‘?ufs::scaleStructure’) for more information.

3.0.8.2.1.2 Eigen values

3.046, 0.482, 0.276 & 0.196

3.0.8.2.1.3 Factor analysis (reproducing only shared variance)
ML1
int_1 0.866
int_2 0.879
int_3 0.753
int_4 0.796
3.0.8.2.1.4 Component analysis (reproducing full covariance matrix)
PC1
int_1 0.881
int_2 0.888
int_3 0.847
int_4 0.874
3.0.8.2.1.5 Item descriptives
mean median var sd IQR se min q1 q3 max skew kurt dip n NA valid
int_1 2.6755 2 1.5466 1.2436 2 0.0542 1 1 4 7 0.491 -0.3292 0.1148 527 0 527
int_2 2.666 2 1.721 1.3119 2 0.0571 1 1 4 7 0.4772 -0.4838 0.1101 527 0 527
int_3 2.7723 3 1.3777 1.1738 2 0.0511 1 2 4 7 0.103 -0.7634 0.1376 527 0 527
int_4 2.6831 2 1.4983 1.224 2 0.0533 1 1 4 7 0.4485 -0.3486 0.111 527 0 527
3.0.8.2.1.6 Scattermatrix
Scatterplot

Scatterplot

3.0.8.3 Summary statistics

name label data_type value_labels n_missing complete_rate min median max mean sd n_value_labels hist
int_1 sincere - insincere haven_labelled 1. 1,
2. 2,
3. 3,
4. 4,
5. 5,
6. 6,
7. 7		 0 1 1 2 7 2.675522 1.243626 7 ▅▇▆▅▁▂▁▁
int_2 honest - dishonest haven_labelled 1. 1,
2. 2,
3. 3,
4. 4,
5. 5,
6. 6,
7. 7		 0 1 1 2 7 2.666034 1.311851 7 ▆▇▆▆▁▂▁▁
int_3 just - unjust haven_labelled 1. 1,
2. 2,
3. 3,
4. 4,
5. 5,
6. 6,
7. 7		 0 1 1 3 7 2.772296 1.173759 7 ▅▇▇▇▁▁▁▁
int_4 fair - unfair haven_labelled 1. 1,
2. 2,
3. 3,
4. 4,
5. 5,
6. 6,
7. 7		 0 1 1 2 7 2.683112 1.224030 7 ▅▇▅▆▁▁▁▁

3.0.9 Scale: ben

3.0.9.1 Overview

Reliability: ωordinal [95% CI] = 0.9 [0.89;0.91].

Missing: 0.

Likert plot of scale ben items

Likert plot of scale ben items

Distribution of scale ben

Distribution of scale ben

3.0.9.2 Reliability details

3.0.9.2.1 Scale diagnosis
3.0.9.2.1.1 Reliability (internal consistency) estimates
3.0.9.2.1.1 Scale structure
3.0.9.2.1.1 Information about this scale
Dataframe: res$dat
Items: ben_1, ben_2, ben_3 & ben_4
Observations: 527
Positive correlations: 6
Number of correlations: 6
Percentage positive correlations: 100
3.0.9.2.1.1 Estimates assuming interval level
Omega (total): 0.88
Omega (hierarchical): 0.85
Revelle’s Omega (total): 0.90
Greatest Lower Bound (GLB): 0.90
Coefficient H: 0.89
Coefficient Alpha: 0.88

3.0.9.2.1.1 Confidence intervals

Omega (total): [0.86; 0.9]
Coefficient Alpha: [0.86; 0.9]
3.0.9.2.1.1 Estimates assuming ordinal level
Ordinal Omega (total): 0.9
Ordinal Omega (hierarch.): 0.9
Ordinal Coefficient Alpha: 0.9

3.0.9.2.1.1 Confidence intervals

Ordinal Omega (total): [0.89; 0.91]
Ordinal Coefficient Alpha: [0.89; 0.91]

Note: the normal point estimate and confidence interval for omega are based on the procedure suggested by Dunn, Baguley & Brunsden (2013) using the MBESS function ci.reliability, whereas the psych package point estimate was suggested in Revelle & Zinbarg (2008). See the help (‘?ufs::scaleStructure’) for more information.

