| Title: | Open Datasets from Meta-Analyses in Psychology Research |
|---|---|
| Description: | Data and examples from meta-analyses in psychology research. |
| Authors: | Josue E. Rodriguez [aut, cre],
Donald Williams [aut],
Lukas Wallrich [aut] |
| Maintainer: | Josue E. Rodriguez <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.0.2 |
| Built: | 2024-11-16 04:34:23 UTC |
| Source: | https://github.com/josue-rodriguez/psymetadata |
Results from 21 studies, including 85 effect sizes (fisher-z), on the effect of out-Group entitativity and prejudice (Agadullina and Lovakov 2018).
data("agadullina2018")data("agadullina2018")
A data frame with 85 rows and 9 variables:
es_id: effect size id
study_id: study id
author: study author
pub_year: year of publication
n: sample size
design: within or between subjects design
ent_alpha: reliability of the entitativity measure
yi: effect size (fisher-z)
vi: sampling variance (SE^2)
Further details can be found at https://osf.io/8dw5y/.
Agadullina ER, Lovakov AV (2018). “Are people more prejudiced towards groups that are perceived as coherent? A meta-analysis of the relationship between out-group entitativity and prejudice.” British Journal of Social Psychology, 57(4), 703–731.
Results from 48 studies, including 637 effect sizes (Hedge's g), on the effect of the Cogmed Working Memory Training program on cognitive and academic outcomes (Aksayli et al. 2019).
data(aksayli2019)data(aksayli2019)
A dataset with 637 rows and 15 variables.
study_id: unique id for study
es_id: unique id for effect size
yi: effect size in Hedge's g
vi: variance (SE^2)
ni: sample size
author: author of study
transfer: transfer type: near or far
test: type of working memory test?
allocation: whether participants were randomly assigned
comparison: active or non-active: whether the CWMT groups was compared to another cognitively demannding activity
baseline: standardized mean difference corrected for upward bias between exp. and control at pre-test assessment
age_group: whether participants were children (< 16 yrs), adults (17-55), or older adults (> 55)
age_mean_exp: mean age of experimental group
age_mean_control: mean age of control group
population: whether the participants were typical subjects not suffering from any clinical conditions
Aksayli ND, Sala G, Gobet F (2019). “The cognitive and academic benefits of Cogmed: A meta-analysis.” Educational Research Review, 27, 229–243.
Results from 332 studies, including 747 effect sizes in total (Fisher-z), on the relation between math anxiety and math achievement (Barroso et al. 2021).
data("barroso2021")data("barroso2021")
A data frame with 747 rows and 11 variables:
es_id: effect size id
study_id: study id, corresponding to the author variable.
author: study authors
pub_year: year of publication
continent: 1 = North America; 2 = South America; 3 = Europe; 4 = Asia;
5 = Africa; 6 = Oceania (Australia and New Zealand); -999 not included
grade: 1 = 1st - 2nd grade; 2 = 3rd - 5th grade; 3 = 6th - 8th grade;
4 = 9th - 12th; 5 = postsecondary school (undergraduate and graduate students);
6 = non-student adults
low_ability: low math ability. 1 = yes; 2 = no
teachers: 1 = teacher sample; 2 = not teacher sample
ni: sample size
yi: effect size (Fisher-z)
vi: sampling variance (SE^2)
Further details can be found at https://osf.io/5admx/.
Barroso C, Ganley CM, McGraw AL, Geer EA, Hart SA, Daucourt MC (2021). “A meta-analysis of the relation between math anxiety and math achievement.” Psychological Bulletin, 147(2), 134.
Results from 138 studies, including 274] effect sizes (Cohen's d), on the facial feedback hypothesis (Coles et al. 2019).
data(coles2019)data(coles2019)
A dataset with 286 rows and 13 variables.
study_id: Unique id for study
es_id: Unique id for effect size
yi: Effect size in Cohen's d
vi: Variance of effect size (SE^2)
title: Title of publication
year: Year of publication
file_drawer: Publication status
prop_women: Proportion of study that identified as women
video_know: Yes: Participants were told they were going to be recorded or the methodology stated that a video camera was placed within participant view.
