| Type: | Package |
| Title: | Modern Psychometrics with R |
| Version: | 0.10-8 |
| Date: | 2020-06-17 |
| Maintainer: | Patrick Mair <mair@fas.harvard.edu> |
| Description: | Supplementary materials and datasets for the book "Modern Psychometrics With R" (Mair, 2018, Springer useR! series). |
| Imports: | graphics, stats |
| Depends: | R (≥ 3.0.2) |
| License: | GPL-2 |
| NeedsCompilation: | no |
| Packaged: | 2020-06-17 18:06:10 UTC; root |
| Author: | Patrick Mair [aut, cre] |
| Repository: | CRAN |
| Date/Publication: | 2020-06-18 06:17:05 UTC |
Adult Self-Transcendence Inventory
Description
The ASTI (Levenson et al., 2005) is a self-report scale measuring the complex target construct of wisdom. The items can be assigned to five dimensions: self-knowledge and integration (SI), peace of mind (PM), non-attachment (NA), self-transcendence (ST), and presence in the here-and-now and growth (PG).
Usage
data("ASTI")
Format
A data frame with 1215 individuals, 25 ASTI items (3 or 4 categories per items), and 2 covariates (gender, group). Item wordings:
ASTI1I often engage in quiet contemplation. (PM; reversed)
ASTI2I feel that my individual life is a part of a greater whole. (ST)
ASTI3I don't worry about other people's opinions of me. (NA)
ASTI4I feel a sense of belonging with both earlier and future generations. (ST)
ASTI5My peace of mind is not easily upset. (PM)
ASTI6My sense of well-being does not depend on a busy social life. (NA)
ASTI7I feel part of something greater than myself. (ST)
ASTI8My happiness is not dependent on other people and things. (NA; reversed)
ASTI9I do not become angry easily. (PM)
ASTI10I have a good sense of humor about myself. (SI; reversed)
ASTI11I find much joy in life. (PG; reversed)
ASTI12Material possessions don't mean much to me. (NA)
ASTI13I feel compassionate even toward people who have been unkind to me. (ST)
ASTI14I am not often fearful. (PG)
ASTI15I can learn a lot from others. (PG)
ASTI16I often have a sense of oneness with nature. (ST)
ASTI17I am able to accept my mortality. (PG)
ASTI18I often "lose myself" in what I am doing. (PG)
ASTI19I feel that I know myself. (SI; reversed)
ASTI20I am accepting of myself, including my faults. (SI; reversed)
ASTI21I am able to integrate the different aspects of my life. (SI; reversed)
ASTI22I can accept the impermanence of things. (PM; reversed)
ASTI23I have grown as a result of losses I have suffered. (PG; reversed)
ASTI24Whatever [good] I do for others, I do for myself. (ST; reversed)
ASTI25Whatever [bad] I do to others, I do to myself. (ST)
gendergender
groupstudent vs. non-student
Source
Levenson, M. R., Jennings, P. A., Aldwin, C. M., & Shiraishi, R. W. (2005). Self-transcendence: conceptualization and measurement. The International Journal of Aging and Human Development, 60, 127-143.
Koller I., Levenson, M. R. , & Glueck, J. (2017). What do you think you are measuring? A mixed-methods procedure for assessing the content validity of test items and theory-based scaling. Frontiers in Psychology, 8(126), 1-20.
Examples
data(ASTI)
si <- ASTI[ ,c(10,19,20,21)] ## self-knowledge and integration
pm <- ASTI[ ,c(1,5,9,22)] ## peace of mind
na <- ASTI[ ,c(3,6,8,12)] ## non-attachment
st <- ASTI[ ,c(2,4,7,13,16,24,25)] ## self-transcendence
pg <- ASTI[ ,c(11,14,15,17,18,23)] ## Presence in the here-and-now and growth
Preparanedness Backcountry Skiing
Description
Haegeli et al. (2012) studied high-risk cohorts in a complex and dynamic risk environment. This dataset contains four variables related to preparedness before going backcountry skiing. The variables with response categories are are 1) check avalanche danger information (check conditions on internet prior to leaving home; talk to ski patrol; check postings at gates or information kiosks at resort; do not check or Do not know), 2) discuss avalanche hazard in your group (all the time; 50% to 90% of time; 10% to 40% of time; never or solo traveller), 3) approach to decision making (dedicated leader or everybody contributes; person in front decides; everybody makes their own choices or solo traveller), and 4) use of avalanche safety gear (everybody carries beacon, shovel and probe; everybody carries beacon or beacon and shovel; some in group carry beacons; some in group have cell phones; no safety equipment is carried).
Usage
data("AvalanchePrep")
Format
A data frame with 1355 skiers and the following 4 items:
infoCheck avalanche danger information.
discussDiscuss avalanche hazard in your group.
gearUse of avalanche safety gear.
decisionApproach to decision making.
Source
Haegeli, P., Gunn, M., & Haider, W. (2012). Identifying a high-risk cohort in a complex and dynamic risk environment: Out-of-bounds skiing–an example from avalanche safety. Prevention Science, 13, 562-573.
Examples
data("AvalanchePrep")
str(AvalanchePrep)
Brief Sensation Seeking Scale Questions (BSSS-8)
Description
Haegeli et al. (2012) where interested in studying risk-taking behaviors of out-of-bounds skiers. The skiers where exposed to the “Brief Sensation Seeking Scale” (BSSS-8; Hoyle et al., 2002). It is a short 8-item scale with 5-point response categories. The scale has 4 subscales (with 2 items each): experience seeking (ES), boredom susceptibility (BS), thrill and adventure seeking (TAS) and disinhibition (DIS).