3.0.9.2.1.2 Eigen values

2.931, 0.462, 0.363 & 0.244

3.0.9.2.1.3 Factor analysis (reproducing only shared variance)
ML1
ben_1 0.855
ben_2 0.857
ben_3 0.759
ben_4 0.731
3.0.9.2.1.4 Component analysis (reproducing full covariance matrix)
PC1
ben_1 0.876
ben_2 0.877
ben_3 0.844
ben_4 0.826
3.0.9.2.1.5 Item descriptives
mean median var sd IQR se min q1 q3 max skew kurt dip n NA valid
ben_1 2.7723 3 1.4538 1.2057 2 0.0525 1 2 4 7 0.2293 -0.429 0.1319 527 0 527
ben_2 2.8311 3 1.5893 1.2607 2 0.0549 1 2 4 7 0.2239 -0.3204 0.1186 527 0 527
ben_3 2.7324 3 1.5994 1.2647 2 0.0551 1 2 4 7 0.6325 0.2817 0.111 527 0 527
ben_4 2.9962 3 1.5095 1.2286 2 0.0535 1 2 4 7 0.0937 -0.5434 0.1224 527 0 527
3.0.9.2.1.6 Scattermatrix
Scatterplot

Scatterplot

3.0.9.3 Summary statistics

name label data_type value_labels n_missing complete_rate min median max mean sd n_value_labels hist
ben_1 moral - immoral haven_labelled 1. 1,
2. 2,
3. 3,
4. 4,
5. 5,
6. 6,
7. 7		 0 1 1 3 7 2.772296 1.205718 7 ▅▇▇▇▁▁▁▁
ben_2 ethical - unethical haven_labelled 1. 1,
2. 2,
3. 3,
4. 4,
5. 5,
6. 6,
7. 7		 0 1 1 3 7 2.831119 1.260673 7 ▅▆▆▇▁▁▁▁
ben_3 responsible - irresponsible haven_labelled 1. 1,
2. 2,
3. 3,
4. 4,
5. 5,
6. 6,
7. 7		 0 1 1 3 7 2.732448 1.264667 7 ▃▇▆▅▁▁▁▁
ben_4 considerate - inconsiderate haven_labelled 1. 1,
2. 2,
3. 3,
4. 4,
5. 5,
6. 6,
7. 7		 0 1 1 3 7 2.996205 1.228614 7 ▃▆▆▇▁▂▁▁

3.0.10 Scale: tsm

3.0.10.1 Overview

Reliability: ωordinal [95% CI] = 0.74 [0.7;0.77].

Missing: 0.

Likert plot of scale tsm items

Likert plot of scale tsm items

Distribution of scale tsm

Distribution of scale tsm

3.0.10.2 Reliability details

3.0.10.2.1 Scale diagnosis
3.0.10.2.1.1 Reliability (internal consistency) estimates
3.0.10.2.1.1 Scale structure
3.0.10.2.1.1 Information about this scale
Dataframe: res$dat
Items: tsm_1, tsm_2, tsm_3 & tsm_4
Observations: 527
Positive correlations: 6
Number of correlations: 6
Percentage positive correlations: 100
3.0.10.2.1.1 Estimates assuming interval level
Omega (total): 0.68
Omega (hierarchical): 0.60
Revelle’s Omega (total): 0.74
Greatest Lower Bound (GLB): 0.73
Coefficient H: 0.72
Coefficient Alpha: 0.67

3.0.10.2.1.1 Confidence intervals

Omega (total): [0.63; 0.72]
Coefficient Alpha: [0.62; 0.72]
3.0.10.2.1.1 Estimates assuming ordinal level
Ordinal Omega (total): 0.74
Ordinal Omega (hierarch.): 0.72
Ordinal Coefficient Alpha: 0.73

3.0.10.2.1.1 Confidence intervals

Ordinal Omega (total): [0.7; 0.77]
Ordinal Coefficient Alpha: [0.7; 0.77]

Note: the normal point estimate and confidence interval for omega are based on the procedure suggested by Dunn, Baguley & Brunsden (2013) using the MBESS function ci.reliability, whereas the psych package point estimate was suggested in Revelle & Zinbarg (2008). See the help (‘?ufs::scaleStructure’) for more information.