No" Methodology stated that participants were unaware of video recording, that the video camera was hidden, or that there was not a video camera
stim: Type of stimuli
proc: Type of facial feedback manipulation
proc_aware: Whether participants were aware of the facial feedback manipulation
w_v_b: Whether the study used a between- or within-subjects design
Coles NA, Larsen JT, Lench HC (2019). “A meta-analysis of the facial feedback literature: Effects of facial feedback on emotional experience are small and variable.” Psychological bulletin, 145(6), 610.
Results from 46 studies, including 89 effect sizes (r), on the specificity of future thinking in depression (Gamble et al. 2019)
data(gamble2019)data(gamble2019)
A data frame with 89 rows and 20 variables.
study_id: Unique id for study
samp_id: Unique id for each sample
es_id: Unique id for effect size
authors: Authors of study
yi: Effect size in r
vi: Variance of effect size
ni: Sample size of study
sex: Proportion of study that was female
age: Mean age of participants
dep_status: Clinical status of depression
comorbid_anx: Whehter comorbid with anxiety
emo_val: Emotional valence of simulations
macro_micro: Macro vs. micro specificity
cue_type: Cue type
spec_rated: Self- vs. researcher-rated specificity
dep_rated: Self- vs. researcher-rated depression
cat_dim: Categorical vs. dimensional designs
quality: Study quality rating
published: Published or not
mode: Mode or prospection
Gamble B, Moreau D, Tippett LJ, Addis DR (2019). “Specificity of future thinking in depression: A meta-analysis.” Perspectives on Psychological Science, 14(5), 816–834.
Results from 22 studies, including 67 effect sizes (SMD), on the effect of the color red on cognitive performance (Gnambs 2020).
data("gnambs2020")data("gnambs2020")
A data frame with 67 rows and 10 variables:
es_id: effect size id
study_id: study id
author: study author
pub_year: year of publication
country: country where experiment was conducted
color: control color
n: sample size
design: within or between subjects design
yi: effect size (standardized mean difference)
vi: sampling variance (SE^2)
Further details can be found at https://psyarxiv.com/a4qdv/.
Gnambs T (2020). “Limited evidence for the effect of red color on cognitive performance: A meta-analysis.” Psychonomic bulletin & review, 27(6), 1374–1382.
Results from 150 studies, including 1194 effect sizes (Hedge's g), on the extent to which shared reading impacts language development (Lowe 2020).
data("lowe2020")data("lowe2020")
A data frame with 1194 rows and 20 variables:
pub_year: year of publication
pub_type: publication type
es_id: effect size id
study_id: study id
yi: effect size (Hedge's g)
vi: sampling variance (SE^2)
subsample: coding for independent subsamples within studies
participants: unique id for participant pairs
clusters: unique id for participant clusters
lab_group: unique id for research group
proficiency: whether sample consisted of emergent or balanced bilinguals
age: mean age of the sample
country: country of study
geo_area: geographic area of study
match: did the study use matched samples (0 = no, 1 = yes)
study_quality: summated study quality score
verbal_non_verbal: whether task was verbal, non-verbal, or both
outcome_task: name of task used
outcome_type: coded for incongruent, congruent, and neutral trials
sub_measure: coded for reaction time, accuracy, or other outcomes
Further details can be found at https://osf.io/jv7wt/
Lowe C (2020). “The bilingual advantage in children: a meta-analytic review.” PsyArXiv.
Results from 158 studies, including 1246 effect sizes (r), on the relationship between emotional intelligence (EI) and academic performance (MacCann et al. 2020).