Usage
data("BSSS")
Format
A data frame with 1626 skiers and the following 8 items (5 response categories):
ExploreI would like to explore strange places.
RestlessI get restless when I spend too much time at home .
FrightningI like to do frightening things.
PartyI like wild parties.
TripI would like to take off on a trip with no pre-planned routes or timetables.
FriendsI prefer friends who are exciting and unpredictable.
BungeeI would like to do bungee jumping.
IllegalI would love to have new and exciting experiences, even if they are illegal.
Source
Hoyle, R. H., Stephenson, M. T., Palmgreen, P., Lorch, E. P., & Donohew, R. L. (2002). Reliability and validity of a brief measure of sensation. Personality and Individual Differences, 32, 401-414.
Haegeli, P., Gunn, M., & Haider, W. (2012). Identifying a high-risk cohort in a complex and dynamic risk environment: Out-of-bounds skiing–an example from avalanche safety. Prevention Science, 13, 562-573.
Examples
data("BSSS")
str(BSSS)
Generalized Prejudice Dataset
Description
Dataset from Bergh et al. (2016) where ethnic prejudice, sexism, sexual prejudice against gays and lesbians, and prejudice toward mentally people with disabilities are modeled as indicators of a generalized prejudice factor. It also includes indicators for agreeableness and openness. All variables are composite scores based on underlying 5-point questionnaire items.
Usage
data("Bergh")
Format
A data frame with 861 individuals, 10 composite scores, and gender:
EPEthnic prejudice
SPSexism
HPSexual prejudice against gays and lesbians
DPPrejudice toward mentally people with disabilities
A1Agreeableness indicator 1
A2Agreeableness indicator 2
A3Agreeableness indicator 3
O1Openness indicator 1
O2Openness indicator 2
O3Openness indicator 3
gendergender
Source
Bergh, R., Akrami, N., Sidanius, J., & Sibley, C. (2016) Is group membership necessary for understanding prejudice? A re-evaluation of generalized prejudice and its personality correlates. Journal of Personality and Social Psychology, 111, 367-395.
Examples
data("Bergh")
str(Bergh)
Brain Size and Intelligence
Description
Willerman et al. (1991) conducted their study at a large southwestern university. They selected a sample of 40 right-handed Anglo introductory psychology students who had indicated no history of alcoholism, unconsciousness, brain damage, epilepsy, or heart disease. These subjects were drawn from a larger pool of introductory psychology students with total Scholastic Aptitude Test Scores higher than 1350 or lower than 940 who had agreed to satisfy a course requirement by allowing the administration of four subtests (Vocabulary, Similarities, Block Design, and Picture Completion) of the Wechsler (1981) Adult Intelligence Scale-Revised. With prior approval of the University's research review board, students selected for MRI were required to obtain prorated full-scale IQs of greater than 130 or less than 103, and were equally divided by sex and IQ classification.
Usage
data("BrainIQ")
Format
A data frame with 40 individuals and the following 7 variables.
GenderParticipant's gender.
FSIQFull Scale IQ.
VIQVerbal IQ.
PIQPerformance IQ.
WeightBody weight.
HeightBody height.
MRI_CountMRI pixel count (brain size).
Source
Willerman, L., Schultz, R., Rutledge, J. N., & Bigler, E. (1991). In vivo brain size and intelligence. Intelligence, 15, 223-228.
Examples
data(BrainIQ)
str(BrainIQ)
Children's Empathic Attitudes Questionnaire (CEAQ)
Description
The CEAQ (Funk et al., 2008) is a scale to measure empathy of late elementary and middle-school aged children.
Usage
data("CEAQ")
Format
A data frame with 208 children, 16 CEAQ items and 3 covariates (age, grade, gender): Item wordings:
ceaq1When I'm mean to someone, I usually feel bad about it later.
ceaq2I'm happy when the teacher says my friend did a good job.
ceaq3I would get upset if I saw someone hurt an animal.
ceaq4I understand how other kids feel.
ceaq5I would feel bad if my mom's friend got sick.
ceaq6Other people's problems really bother me.
ceaq7I feel happy when my friend gets a good grade.
ceaq8When I see a kid who is upset it really bothers me.
ceaq9I would feel bad if the kid sitting next to me got in trouble.
ceaq10It's easy for me to tell when my mom or dad has a good day at work.
ceaq11It bothers me when my teacher doesn't feel well.
ceaq12I feel sorry for kids who can't find anyone to hang out with.
ceaq13Seeing a kid who is crying makes me feel like crying.
ceaq14If two kids are fighting, someone should stop it.
ceaq15It would bother me if my friend got grounded.
ceaq16When I see someone who is happy, I feel happy too.
ageChildren's age.
gradeChildren's grade.
genderGender.
Source
Funk, J. B., Fox, C. M., Chang, M., & Curtiss, K. (2008). The development of the Children's Empathic Attitudes Questionnaire using classical and Rasch analyses. Journal of Applied Developmental Psychology, 29, 187-196.
Bond, T. G., & Fox, C. M. (2015). Applying the Rasch Model: Fundamental Measurement in the Human Sciences. Routledge.
Examples
data(CEAQ)
str(CEAQ)
Family Intelligence
Description
Dataset from Hox (2010) containing six intelligence measures. Children are nested within families.