3.0.10.2.1.2 Eigen values

2.025, 0.902, 0.606 & 0.467

3.0.10.2.1.3 Factor analysis (reproducing only shared variance)
ML1
tsm_1 0.511
tsm_2 0.697
tsm_3 0.378
tsm_4 0.726
3.0.10.2.1.4 Component analysis (reproducing full covariance matrix)
PC1
tsm_1 0.715
tsm_2 0.754
tsm_3 0.595
tsm_4 0.769
3.0.10.2.1.5 Item descriptives
mean median var sd IQR se min q1 q3 max skew kurt dip n NA valid
tsm_1 1.8691 2 0.749 0.8654 1 0.0377 1 1 3 4 0.6975 -0.3215 0.1888 527 0 527
tsm_2 2.0455 2 0.8991 0.9482 2 0.0413 1 1 3 4 0.5134 -0.7183 0.1708 527 0 527
tsm_3 2.0455 2 0.9447 0.972 2 0.0423 1 1 3 4 0.6195 -0.6006 0.1708 527 0 527
tsm_4 2.2903 2 1.0201 1.01 2 0.044 1 1 3 4 0.2274 -1.0523 0.1347 527 0 527
3.0.10.2.1.6 Scattermatrix
Scatterplot

Scatterplot

3.0.10.3 Summary statistics

name label data_type value_labels n_missing complete_rate min median max mean sd n_value_labels hist
tsm_1 The insights from the text are arbitrary. haven_labelled 1. fully disagree,
2. [empty 1],
3. [empty 2],
4. fully agree		 0 1 1 2 4 1.869070 0.8654389 4 ▇▁▇▁▁▃▁▁
tsm_2 The knowledge contained in the text cannot be generalized to other situations at all. haven_labelled 1. fully disagree,
2. [empty 1],
3. [empty 2],
4. fully agree		 0 1 1 2 4 2.045541 0.9481892 4 ▇▁▇▁▁▅▁▂
tsm_3 The opposite of the knowledge formulated in the text would be equally right/wrong. haven_labelled 1. fully disagree,
2. [empty 1],
3. [empty 2],
4. fully agree		 0 1 1 2 4 2.045541 0.9719517 4 ▇▁▇▁▁▃▁▂
tsm_4 The knowledge formulated in the text cannot claim validity for other situations. haven_labelled 1. fully disagree,
2. [empty 1],
3. [empty 2],
4. fully agree		 0 1 1 2 4 2.290323 1.0100076 4 ▆▁▇▁▁▆▁▃

3.0.11 Scale: tch

3.0.11.1 Overview

Reliability: ωtotal [95% CI] = 0.84 [0.82;0.86].

Missing: 0.

Likert plot of scale tch items

Likert plot of scale tch items

Distribution of scale tch

Distribution of scale tch

3.0.11.2 Reliability details

3.0.11.2.1 Scale diagnosis
3.0.11.2.1.1 Reliability (internal consistency) estimates
3.0.11.2.1.1 Scale structure
3.0.11.2.1.1 Information about this scale
Dataframe: res$dat
Items: tch_1, tch_2, tch_3, tch_4 & tch_5
Observations: 527
Positive correlations: 10
Number of correlations: 10
Percentage positive correlations: 100
3.0.11.2.1.1 Estimates assuming interval level
Omega (total): 0.84
Omega (hierarchical): 0.52
Revelle’s Omega (total): 0.86
Greatest Lower Bound (GLB): 0.85
Coefficient H: 0.88
Coefficient Alpha: 0.80

3.0.11.2.1.1 Confidence intervals

Omega (total): [0.82; 0.86]
Coefficient Alpha: [0; 0.14]

(Estimates assuming ordinal level not computed, as at least one item seems to have more than 8 levels; the highest number of distinct levels is 5 and the highest range is 1004. This last number needs to be lower than 9 for the polychoric function to work. If this is unexpected, you may want to check for outliers.)