data("maccann2020")data("maccann2020")
A data frame with 1246 rows and 19 variables:
study_id: unique id of study
sample_id: unique id of sample
es_id: unique id of effect size
author: author of study
pub_year: year of study publication
yi: effect size (r)
vi: sampling variance for effect (SE^2)
pub_type: publication type (0 = journal article, 1 = dissertation, 2 = conference proceedings, 3 = unpublished data)
n: number of participants contributing to effect size
ed_level1: level of education of the sample at the time of data collection (0 = primary, 1 = secondary, 2 = tertiary, 3 = mixed)
ed_level2: level of education based on the type of academic achievement reported (0 = primary, 1 = secondary, 2 = tertiary, 3 = mixed)
country: country where the participants in the studies were from
perc_white: percentage of the sample categorized as "white" (USA samples only)
age: mean age of the sample
perc_female: percentage of sample who are female
ei_construct: the EI facet or construct represented (0 = overall ei, 2 = perception, 2 = facilitation, 3 = understanding, 4 = management, 5 = intrapersonal, 6 = interpersonal, 7 = stress management, 8 = adaptability, 9 = general mood)
ei_stream: the stream (or type) of EI instrument used (1 = maximum-performance ability tests, 2 = rating scales based on ability models, 3 = other broader models of EI that include non-ability constructs)
ei_measure: the test of EI used (1.1 = MSCEIT, 1.2 = MEIS, 1.3 = DANVA, 1.4 = STEU, 1.5 = STEM, 2.1 = SUEIT, 2.2 = SSRI, 2.3 = SREIT, 2.4 = TMMS, 2.5 = WLEIS, 3.1 = EQi, 3.2 = TEIQue, 3.3 = ESAP)
subject: subject area of the academic performance (0 = general, 1 = verbal/language arts, 2 = math, 3 = science, 4 = social studies, 5 = foreign language, 6 = psychology, 7 = medicine, 8 = engineering, 9 = physical education, 10 = art)
humanities: subject area of the acadmeic performance, categorized as sciences versus humanities (0 = general, 1 = math and sciences, 2 = humanities and verbal abilities)
achievement_type: type of achievement (0 = course grade, 1 = standardized test)
Further details can be found at https://osf.io/hnmy4/
MacCann C, Jiang Y, Brown LE, Double KS, Bucich M, Minbashian A (2020). “Emotional intelligence predicts academic performance: A meta-analysis.” Psychological Bulletin, 146(2), 150.
Results from 431 studies, including 1268 effect sizes (Hedge's g), on age differences in executive functioning (Maldonado et al. 2020).
data("maldonado2020")data("maldonado2020")
A data frame with 1268 rows and 13 variables:
es_id: effect size id
study_id: study id
author: study authors
domain: executive functioning domain
n1: sample size in younger group
n2: sample size in older group
n_total: total sample size (n1 + n2)
mean_age1: mean age of younger group
mean_age2: mean age of older group
miyake: framework put forward by Mijake and colleagues
task: cognitive task administered
yi: effect size (Hedge's g)
vi: sampling variance (SE^2)
Further details can be found at https://osf.io/bcywg/.
Maldonado T, Orr JM, Goen JR, Bernard JA (2020). “Age differences in the subcomponents of executive functioning.” The Journals of Gerontology: Series B, 75(6), e31–e55.
Meta-analytic data collected from the ManyBabies Consortium, including 108 effect sizes, aimed at assessing the overall replicability of theoretically-important phenomenon and examing the methodological, situational, cultural, and developmental moderators on infant's preference for infant-directed speech (IDS) over adult-directed speech (ADS)
data(manybabies2020)data(manybabies2020)
A dataset with 108 rows and 8 variables.
lab: name of the lab which observed the effect
es_id: unique id for each effect size
yi: observed effect sizes, expressed as Cohen's d
vi: sampling variance (SE^2)
ni: sample size for each observed effect
age_group: age category for each observed effect
method: method used for each observed effect
nae: whether North American English stimuli were used
age_mo: mean age of babies (in months) for each observed effect
age_mo_centered: mean-centered age of babies (in months) for each observed effect
https://github.com/manybabies/mb1-analysis-public
ManyBabiesConsortium (2020). “Quantifying sources of variability in infancy research using the infant-directed-speech preference.” Advances in Methods and Practices in Psychological Science, 3(1), 24–52.