Usage
data("FamilyIQ")
Format
A data frame with 399 children, nested within 60 families:
familyFamily ID.
childChild ID.
wordlistWord list intelligence measure.
cardsCards intelligence measure.
matricesMatrices intelligence measure.
figuresFigures intelligence measure.
animalsAnimals intelligence measure.
occupationOccupation intelligence measure.
Source
Hox, J. J. (2010). Multilevel analysis: Techniques and applications (2nd ed.). New York: Routledge.
Van Peet, A. A. J. (1992). De potentieeltheorie van intelligentie. [The potentiality theory of intelligence]. Amsterdam: University of Amsterdam, Ph.D. Thesis.
Examples
data("FamilyIQ")
str(FamilyIQ)
Health Risk Behavior
Description
Dataset based on a questionnaire assessing health risk behaviors, including smoking, drinking, and marijuana consumption. The questionnaire was presented to teenagers at 5 points in time (from middle school to high school). The items are binary: 0 = never, 1 = at least one.
Usage
data("HRB")
Format
A data frame with 538 individuals with 4 items presented at 5 points in time. Items:
Alcohol.1Days with at least one drink in past year (T1).
Cigarettes.1Number of cigarettes per day in past year (T1).
Alcohol2.1Days with at least 5 drinks within a few hours in the past year (T1).
Marijuana.1Times consumed marijuana in the past year (T1).
Alcohol.2Days with at least one drink in past year (T2).
Cigarettes.2Number of cigarettes per day in past year (T2).
Alcohol2.2Days with at least 5 drinks within a few hours in the past year (T2).
Marijuana.2Times consumed marijuana in the past year (T2).
Alcohol.3Days with at least one drink in past year (T3).
Cigarettes.3Number of cigarettes per day in past year (T3).
Alcohol2.3Days with at least 5 drinks within a few hours in the past year (T3).
Marijuana.3Times consumed marijuana in the past year (T3).
Alcohol.4Days with at least one drink in past year (T4).
Cigarettes.4Number of cigarettes per day in past year (T4).
Alcohol2.4Days with at least 5 drinks within a few hours in the past year (T4).
Marijuana.4Times consumed marijuana in the past year (T4).
Alcohol.5Days with at least one drink in past year (T5).
Cigarettes.5Number of cigarettes per day in past year (T5).
Alcohol2.5Days with at least 5 drinks within a few hours in the past year (T5).
Marijuana.5Times consumed marijuana in the past year (T5).
Note
Thanks to Peter Franz for providing this dataset.
Examples
data("HRB")
str(HRB)
Research Topics Harvard Psychology Faculty
Description
A frequency table with the faculty members in the rows and the research topics in the colunms. The data are based on a scraping job from the faculty website by extracting the research summary of each faculty members. Subsequently, the data were cleaned using basic text processing tools. Finally, a document term matrix was created containing the most important keywords in the columns.
Usage
data("HarvardPsych")
Format
A word frequency table spanned 29 faculty members and 43 keywords.
Source
URL: http://psychology.fas.harvard.edu/faculty
Examples
data("HarvardPsych")
str(HarvardPsych)
Korean Speech Data
Description
This dataset represents a subset of the data collected in an experiment on the phonetic profile of Korean formality by Winter and Grawunder (2012). The authors were interested in pitch changes between two different attitudes (formal vs. informal).
Usage
data("KoreanSpeech")
Format
A data frame with 6 individuals (14 measurements per person) and the following variables:
subjectSubject ID
genderGender
scenario7 interaction types ("making an appointment", "asking for a favor", "apologizing for coming too late", etc.)
attitudeFormality: formal vs. informal.
frequencyPitch frequency in Hz
Source
Winter, B. (2013). Linear models and linear mixed effects models in R with linguistic applications. arXiv:1308.5499. (http://arxiv.org/pdf/1308.5499.pdf
Winter, B., & Grawunder, S. (2012) The phonetic profile of Korean formality. Journal of Phonetics, 40, 808-815.
Examples
data("KoreanSpeech")
str(KoreanSpeech)
Response to challenge scale
Description
The response to challenge scale (RCS) is a theory-derived, observer-rated measure of children's self-regulation in response to a physically challenging situation (Lakes & Hoyt, 2004; Lakes, 2012). It asks raters to make inferences in 3 domains: cognitive (6 items), affective/motivational (7 items), and physical (3 items). The data included here are post test ratings from the study presented in Lakes & Hoyt (2009).
Usage
data("Lakes")
Format
A data frame in long format with 194 individuals and the following variables:
personIDPerson ID.
raterIDRater ID.
itemItems for 3 subtests.
score7-point response score.
subtestSubtests (cognitive, affective, physical).
Source
Lakes, K. D. (2012). The Response to Challenge Scale (RCS): The development and construct validity of an observer-rated measure of children's self-regulation. The International Journal of Educational and Psychological Assessment, 10, 83-96.
Lakes, K. D, & Hoyt, W. T. (2004). Promoting self-regulation through school-based martial arts training. Journal of Applied Developmental Psychology, 25, 283-302.
Lakes, K. D., & Hoyt, W. T. (2009). Applications of generalizability theory to clinical child and adolescent psychology research. Journal of Clinical Child & Adolescent Psychology, 38, 144-165.
Examples
data("Lakes")
str(Lakes)
Neural Activity
Description
20 participants were scanned (fMRI) while performing a task designed to elicit their thoughts about 60 mental states.