Note: the normal point estimate and confidence interval for omega are based on the procedure suggested by Dunn, Baguley & Brunsden (2013) using the MBESS function ci.reliability, whereas the psych package point estimate was suggested in Revelle & Zinbarg (2008). See the help (‘?ufs::scaleStructure’) for more information.

3.0.11.2.1.2 Eigen values

2.801, 1.014, 0.575, 0.326 & 0.284

3.0.11.2.1.3 Factor analysis (reproducing only shared variance)
ML2 ML1
tch_1 0.251 0.337
tch_2 0.817 0.039
tch_3 0.870 -0.036
tch_4 -0.006 1.000
tch_5 0.810 0.001
3.0.11.2.1.4 Component analysis (reproducing full covariance matrix)
TC1 TC2
tch_1 0.011 0.837
tch_2 0.871 0.043
tch_3 0.908 -0.018
tch_4 -0.009 0.852
tch_5 0.894 -0.019
3.0.11.2.1.5 Item descriptives
mean median var sd IQR se min q1 q3 max skew kurt dip n NA valid
tch_1 -107.7837 2 98412.8885 313.7083 3 13.6653 -999 1 4 4 -2.499 4.2614 0.1262 527 0 527
tch_2 -265.5503 1 196879.3848 443.7109 1003 19.3284 -999 -999 4 4 -1.0532 -0.8942 0.1338 527 0 527
tch_3 -278.8501 1 202906.5193 450.4515 1003 19.622 -999 -999 4 4 -0.9781 -1.0472 0.1404 527 0 527
tch_4 -111.5123 2 101388.68 318.4159 3 13.8704 -999 1 4 4 -2.4383 3.9605 0.1319 527 0 527
tch_5 -276.9488 1 202067.9194 449.5197 1002 19.5814 -999 -999 4 4 -0.9886 -1.0265 0.1395 527 0 527
3.0.11.2.1.6 Scattermatrix
Scatterplot

Scatterplot

3.0.11.3 Summary statistics

name label data_type value_labels n_missing complete_rate min median max mean sd n_value_labels hist
tch_1 It is transparent which data form the basis of the study. haven_labelled 1. fully disagree,
2. [empty 1],
3. [empty 2],
4. fully agree,
-999. don’t know		 0 1 -999 2 4 -107.7837 313.7083 5 ▁▁▁▁▁▁▁▇
tch_2 Interested parties can have a close look at the questionnaire of the described study. haven_labelled 1. fully disagree,
2. [empty 1],
3. [empty 2],
4. fully agree,
-999. don’t know		 0 1 -999 1 4 -265.5503 443.7109 5 ▃▁▁▁▁▁▁▇
tch_3 The data collected in the study are publicly available. haven_labelled 1. fully disagree,
2. [empty 1],
3. [empty 2],
4. fully agree,
-999. don’t know		 0 1 -999 1 4 -278.8501 450.4515 5 ▃▁▁▁▁▁▁▇
tch_4 The authors make it easy for other researchers to understand their statistical analyses. haven_labelled 1. fully disagree,
2. [empty 1],
3. [empty 2],
4. fully agree,
-999. don’t know		 0 1 -999 2 4 -111.5123 318.4159 5 ▁▁▁▁▁▁▁▇
tch_5 If other researchers want to repeat the study, they have easy access to the questionnaires used. haven_labelled 1. fully disagree,
2. [empty 1],
3. [empty 2],
4. fully agree,
-999. don’t know		 0 1 -999 1 4 -276.9488 449.5197 5 ▃▁▁▁▁▁▁▇

3.1 Missingness report

3.2 Codebook table

JSON-LD metadata The following JSON-LD can be found by search engines, if you share this codebook publicly on the web.