A subset of the data collected in the Many Labs 2 project which conducted replications of 28 classic and contemporary findings in psychology. The study examined the extent to which variability in replication success can be attributed to the study sample.
data(manylabs2018)data(manylabs2018)
A dataset with 1,414 rows and 23 variables.
lab: The lab which conducted the replication
es_id: Unique id for each effect size
yi_r: A numeric indicating the observed effect size, expressed in r
vi_r: A numeric indicating the variance on the observed effect size, expressed in r
yi_d: A numeric indicating the observed effect size, expressed in Cohen's d
vi_d: A numeric indicating the variance on the observed effect size, expressed in Cohen's d
ni: A numeric indicating the total sample size for the observed effect size
country: Country where the sample was collected
weird: Dummy variable encoding whether a country was classified as WEIRD; 0 = non-WEIRD, 1 = WEIRD
western: Dummy variable encoding a team judgment whether country was considered "western"
educated: Education score as measured by the Education Index
industrialized: Industrialization score as measured in the 2016 Industrial Development Report
rich: Dummy variable encoding whether a country is developed according to the 2014 World Economic Situation and Prospects Report; 0 = emerging or in transition, 1 = developed
democratic: The quality democracy in the corresponding country according to the 2015 Democracy Ranking Report. Higher scores indicate higher quality.
mean_weird_score: The arithmetic mean of the weird, western, educated, industrialized, and rich variables
online: Whether the study was replicated in a lab or online
analysis: Unique id for replicated study
Klein, R. A., et al. (2018). Many Labs 2: Investigating variation in replicability across samples and settings. Advances in Methods and Practices in Psychological Science, 1(4), 443-490. (APS)
Results from 54 studies, including 316 effect sizes (Hedge's g), on the extent to which shared reading impacts language development (Noble et al. 2019).
data("noble2019")data("noble2019")
A data frame with 316 rows and 13 variables:
es_id: effect size id
study_id: study id
author: study author
measure: measure used in the study
age: age of participants, grouped into categories.
ses: socio-economic status
experimenter: who administered the test (pa)
duratio: number of weeks
dialogic_reading: dialogic reading
follow_up: follow up
n: sample size
yi: effect size (Hedge's g)
vi: sampling variance (SE^2)
Further details can be found at https://osf.io/34xyw/
Noble C, Sala G, Peter M, Lingwood J, Rowland C, Gobet F, Pine J (2019). “The impact of shared book reading on children's language skills: A meta-analysis.” Educational Research Review, 28, 100290.
Data resulting from 131 meta-analyses, including 2443 effect sizes (fisher-z), on different areas of intelligence research (Nuijten et al. 2020)
data(nuijten2020)data(nuijten2020)
A dataset with 2443 rows and 14 variables.
study_id: Unique id for study
effect_id: Unique id for effect size
authors: identifier for the primary study within a meta-analysis based o
the first author of the study or the sample used
year: year in which the primary study was reported
yi: original effect size converted to a Fishers z value
vi: variance around the z value in yi
ni: total sample size of the primary study
es: effect size as indicated in type_es
se: standard errors of the effect size
type_es: the type of effect size extracted from the meta-analysis; 1 = r transformed to Fishers z, 2 = Hedge's g, 3 = log odds ratio
4 = Cohen's d, 5 = Hazard Ratio, 6, 7, 8 = other
type: type of IQ research summarized by the meta-analysis; 1 = Correlational, 2 = Group_differences
3 = Experiments/Interventions, 4 = Toxicology, 5 = (Behavior) Genetics
citations: number of times the primary study was cited
countrycode: country in which the first author of a primary study was situated at the time of publication
jrnl_impact: impact factor in 2014 of the journal where the primary study was published
similarity: whether the primary study matched the research question of the meta-analysis; 0 = dissimilar, 1 = similar
Nuijten MB, van Assen MA, Augusteijn HE, Crompvoets EA, Wicherts JM (2020). “Effect sizes, power, and biases in intelligence research: A meta-meta-analysis.” Journal of Intelligence, 8(4), 36.
Results from 332 studies, including 1,555 effect sizes (Hedge's g), on whether skills learned from cognitive training generalize to other situations (Sala et al. 2019).
data(sala2019)data(sala2019)
A data frame with 1,555 rows and 10 variables:
* study_id: unique id for each meta-analysis
* es_id: unique id for each effect size
* yi: the observed effect size, expressed in Hedge's g
* vi: the variance of the observed effect size
* ni: the total sample size for the observed effect size in the meta-analysis
* author: author of study
* comparison: type of control group ("Active" or "Non-active")
* age: Age group used in study ("adults", "LD children", "TD children", "old", or "children")
* test: test used in study
* model: indicator for which model the study is used (see paper for details)
Sala G, Aksayli ND, Tatlidil KS, Tatsumi T, Gondo Y, Gobet F, Zwaan R, Verkoeijen P (2019). “Near and far transfer in cognitive training: A second-order meta-analysis.” Collabra: Psychology, 5(1).