On each trial, participants saw the name of a mental state (e.g., "awe"), and decided which of two scenarios would better evoke that mental state in another person (e.g., "seeing the Pyramids" or "watching a meteor shower"). Based on these measures, a
60 \times 60 correlation matrix was computed for each individual, subsequently converted into a dissimilarity matrix. In total, we have 20 such dissimilarity matrices. As additional external scales, NeuralScales gives 16 dimensions extracted from the psychological literature as a starting point for developing a theory of mental state representation: valence, arousal, warmth, competence, agency, experience, emotion, reason, mind, body, social, nonsocial, shared, and unique.
Usage
data("NeuralActivity")
data("NeuralScales")
data("NeuralScanner")
Format
A list of 20 dissimilarity matrices (NeuralActivity).
External scales (based on a questionnaire) containing proportions telling us to which degree people associate each of the 60 mental states to the 16 theoretical dimensions they extracted from the literature (NeuralScales).
Scanner information on states, onset times and stimulus duration (NeuralScanner).
Head motion parameters (NeuralHM).
Source
Tamir D. I., Thornton M. A., Contreras J. M., & Mitchell J. P. (2015) Neural evidence that three dimensions organize mental state representation: rationality, social impact, and valence. Proceedings of the National Academy of Sciences of the United States of America, 113(1), 194-199.
Examples
data(NeuralActivity)
str(NeuralActivity)
data(NeuralScales)
str(NeuralScales)
data(NeuralScanner)
str(NeuralScanner)
Goal-Directed Visual Processing
Description
Data derived from an fMRI experiment on visual representations. In the original experiment there were three experimental conditions (color on objects and background, color on dots, color on objects), three brain regions of interest (V1, PFS, Superior IPS), and two tasks (color and shape). The data included here are two dissimilarity matrices involving eight objects presented to the participants. The first matrix is based on a color task, the second matrix on a shape task.
Usage
data("Pashkam")
Format
A list of 2 dissimilarity matrices (color task and shape task):
BDBody
CTCat
CHChair
CRCar
ELElephant
FAFace
HOHouse
SCScissors
Source
Vaziri-Pashkam M., & Xu, Y. (2017) Goal-directed visual processing differentially impacts human ventral and dorsal visual representations. The Journal of Neuroscience, 37, 8767-8782.
Examples
data(Pashkam)
str(Pashkam)
Cognitive appraisal of work intensification
Description
Due to economic and technological changes, work has intensified over the past few decades. This intensification of work takes a toll on employees well-being and job satisfaction. Paskvan et al. (2016) established a model which explores the effects of work intensification on various outcomes (emotional exhaustion, job satisfaction). They used cognitive appraisal (i.e., how an individual views a situation) as a mediator and the participative climate as a moderator of the relationship between work intensification and cognitive appraisal.
Usage
data("Paskvan")
Format
A data frame with 803 individuals and the following 4 variables.
pclimateParticipative climate.
wintenseWork Intensification.
cogappCognitive appraisal of work intensification.
emotionEmotional exhaustion.
Source
Paskvan, M., Kubicek, B., Prem, R., & Korunka, C. (2016). Cognitive appraisal of work intensification. International Journal of Stress Management, 23, 124-146.
Examples
data("Paskvan")
str(Paskvan)
Internet Privacy
Description
These items measure various advantages and disadvantages which online users perceive when providing personal information on the Internet. The items are based on 25 qualitative interviews with online Marketing companies and experts as well as customer advocates. They represent the opinions of both organizations and individuals. Advantages of providing personal information online include support for purchasing decisions, increased satisfaction, targeted communication, participation in raffles, time savings and interesting content. Disadvantages include unsolicited advertising, excessive data collection, lack of information about data usage and decreasing service quality.
Usage
data("Privacy")
Format
A data frame with 405 individuals and the following 10 variables.
apc1Individualized communication supports me in making purchase decisions.
apc2Individualized communication increases my satisfaction with the organization.
apc3Individualization reduces the total amount of communication (e.g. the amount of emails I receive), since companies can advertise more target-oriented.
apc4I provide correct data, if I have a change of winning prizes.
apc5I provide correct data, if it saves me time (e.g. if I don't have to key in the data in the future).
apc6I provide correct data, if I get access to interesting content.
dpc1On the Internet my data are permanently collected and I can do nothing against it.
dpc2I feel that I am badly informed about the usage of my data.
dpc3If I divulge personal data, I lose control over how companies use my data.
dpc4Personalization leads to an increase in unsolicited advertising messages, since companies know what I am interested in.
Source
Treiblmaier, H. (2006) Datenqualitaet und individualisierte Kommunikation" [Data Quality and Individualized Communication], DUV Gabler Edition Wissenschaft, Wiesbaden.
Treiblmaier, H., Bentler, P. M., & Mair, P. (2011). Formative constructs implemented via common factors. Structural Equation Modeling: A Multidisciplinary Journal, 18, 1-17.
Examples
data(Privacy)
str(Privacy)
Work design questionnaire R package authors
Description
Contains the knowledge characteristics subscale of the Work Design Questionnaire (WDQ). Knowledge characteristics include job complexity, information processing, problem solving, skill variety, and specialization.