{
  "name": "Journals’ Open Science Badges Foster Trust in Scientists. Study 1: Undergraduates Sample.",
  "description": "Code book to manuscript\n\n\n## Table of variables\nThis table contains variable names, labels, and number of missing values.\nSee the complete codebook for more.\n\n[truncated]\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.9.2).",
  "identifier": "",
  "datePublished": "2021-07-12",
  "creator": {
    "@type": "Person",
    "givenName": "Schneider",
    "familyName": "Jürgen",
    "email": "juergen.schneider@uni-tuebingen.de",
    "affiliation": {
      "@type": "Organization",
      "name": "University of Tübingen"
    }
  },
  "citation": "Schneider, J. (2021). Journals’ Open Science Badges Foster Trust in Scientists. Codebook of Study 1: Undergraduates sample",
  "keywords": ["session", "treat", "exp_1", "exp_2", "exp_3", "exp_4", "exp_5", "exp_6", "int_1", "int_2", "int_3", "int_4", "ben_1", "ben_2", "ben_3", "ben_4", "meas_rep", "tsm_1", "tsm_2", "tsm_3", "tsm_4", "age", "semester", "sex", "tch_1", "tch_2", "tch_3", "tch_4", "tch_5", "exp", "int", "ben", "tsm", "tch"],
  "@context": "http://schema.org/",
  "@type": "Dataset",
  "variableMeasured": [
    {
      "name": "session",
      "@type": "propertyValue"
    },
    {
      "name": "treat",
      "description": "Treatment condition, the participant was assigned to.",
      "value": "GB. Greyed out badges (no adherence to Open Science standards),\nCC. Control Condition (no badges),\nCB. Colored out badges (adherence to Open Science standards)",
      "maxValue": "GB",
      "minValue": "CB",
      "@type": "propertyValue"
    },
    {
      "name": "exp_1",
      "description": "competent - incompetent",
      "value": "1. 1,\n2. 2,\n3. 3,\n4. 4,\n5. 5,\n6. 6,\n7. 7",
      "maxValue": 7,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "exp_2",
      "description": "intelligent - unintelligent",
      "value": "1. 1,\n2. 2,\n3. 3,\n4. 4,\n5. 5,\n6. 6,\n7. 7",
      "maxValue": 7,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "exp_3",
      "description": "well educated -\tpoorly educated",
      "value": "1. 1,\n2. 2,\n3. 3,\n4. 4,\n5. 5,\n6. 6,\n7. 7",
      "maxValue": 7,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "exp_4",
      "description": "professional - unprofessional",
      "value": "1. 1,\n2. 2,\n3. 3,\n4. 4,\n5. 5,\n6. 6,\n7. 7",
      "maxValue": 7,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "exp_5",
      "description": "experienced - inexperienced",
      "value": "1. 1,\n2. 2,\n3. 3,\n4. 4,\n5. 5,\n6. 6,\n7. 7",
      "maxValue": 7,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "exp_6",
      "description": "qualified - unqualified",
      "value": "1. 1,\n2. 2,\n3. 3,\n4. 4,\n5. 5,\n6. 6,\n7. 7",
      "maxValue": 7,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "int_1",
      "description": "sincere - insincere",
      "value": "1. 1,\n2. 2,\n3. 3,\n4. 4,\n5. 5,\n6. 6,\n7. 7",
      "maxValue": 7,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "int_2",
      "description": "honest - dishonest",
      "value": "1. 1,\n2. 2,\n3. 3,\n4. 4,\n5. 5,\n6. 6,\n7. 7",
      "maxValue": 7,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "int_3",
      "description": "just - unjust",
      "value": "1. 1,\n2. 2,\n3. 3,\n4. 4,\n5. 5,\n6. 6,\n7. 7",
      "maxValue": 7,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "int_4",
      "description": "fair - unfair",
      "value": "1. 1,\n2. 2,\n3. 3,\n4. 4,\n5. 5,\n6. 6,\n7. 7",
      "maxValue": 7,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "ben_1",
      "description": "moral - immoral",
      "value": "1. 1,\n2. 2,\n3. 3,\n4. 4,\n5. 5,\n6. 6,\n7. 7",
      "maxValue": 7,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "ben_2",
      "description": "ethical - unethical",
      "value": "1. 1,\n2. 2,\n3. 3,\n4. 4,\n5. 5,\n6. 6,\n7. 7",
      "maxValue": 7,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "ben_3",
      "description": "responsible - irresponsible",
      "value": "1. 1,\n2. 2,\n3. 3,\n4. 4,\n5. 5,\n6. 6,\n7. 7",