Results from 62 studies, including 75 effect sizes (Hedge's g) on the effect of transcranial direct current stimulation (tDCS) in inhibitory control (Schroeder et al. 2020).
data("schroeder2020")data("schroeder2020")
A data frame with 75 rows and 13 variables:
study_id: unique id for study
es_id: unique id for effect size
yi: effect size (Hedge's g)
vi: sampling variance for effect size
study_design: study design ("between-subjects" or "within")
control: control condition ("active control", "no tDCS", or "sham")
blinding: blinding strategy ("no blinding", "not reported", "success")
task: task used in study: go/no-go task ("GNG) or stop-signal task ("SST")
population: population of study ("ADHD", "healthy" or "other patients")
stimulation: tDCS polarity ("anodal" or "cathodal")
intensity: tDCS intensity (1 mA, 1.5 mA, or 2 mA)
target_electrode_placement: target electrode placement
return_electrode_placement: return electrode placement
timing: timing of stimulation ("online" or "offline")
Further details can be found at https://osf.io/mrxhe/
Schroeder PA, Schwippel T, Wolz I, Svaldi J (2020). “Meta-analysis of the effects of transcranial direct current stimulation on inhibitory control.” Brain Stimulation.
Results from 26 studies, including 99 effect sizes (Hedge's g), on inhibition, shifting, and attention in people with intellectual disability compared to people matched on mental age (Spaniol and Danielsson 2019).
data("spaniol2020")data("spaniol2020")
A data frame with 99 rows and 11 variables:
author: author of study
study_id: unique id for study
study_year: year of publication
es_id: unique id for effect size
yi: effect size in (Hedge's g)
vi: sampling variance for effect size (SE^2)
group_id: experimental intellectual disability group. one of: non-specific cause ("NSID"), Fragile X syndrome ("FXS"), Down syndrome ("DS"), or Williams syndrome ("WS")
ef_type: task type ("inhibition", "updating", "shifting", "fluency", "attention", or "other")
ef_component: executive function component ("inhibition", "shifting" or "attention")
domain: domain of executive function component ("verbal", "visuospatial", or "other")
test: test used to measure executive function
Further details can be found at https://psyarxiv.com/gjqcs/
Spaniol M, Danielsson H (2019). “A Meta-analysis of the Executive Functions Inhibition, Shifting and Updating in Intellectual Disabilities.” PsyArXiv.
Results from 35 studies, including 76 effect sizes (r), on learning goal orientation and perfromance adaptation (Stasielowicz 2019).
data("stasielowicz2019a")data("stasielowicz2019a")
A data frame with 76 rows and 24 variables:
study_id: unique id for study
es_id: unique id for effect size
author: author of study
pub_year: publication year
pub_type: publication type (0 = journal article, 2 = book chapter, 3 = dissertation, 4 = master's thesis, 5 = bachelor's thesis, 6 = conference proceedings, 7 = report, 8 = other)
peer_review: whethere publication was peer-reviewed (0 = no, 1 = yes)
n: sample size of effect size
yi: effect size (r)
vi: sampling variance of effect size (SE^2)
adapt_measures: assessment method(s) of adaptation used in the study (1 = self-report, 2 = other people, 3 = objective, 4 = mixed)
adapt_method: assessment method of adaption (0 = subjective ratings, 1 = objective scores)
adapt_method_specific: specific assessment method of adaptation used for the particular effect size (1 = self-report, 2 = other people, 3 = objective)
go_measure: instrument used to assess goal orientation
financ_support: financial support (0 = no, 1 = yes)
age: mean age of sample
age_imputed: mean age of sample (imputed)
sex: sex of sample (1 = female sample, 2 = male sample, 3 = mixed sample)
perc_men: proportion of men in the sample
country: country where sampled was collected
sample: sample type (1 = students, 2 = employees, 3 = manager, 4 = mixed, 5 = other)
level: level (1 = individuals, 2 = team)
complexity_component: component complexity of the task (0 = relatively low, 1 = relatively high)
complexity_coordinative: coordinative complexity of the task (0 = relatively low, 1 = relatively high)
complexity_dynamic: which complexity aspect changed while completing task (0 = neither component nor coordinative, 1 = only component, 2 = only coordinative, 3 = both component and coordinative)
Further details can be found at https://osf.io/szfwx/
Stasielowicz L (2019). “Goal orientation and performance adaptation: A meta-analysis.” Journal of Research in Personality, 82, 103847.