Usage
data("RWDQ")
Format
A data frame with 1055 individuals and 18 items: job complexity (22-24), information processing (25-27), problem solving (28-31), variety of skills (32-35), specialization (36-39). Item wordings:
wdq_22The work on R packages requires that I only do one task or activity at a time.
wdq_23The work on R packages comprises relatively uncomplicated tasks.
wdq_24The work on R packages involves performing relatively simple tasks.
wdq_25The work on R packages requires that I engage in a large amount of thinking.
wdq_26The work on R packages requires me to keep track of more than one thing at a time.
wdq_27The work on R packages requires me to analyze a lot of information
wdq_28The work on R packages involves solving problems that have no obvious correct answer.
wdq_29The work on R packages requires me to be creative.
wdq_30The work on R packages often involves dealing with problems that I have not encountered before.
wdq_31The work on R packages requires unique ideas or solutions to problems.
wdq_32The work on R packages requires data analysis skills.
wdq_33The work on R packages requires programming skills.
wdq_34The work on R packages requires technical skills regarding package building and documentation.
wdq_35The work on R packages requires the use of a number of skills.
wdq_36The work on R packages is highly specialized in terms of purpose, tasks, or activities.
wdq_37The tools, procedures, materials, and so forth used to develop R packages are highly specialized in terms of purpose.
wdq_38The work on R packages requires very specialized knowledge.
wdq_39The work on R packages requires a depth of expertise.
Source
Mair, P., Hofmann, E., Gruber, K., Zeileis, A., & Hornik, K. (2015) Motivation, values, and work design as drivers of participation in the R open source Project for Statistical Computing. Proceedings of the National Academy of Sciences of the United States of America, 112(48), 14788-14792.
Morgeson, F. P., & Humphrey, S. E. (2006). The Work Design Questionnaire (WDQ): Developing and validating a comprehensive measure for assessing job design and the nature of work. Journal of Applied Psychology, 91, 1321-1339
Examples
data(RWDQ)
str(RWDQ)
Motivational structure of R package authors
Description
Motivation is accurately understood as a complex continuum of intrinsic, extrinsic, and internalized extrinsic motives. This dataset contains three subscales for that measure extrinsic (12 items), hybrid (19 items), and intrinsic (5 items) aspects of motivation in relation to why package authors contribute to the R environment. The items were taken from Reinholt's motivation scale and adapted to R package authors. Each item started with "I develop R packages, because...".
Usage
data("Rmotivation")
Format
A data frame with 852 individuals, 36 motivation items, and 9 covariates:
ext1I can publish the packages in scientific journals.
ext2they are part of my master / PhD thesis.
ext3I need them for teaching courses.
ext4I develop them for clients who pay me.
ext5they are a byproduct of my empirical research. If I cannot find suitable existing software to analyze my data, I develop software components myself.
ext6they are a byproduct of my methodological research. If I develop/extend methods, I develop accompanying software, e.g., for illustrations and simulations.
ext7I expect an enhancement of my career from it.
ext8my employer pays me to do so.
ext9that's what my friends do.
ext10it is expected from me.
ext11that's what my work colleagues do.
ext12it comes more or less with my job.
hyb1it is an important task for me.
hyb2I believe that it is a necessity.
hyb3I believe it is vital to improve R.
hyb4I feel that R requires continuous enhancement.
hyb5I think that it is of importance.
hyb6it is part of my identity.
hyb7it is important for my personal goals but for no apparent rewards, such as money, career opportunities, etc.
hyb8it is part of my character to do so.
hyb9it is an integral part of my personality.
hyb10it is in line with my personal values.
hyb11I feel an obligation towards the R community.
hyb12it reflects my responsibility towards the R community.
hyb13I believe that it is appropriate to do so.
hyb14I aim for social approval of my activities.
hyb15I am committed to the R community.
hyb16I can feel satisfied with my performance.
hyb17it leaves me with a feeling of accomplishment.
hyb18it gives me satisfaction to produce something of high quality.
hyb19I get the feeling that I've accomplished something of great value.
int1I enjoy undertaking the required tasks.
int2I take pleasure in applying my skills.
int3it means pure fun for me.
int4I feel that it is an interesting exercise.
int5it is a joyful activity.
listsParticipation in R lists.
meetParticipation in R meetings/conferences.
npkgsNumber of packages developed/contributed.
genderGender.
phdPhD degree.
statseducEducation in statistics.
fulltimeFull-time vs. part-time employment.
academiaWork in acedemia.
statsworkWork in the area of statistics.
Source
Mair, P., Hofmann, E., Gruber, K., Zeileis, A., & Hornik, K. (2015) Motivation, values, and work design as drivers of participation in the R open source Project for Statistical Computing. Proceedings of the National Academy of Sciences of the United States of America, 112(48), 14788-14792.
Reinholt, M. (2006). No more polarization, please! Towards a more nuanced perspective on motivation in organizations. Technical report, Center for Strategic Management Working Paper Series, Copenhagen Business School, Copenhagen, Denmark.
Examples
data(Rmotivation)
str(Rmotivation)
Psychometric structure of R package authors
Description
This dataset contains factor scores (person parameters) based on a 2-PL IRT model fitted on the following three scales: word design questionnaire (WDQ; task, social, and knowledge characteristics), Reinholt's motivation scale (extrinsic, intrinsic, hyrbrid), and Schwartz' value scale (universalism, power, self-direction).