      "maxValue": 7,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "ben_4",
      "description": "considerate - inconsiderate",
      "value": "1. 1,\n2. 2,\n3. 3,\n4. 4,\n5. 5,\n6. 6,\n7. 7",
      "maxValue": 7,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "meas_rep",
      "description": "Measurement repetition, first and second measurement",
      "value": "1. first measurement,\n2. second measurement",
      "maxValue": 2,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "tsm_1",
      "description": "The insights from the text are arbitrary.",
      "value": "1. fully disagree,\n2. [empty 1],\n3. [empty 2],\n4. fully agree",
      "maxValue": 4,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "tsm_2",
      "description": "The knowledge contained in the text cannot be generalized to other situations at all.",
      "value": "1. fully disagree,\n2. [empty 1],\n3. [empty 2],\n4. fully agree",
      "maxValue": 4,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "tsm_3",
      "description": "The opposite of the knowledge formulated in the text would be equally right/wrong.",
      "value": "1. fully disagree,\n2. [empty 1],\n3. [empty 2],\n4. fully agree",
      "maxValue": 4,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "tsm_4",
      "description": "The knowledge formulated in the text cannot claim validity for other situations.",
      "value": "1. fully disagree,\n2. [empty 1],\n3. [empty 2],\n4. fully agree",
      "maxValue": 4,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "age",
      "description": "Age",
      "@type": "propertyValue"
    },
    {
      "name": "semester",
      "description": "How many semesters of teaching/education-related coursework are you in (counting bachelor's degrees)?",
      "@type": "propertyValue"
    },
    {
      "name": "sex",
      "description": "Sex",
      "value": "1. female,\n2. male,\n3. other",
      "maxValue": 3,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "tch_1",
      "description": "It is transparent which data form the basis of the study.",
      "value": "1. fully disagree,\n2. [empty 1],\n3. [empty 2],\n4. fully agree,\n-999. don't know",
      "maxValue": 4,
      "minValue": -999,
      "@type": "propertyValue"
    },
    {
      "name": "tch_2",
      "description": "Interested parties can have a close look at the questionnaire of the described study.",
      "value": "1. fully disagree,\n2. [empty 1],\n3. [empty 2],\n4. fully agree,\n-999. don't know",
      "maxValue": 4,
      "minValue": -999,
      "@type": "propertyValue"
    },
    {
      "name": "tch_3",
      "description": "The data collected in the study are publicly available.",
      "value": "1. fully disagree,\n2. [empty 1],\n3. [empty 2],\n4. fully agree,\n-999. don't know",
      "maxValue": 4,
      "minValue": -999,
      "@type": "propertyValue"
    },
    {
      "name": "tch_4",
      "description": "The authors make it easy for other researchers to understand their statistical analyses.",
      "value": "1. fully disagree,\n2. [empty 1],\n3. [empty 2],\n4. fully agree,\n-999. don't know",
      "maxValue": 4,
      "minValue": -999,
      "@type": "propertyValue"
    },
    {
      "name": "tch_5",
      "description": "If other researchers want to repeat the study, they have easy access to the questionnaires used.",
      "value": "1. fully disagree,\n2. [empty 1],\n3. [empty 2],\n4. fully agree,\n-999. don't know",
      "maxValue": 4,
      "minValue": -999,
      "@type": "propertyValue"
    },
    {
      "name": "exp",
      "description": "aggregate of 6 exp items",
      "@type": "propertyValue"
    },
    {
      "name": "int",
      "description": "aggregate of 4 int items",
      "@type": "propertyValue"
    },
    {
      "name": "ben",
      "description": "aggregate of 4 ben items",
      "@type": "propertyValue"
    },
    {
      "name": "tsm",
      "description": "aggregate of 4 tsm items",
      "@type": "propertyValue"
    },
    {
      "name": "tch",
      "description": "aggregate of 5 tch items",
      "@type": "propertyValue"
    }
  ]
}`