Studies on Out-Group Entitativity and Prejudice

Description

Results from 21 studies, including 85 effect sizes (fisher-z), on the effect of out-Group entitativity and prejudice (Agadullina and Lovakov 2018).

Usage

data("agadullina2018")

Format

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)

Details

Further details can be found at https://osf.io/8dw5y/.

References

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.

Studies on the cognitive and academic benefits of Cogmed

Description

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).

Usage

data(aksayli2019)

Format

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

Source

https://osf.io/jhavp/

References

Aksayli ND, Sala G, Gobet F (2019). “The cognitive and academic benefits of Cogmed: A meta-analysis.” Educational Research Review, 27, 229–243.

Studies on Math Anxiety and Math Achievement

Description

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).

Usage

data("barroso2021")

Format

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)

Details

Further details can be found at https://osf.io/5admx/.

References

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.

Studies on the Facial Feedback Literature

Description

Results from 138 studies, including 274] effect sizes (Cohen's d), on the facial feedback hypothesis (Coles et al. 2019).

Usage

data(coles2019)

Format

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

Source

https://osf.io/v8kxb/

References

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.

Meta-analytic data collected from studying on the specificity of future thinking in depression

Description

Results from 46 studies, including 89 effect sizes (r), on the specificity of future thinking in depression (Gamble et al. 2019)

Usage

data(gamble2019)

Format

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

Source

https://osf.io/5wjb2/

References

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.

Studies on the Color Red and Cognitive Performance

Description

Results from 22 studies, including 67 effect sizes (SMD), on the effect of the color red on cognitive performance (Gnambs 2020).

Usage

data("gnambs2020")

Format

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)

Details

Further details can be found at https://psyarxiv.com/a4qdv/.

References

Gnambs T (2020). “Limited evidence for the effect of red color on cognitive performance: A meta-analysis.” Psychonomic bulletin \& review, 27(6), 1374–1382.

Studies on the advantage of bilingualism in children: a meta-analytic review

Description

Results from 150 studies, including 1194 effect sizes (Hedge's g), on the extent to which shared reading impacts language development (Lowe 2020).

Usage

data("lowe2020")

Format

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

Details

Further details can be found at https://osf.io/jv7wt/

References

Lowe C (2020). “The bilingual advantage in children: a meta-analytic review.” PsyArXiv.

Studies examining whether student emotional intelligence is associated with academic performance

Description

Results from 158 studies, including 1246 effect sizes (r), on the relationship between emotional intelligence (EI) and academic performance (MacCann et al. 2020).

Usage

data("maccann2020")

Format

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)

Details

Further details can be found at https://osf.io/hnmy4/

References

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.

Studies on Age Differences in Executive Functioning

Description

Results from 431 studies, including 1268 effect sizes (Hedge's g), on age differences in executive functioning (Maldonado et al. 2020).

Usage

data("maldonado2020")

Format

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)

Details

Further details can be found at https://osf.io/bcywg/.

References

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.

Studies from ManyBabies 1: Infant-Directed Speech Preference.

Description

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) ⁠ ⁠

Usage

data(manybabies2020)

Format

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

Source

https://github.com/manybabies/mb1-analysis-public

References

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.

Studies from the Many Labs 2 project.

Description

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.

Usage

data(manylabs2018)

Format

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

Source

https://osf.io/ux3eh/

References

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)

Studies on Shared Reading and Language Development

Description

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).

Usage

data("noble2019")

Format

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)

Details

Further details can be found at https://osf.io/34xyw/

References

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 collected from meta-analyses on intelligence research

Description

Data resulting from 131 meta-analyses, including 2443 effect sizes (fisher-z), on different areas of intelligence research (Nuijten et al. 2020)

Usage

data(nuijten2020)

Format

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

Source

https://osf.io/fq5wp/

References

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.

Studies on the impact of working-memory training on near- and far-transfer measures

Description

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).

Usage

data(sala2019)

Format

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)

Source

https://osf.io/qk2vu/

References

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).

Studies on the effects of transcranial direct current stimulation on inhibitory control

Description

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).

Usage

data("schroeder2020")

Format

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")

Details

Further details can be found at https://osf.io/mrxhe/

References

Schroeder PA, Schwippel T, Wolz I, Svaldi J (2020). “Meta-analysis of the effects of transcranial direct current stimulation on inhibitory control.” Brain Stimulation.

Studies on Executive function components in intellectual disability

Description

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).

Usage

data("spaniol2020")

Format

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

Details

Further details can be found at https://psyarxiv.com/gjqcs/

References

Spaniol M, Danielsson H (2019). “A Meta-analysis of the Executive Functions Inhibition, Shifting and Updating in Intellectual Disabilities.” PsyArXiv.

Studies on the association between goal orientation and performance adaptation

Description

Results from 35 studies, including 76 effect sizes (r), on learning goal orientation and perfromance adaptation (Stasielowicz 2019).

Usage

data("stasielowicz2019a")

Format

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)

Details

Further details can be found at https://osf.io/szfwx/

References

Stasielowicz L (2019). “Goal orientation and performance adaptation: A meta-analysis.” Journal of Research in Personality, 82, 103847.

Studies on the association between goal orientation and performance adaptation

Description

Results from 28 studies, including 86 effect sizes (r), on performance goal orientation and performance adaptation (Stasielowicz 2019).

Usage

data("stasielowicz2019b")

Format

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)

Details

Further details can be found at https://osf.io/szfwx/

References

Stasielowicz L (2019). “Goal orientation and performance adaptation: A meta-analysis.” Journal of Research in Personality, 82, 103847.

Studies on the importance of cognitive ability in performance adaptation

Description

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).

Usage

data("stasielowicz2020")

Format

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)

Details

Further details can be found at https://psyarxiv.com/qu4t2/

References

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.

Studies on Social Identity Theory and Leadership: Leader Group Prototypicality

Description

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).

Usage

data("steffens2020")

Format

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)

Details

Further details can be found at https://osf.io/y47er/

References

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.

Studies on memory suppression and its deficiency in psychological disorders

Description

Results from 25 studies, including 96 effect sizes (Cohen's d), on suppression-induced forgetting (Stramaccia et al. 2020).

Usage

data("stramaccia2021")

Format

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")

Details

Further details can be found at https://osf.io/f89ur/

References

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.

Studies on juvenile recidivism

Description

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

Usage

data(wibbelink2017)

Format

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

Source

The Quantitative Methods in Psychology

References

Wibbelink et al. (2017). A meta-analysis of the association between mental health disorders and juvenile recidivism. Aggression and Violent Behavior, 33, 78-90.