Usage
data("Rmotivation2")
Format
A data frame with 764 individuals and the following 18 variables.
listsParticipation in R lists.
meetParticipation in R meetings/conferences.
npkgsNumber of packages developed/contributed.
wtaskWDQ task subscale.
wsocialWDQ social subscale.
wknowledgeWDQ knowledge subscale.
mextrinsicExtrinsic motivation.
mhybridHybrid motivation.
mintrinsicIntrinsic motivation.
vuniversalismSchwartz value universalism.
vpowerSchwartz value power.
vselfdirectionSchwartz value self-direction.
genderGender.
phdPhD degree.
statseducEducation in statistics.
fulltimeFull-time vs. part-time employment.
academiaWork in acedemia.
statsworkWork in the area of statistics.
Source
Mair, P., Hofmann, E., Gruber, K., Zeileis, A., & Hornik, K. (2015) Motivation, values, and work design as drivers of participation in the R open source Project for Statistical Computing. Proceedings of the National Academy of Sciences of the United States of America, 112(48), 14788-14792.
See Also
Examples
data("Rmotivation2")
str(Rmotivation2)
Co-Morbid Obsessive-Compulsive Disorder and Depression
Description
Depression/OCD Data Collected at Rogers Memorial Hospital. The scales used in this study were the Quick Inventory of Depressive Symptomatology - self-report version (QIDS-SR), and the Yale-Brown Obsessive Compulsive Scale - self-report (Y-BOCS-SR). The depression scale has 16 items (5 response categories), the OCD scale 10 items (4 response categories).
Usage
data("Rogers")
Format
A data frame with 408 individuals and the following 26 variables (16 depression items followed by 10 OCD items).
onsetSleep-onset insomnia.
middleMiddle insomnia.
lateEarly morning awakening.
hypersomHypersomnia.
sadSadness.
decappetiteDecreased appetite.
incappetiteIncreased appetite.
weightlossWeight loss.
weightgainWeight gain.
concenConcentration impairment.
guiltGuilt and self-blame.
suicideSuicidal thoughts, plans or attempts.
anhedoniaAnhedonia.
fatigueFatigue.
retardPsychomotor retardation.
agitationAgitation.
obtimeTime consumed by obsessions.
obinterferInterference due to obsessions.
obdistressDistress caused by obsessions.
obresistDifficulty resisting obsessions.
obcontrolDifficulty controlling obsessions.
comptimeTime consumed by compulsions.
compinterfInterference due to compulsions.
compdisDistress caused by compulsions.
compresisDifficulty resisting compulsions.
compcontDifficulty controlling compulsions.
Source
McNally, R. J., Mair, P., Mugno, B. L., and Riemann, B. C. (2017). Comorbid obsessive-compulsive disorder and depression: A Bayesian network approach. Psychological Medicine, 47(7), 1204-1214.
Examples
data("Rogers")
str(Rogers)
Co-Morbid Obsessive-Compulsive Disorder and Depression – Adolescents
Description
Depression/OCD Data Collected at Rogers Memorial Hospital. The scales used in this study were the Quick Inventory of Depressive Symptomatology self-report version (QIDS-SR), and the Yale-Brown Obsessive Compulsive Scale - self-report (Y-BOCS-SR). The depression scale has 16 items (5 response categories), the OCD scale 10 items (4 response categories).
Usage
data("Rogers_Adolescent")
Format
A data frame with 87 individuals and 26 variables (16 depression items followed by 10 OCD items). See ?Rogers for details on individual items.
Source
Jones, P. J., Mair, P., Riemann, B. C., Mugno, B. L., & McNally, R. J. (2018). A network perspective on comorbid depression in adolescents with obsessive-compulsive disorder. Journal of Anxiety Disorders, 53, 1-8. #'
Examples
data("Rogers_Adolescent")
str(Rogers_Adolescent)
Longitudinal Social Dominance Orientation (SDO)
Description
Contains 4 SDO items measured across 5 years (1996-2000). Each item is scored on a 7-point scale.
Usage
data("SDOwave")
Format
Data frame containing 612 subjects, 4 items measure across 5 years (wide format). Here are the item labels for one year:
I1.1996It's probably a good thing that certain groups are at the top and other groups are at the bottom.
I2.1996Inferior groups should stay in their place.
I3.1996We should do what we can to equalize conditions for different groups (reversed).
I4.1996Increased social equality is beneficial to society (reversed).
Note
Thanks to Jim Sidanius for providing this dataset.
References
Sidanius, J., & Pratto, F. (2001). Social Dominance: An Intergroup Theory of Social Hierarchy and Oppression. Cambridge University Press, Cambridge, UK.
Examples
data("SDOwave")
str(SDOwave)
Wenchuan PTSD Dataset
Description
PTSD (posttraumatic stress disorder) symptoms reported by survivors of the Wenchuan earthquake in China using the PTSD checklist-civilian version (PCL-C). All items were scaled on a 5-point Likert scale (1 ... not at all; 2 ... a little bit; 3 ... moderately; 4 ... quite a bit; 5 ... extremely).
Usage
data("Wenchuan")
Format
A data frame with 362 observations on the following 17 variables.
intrusionRepeated, disturbing memories, thoughts, or images of a stressful experience from the past?
dreamsRepeated, disturbing dreams of a stressful experience from the past?
flashSuddenly acting or feeling as if a stressful experience were happening again (as if you were reliving it)?
upsetFeeling very upset when something reminded you of a stressful experience from the past?
physiorHaving physical reactions (e.g., heart pounding, trouble breathing, sweating) when something reminded you of a stressful experience from the past?
avoidthAvoiding thinking about or talking about a stressful experience from the past or avoiding having feelings related to it?
avoidactAvoiding activities or situations because they reminded you of a stressful experience from the past?
amnesiaTrouble remembering important parts of a stressful experience from the past?
lossintLoss of interest in activities that you used to enjoy?
distantFeeling distant or cut off from other people?
numbFeeling emotionally numb or being unable to have loving feelings for those close to you?
futureFeeling as if your future will somehow be cut short?
sleepTrouble falling or staying asleep?
angerFeeling irritable or having angry outbursts?
concenHaving difficulty concentrating?
hyperBeing "super-alert" or watchful or on guard?
startleFeeling jumpy or easily startled?