Results from 28 studies, including 86 effect sizes (r), on performance goal orientation and performance adaptation (Stasielowicz 2019).
data("stasielowicz2019b")data("stasielowicz2019b")
A data frame with 86 rows and 25 variables:
study_id: unique id for study
es_id: unique id for effect size
author: author of study
pub_year: publication year
pub_type: publication type (0 = journal article, 2 = book chapter, 3 = dissertation, 4 = master's thesis, 5 = bachelor's thesis, 6 = conference proceedings, 7 = report, 8 = other)
peer_review: whethere publication was peer-reviewed (0 = no, 1 = yes)
n: sample size of effect size
yi: effect size (r)
vi: sampling variance of effect size (SE^2)
pgo_type: the performance goal orientation that was assessed ("avoid", "prove", or "global")
adapt_measures: assessment method(s) of adaptation used in the study (1 = self-report, 2 = other people, 3 = objective, 4 = mixed)
adapt_method: assessment method of adaption (0 = subjective ratings, 1 = objective scores)
adapt_method_specific: specific assessment method of adaptation used for the particular effect size (1 = self-report, 2 = other people, 3 = objective)
go_measure: instrument used to assess goal orientation
financ_support: financial support (0 = no, 1 = yes)
age: mean age of sample
age_imputed: mean age of sample (imputed)
sex: sex of sample (1 = female sample, 2 = male sample, 3 = mixed sample)
perc_men: proportion of men in the sample
country: country where sampled was collected
sample: sample type (1 = students, 2 = employees, 3 = manager, 4 = mixed, 5 = other)
level: level (1 = individuals, 2 = team)
complexity_component: component complexity of the task (0 = relatively low, 1 = relatively high)
complexity_coordinative: coordinative complexity of the task (0 = relatively low, 1 = relatively high)
complexity_dynamic: which complexity aspect changed while completing task (0 = neither component nor coordinative, 1 = only component, 2 = only coordinative, 3 = both component and coordinative)
Further details can be found at https://osf.io/szfwx/
Stasielowicz L (2019). “Goal orientation and performance adaptation: A meta-analysis.” Journal of Research in Personality, 82, 103847.
Results from 47 independent samples, including 133 effect sizes (r), on the role of individual differences in cognitive abilities in the context of performance adaption (Stasielowicz 2020).
data("stasielowicz2020")data("stasielowicz2020")
A data frame with 133 rows and 23 variables:
id: unique id of study
effect_id: unique id of effect
author: author of study
pub_year: year of publication
pub_type: publication type (1 = journal article, 2 = book chapter, 3 = dissertation, 4 = master's thesis, 5 = bachelor's thesis, 6 = conference proceedings, 7 = report, 8 = other (eg., unpublished manuscript))
peer_review: whether publication was peer-reviewed (0 = no, 1 = yes)
n: sample size for effect size
yi: effect size (r)
vi: sampling variance (SE^2)
adapt_measures: assessment method(s) of adaptation used in the study (1 = self-report, 2 = other people, 3 = objective, 4 = mixed)
adapt_method: assessment method of adaption (1 = subjective ratings, 2 = objective scores)
adapt_method_specific: specific assessement method of adaptation used for the particular effect size (1 = self-report, 2 = other people, 3 = objective)
subj_adapt_definition: definition for subjective ratings of performance adaptations ("narrow" or "broad")
cog_abil_measure: measurement method of cognitive abilities
ca_measure: categorized measure of cognitive abilities ("general", "specific" or "ACT/SAT/GPA")
financ_support: financial support (e.g., grant; 0 = no, 1 = yes)
sex: sex (1 = female sample, 2 = male sample, 3 = mixed sample)
men_prop: proportion of men in sample
country: country of sample
sample: sample type (1 = students, 2 = employees, 3 = manager, 4 = mixed, 5 = other)
task: task used to measure performance adaptation ("simulation/video game", "SJT", or "Other")
complexity_component: coordinative complexity of the task (0 = relatively low, 1 = relatively high)
complexity_coordinative: dynamic complexity of the task (0 = relatively low, 1 = relatively high)
Further details can be found at https://psyarxiv.com/qu4t2/
Stasielowicz L (2020). “How important is cognitive ability when adapting to changes? A meta-analysis of the performance adaptation literature.” Personality and Individual Differences, 166, 110178.