Source
McNally, R. J., Robinaugh, D. J., Wu, G. W. Y., Wang, L., Deserno, M. K., & Borsboom, D. (2015). Mental disorders as causal systems: A network approach to posttraumatic stress disorder. Clinical Psychological Science, 3(6), 836-849.
Examples
data(Wenchuan)
head(Wenchuan)
str(Wenchuan)
Wilson-Patterson Conservatism Scale
Description
This dataset contains a modified version of the classical Wilson-Patterson conservatism scale. Each item has the following response categories: 0 ... disapprove, 1 ... approve, 2 ... don't know.
Usage
data("WilPat")
Format
The first 15 items are conservative items, the remaining ones are liberal. There are 804 persons in the sample. In addition there are the following covariates:
CountryParticipant's country.
LibConsSelf-reported liberalism/conservatism.
LeftRightSelf-reported left/right identification.
GenderGender.
AgeAge.
Note
Thanks to Benedek Kurdi and Levente Littvay for providing this dataset.
Examples
data("WilPat")
str(WilPat)
Verbal Paired-Associates Memory Test (VPMT)
Description
Contains data from testmybrain.org within the context of face recognition. It includes the VPMT subscale.
Usage
data("Wilmer")
Format
A data frame with 1471 individuals, 25 VPMT items, as well as age and gender of the participant.
Source
Wilmer, J. B., Germine, L., Chabris, C. F., Chatterjee, G., Gerbasi, M. & Nakayama, K. (2012): Capturing specific abilities as a window into human individuality: The example of face recognition, Cognitive Neuropsychology, 29, 360-392
Examples
data(Wilmer)
str(Wilmer)
Youth Depression Indicators
Description
Contains Children's Depression Inventory (CDI) measures of sixth and seventh grade students. In total, there are 26 CDI items (on of the original CDI items asking about suicidal ideation was removed) with three response categories each (e.g., 0 = nobody really loves me, 1 = I am not sure if anybody loves me, or 2 = I am sure that somebody loves me).
Usage
data("YouthDep")
Format
A data frame with 2290 on the following 27 variables.
CDI1I am sad all the time
CDI2rNothing will ever work out for me
CDI3I do everything wrong
CDI4Nothing is fun at all
CDI5rI am bad all the time
CDI6I am sure that terrible things will happen to me
CDI7rI hate myself
CDI8rAll bad things are my fault
CDI10rI feel like crying every day
CDI11rThings bother me all the time
CDI12I do not want to be with people at all
CDI13rI cannot make up my mind about things
CDI14I look ugly
CDI15rI have to push myself all the time to do my schoolwork
CDI16rI have trouble sleeping every night
CDI17I am tired all the time
CDI18rMost days I do not feel like eating
CDI19I do not worry about aches and pains
CDI20I do not feel alone
CDI21rI never have fun at school
CDI22I do not have any friends
CDI23I do very badly in subjects I used to be good in
CDI24rI can never be as good as other kids
CDI25rNobody really loves me
CDI26I never do what I am told
CDI27I get into fights all the time
raceChildren's race
Source
Vaughn-Coaxum, R. A., Mair, P., & Weisz, J. R. (2015). Racial/ethnic differences in youth depression indicators: An Item Response Theory analysis of symptoms reported by White, Black, Asian, and Latino youths. Clinical Psychological Science, 4, 239-253.
Examples
data(YouthDep)
head(YouthDep)
str(YouthDep)
Time Series Implicit Association Test (Age)
Description
The implicit association test (IAT) measures differential association of two target concepts with an attribute. The outcome measure is the IAT D-measure, here transformed to a Cohen's d). There are different types of IAT. This dataset contains outcomes from the age IAT (where most individuals have an implicit preference for young over old) collected on the ProjectImplicit platform (http://implicit.harvard.edu/) from January 2007 to December 2015. Within each each month the participants d-measures were averaged. This leads to a time series with 140 observations.
Usage
data("ageiat")
Format
A vector of Cohen's d-scores, measured at 108 points in time (January 2007 - December 2015).
Note
Thanks to Tessa Charlesworth and Mahzarin Banaji for sharing this dataset.
Source
Greenwald, A. G., & Banaji, M. R. (1995). Implicit social cognition: Attitudes, self-esteem, and stereotypes. Psychological Review, 102, 4-27.
Greenwald, A. G., McGhee, D.E., & Schwartz, J. K. L. (1998). Measuring individual differences in implicit cognition: The Implicit Association Test. Journal of Personality and Social Psychology, 74, 1464-1480.
Nosek, B. A., Banaji, M. R., & Greenwald, A. G. (2002). Harvesting implicit group attitudes and beliefs from a demonstration web site. Group Dynamics: Theory, Research, and Practice, 6, 101-115.
Examples
data("ageiat")
str(ageiat)
Band Preferences
Description
Toy dataset involving paired comparisons of bands. 200 people stated their preferences of 5 bands in a paired comparison design (no undecided answer allowed).