Results from 128 studies, including 251 effect sizes (fisher-z), on the extent to which a leader is perceived to embody shared social identity (Steffens et al. 2021).
data("steffens2020")data("steffens2020")
A data frame with 251 rows and 10 variables:
es_id: effect size id
study_id: study id
author: study author
n: sample size
design: 0 = experimental; 1 = correlational
published: 0 = published; 1 = unpublished
proto_strength: 0 = ad-hoc; 1 = natural
target.leader: 0 = informal; 1 = formal
yi: effect size (fisher-z)
vi: sampling variance (SE^2)
Further details can be found at https://osf.io/y47er/
Steffens NK, Munt KA, van Knippenberg D, Platow MJ, Haslam SA (2021). “Advancing the social identity theory of leadership: A meta-analytic review of leader group prototypicality.” Organizational Psychology Review, 11(1), 35–72.
Results from 25 studies, including 96 effect sizes (Cohen's d), on suppression-induced forgetting (Stramaccia et al. 2020).
data("stramaccia2021")data("stramaccia2021")
A data frame with 96 rows and 15 variables:
study_id: unique id for study
group_id: unique id for group
es_id: unique id for effect size
yi: effect size (Cohen's d)
vi: sampling variance for effect size (SE^2)
pub_year: year of publication
instructions: type of instructions given to participants to prevent retrieval ("aided", "direct", or "unspecified")
stimuli: type of stimuli ("pictures" or "words")
valence: valence of stimulus material (for the suppress targets only). One of "mixed", "negative", "neutral" or "positive"
tnttime: duration for which cues remained on the screen during the think/no-think phase (see paper for details)
repetitions: the number of times that participants encountered each cue in the think/no-think phase (see paper for details)
n: sample size
dv:
cluster: clusters based on clinical and sub-clinical conditions ("anxiety", "control", "depression", "mixed", or "repression")
group: clinical population ("CP") or healthy control ("HC")
Further details can be found at https://osf.io/f89ur/
Stramaccia DF, Meyer A, Rischer KM, Fawcett JM, Benoit RG (2020). “Memory suppression and its deficiency in psychological disorders: A focused meta-analysis.” Journal of Experimental Psychology: General.
Results from 17 studies, including 100 effect sizes (Cohen's D) on the associations between mental health disorders of delinquent juveniles and subsequent delinquent behavior
data(wibbelink2017)data(wibbelink2017)
A data frame with 100 rows and 10 variables.
study_id: unique id for each study
es_id: unique id for each effect size
yi: observed effect sizes (Cohen's d)
vi: sampling variance (SE^2)
pstatpub: dummy variable encoding whether the study was published, 0 = unpublished, 1 = published
pstatnotpub: dummy variable encoding whether the study was unpublished, 0 = published, 1 = unpublished
typgen: dummy variable encoding the type of recidivism behavior 0 = not applicable, 1 = general
typovert: dummy variable encoding the type of recidivism behavior 0 = not applicable, 1 = overt
typcovert: dummy variable encoding the type of recidivism behavior 0 = not applicable, 1 = covert
pyear: the publication year of the study; mean-centered
The Quantitative Methods in Psychology
Wibbelink et al. (2017). A meta-analysis of the association between mental health disorders and juvenile recidivism. Aggression and Violent Behavior, 33, 78-90.