Usage
data("bandpref")
Format
A data frame with 10 paired comparisons (200 people):
Band1First band
Band2Second band
Win1How often first band was preferred
Win2How often second band was preferred
Examples
data("bandpref")
str(bandpref)
Chile dataset.
Description
This dataset is a modified version of the dataset used in Wright and London (2009), originally taken from pepperjoe.com. The chile length is categorized from longest to shortest.
Usage
data("chile")
Format
A data frame with 85 chiles and the following 3 variables.
nameChile name.
lengthChile length: ordinal (1 ... longest, 10 ... shortest)
heatChile heat scale (see details)
Details
Heat scale according to pepperjoe.com: 1-2 ... for sissys; 3-4 ... sort of hot; 5-6 ... fairly hot; 7-8 ... real hot; 9.5-9 ... torrid; 9.5-10 ... nuclear.
Source
Wright, D. B., & London, K. (2009). Modern Regression Techniques Using R. Sage.
Examples
data(chile)
str(chile)
Attitude towards condoms
Description
This dataset is a modified version of the data used in de Ayala (2009). Originally, the data come from the voluntary HIV counseling and testing efficacy study performed by the center for AIDS prevention studies (2003).
Usage
data("condom")
Format
A data frame with 500 individuals and the following 7 variables. The 6 items were scored on a 4-point response scale (0 ... strongly disagree; 4 ... strongly agree).
FeelCondom does not have a good feel.
BuyI am embarrassed to buy condoms.
PutI am embarrased to put on condom.
BreakCondoms break/slip off.
CheatMy partner wants condoms to cheat.
UncomfortableMy friends said that condoms are uncomfortable.
CountryParticipant's country (artificially added).
Source
de Ayala, R. J. (2009). The Theory and Practice of Item Response Theory. Guilford Press, New York
Examples
data(condom)
str(condom)
Granularity
Description
Granularity refers to a person's ability to separate their emotions into specific types. People with low granularity struggle to separate their emotions (e.g., reporting that sadness, anger, fear, and others all just feel "bad""), whereas people with high granularity are very specific in how they parse their emotions (e.g., easily distinguishing between nuanced emotions like disappointment and frustration). A few outliers were removed compared to the original data.
Usage
data("granularity")
Format
A data frame with 143 individuals and the following 3 variables.
granGranularity score
ageParticipant's age
genderGender
Examples
data("granularity")
str(granularity)
Implicit Association Test (Faces)
Description
The implicit association test (IAT) measures differential association of two target concepts with an attribute. In this experiment the participants saw images of people with long faces, images of people with wide faces, positively valenced words, and negatively valenced words. In the first critical block ("congruent block"), participants were asked to press one response key if they saw a long-faced person or a positive word and a different response key if they saw a wide-faced person or a negative word. In the second critical block ("incongruent block"), the pairing was reversed. Participants were asked to press one key for long-faced people or negative words and a different key for wide-faced people or positive words. IAT theory states that participants are expected to be able to respond fast in congruent conditions and slowly in incongruent conditions. The dataset contains trajectories of 4 participants. Each participant was exposed 80 trials: first, 40 congruent block trials, followed by 40 incongruent block trials. The response variable is latency.
Usage
data("iatfaces")
Format
A data frame (4 individuals, 320 observations in total) with the following variables:
blockCongruent vs. incongruent.
latencyResponse time latency.
idSubject id.
trialTrial number.
Note
Thanks to Benedek Kurdi and Mahzarin Banaji for sharing this dataset.
Source
Greenwald, A. G., & Banaji, M. R. (1995). Implicit social cognition: Attitudes, self-esteem, and stereotypes. Psychological Review, 102, 4-27.
Greenwald, A. G., McGhee, D.E., & Schwartz, J. K. L. (1998). Measuring individual differences in implicit cognition: The Implicit Association Test. Journal of Personality and Social Psychology, 74, 1464-1480.
Nosek, B. A., Banaji, M. R., & Greenwald, A. G. (2002). Harvesting implicit group attitudes and beliefs from a demonstration web site. Group Dynamics: Theory, Research, and Practice, 6, 101-115.
Examples
data("iatfaces")
str(iatfaces)
Learning related emotions in mathematics
Description
This dataset considers achievement emotions students typically experience when learning mathematics. The authors considered 5 emotions: enjoyment (coded as 1), pride (2), anger (3), anxiety (4) and boredom (5). The data are organized in terms of paired comparisons (in standard order).
Usage
data("learnemo")
Format
A data frame with 111 individuals and the following paired comparisons (0 if the first emotion was chosen, 2 if the second emotion was chosen, and 1 if no decision was made).
pc1_2enjoyment vs. pride.
pc1_3enjoyment vs. anger.
pc2_3pride vs. anger.
pc1_4enjoyment vs. anxiety.
pc2_4pride vs. anxiety.
pc3_4anger vs. anxiety.
pc1_5enjoyment vs. boredom.
pc2_5pride vs. boredom.
pc3_5anger vs. boredom.
pc4_5anxiety vs. boredom.
sexParticipant's sex (1 = male, 2 = female).
Source
Grand, A., & Dittrich, R. (2015) Modelling assumed metric paired comparison data - application to learning related emotions. Austrian Journal of Statistics, 44, 3-15.
Examples
data("learnemo")
str(learnemo)
EEG Visual Working Memory Storage Capacity
Description
The data were collected in an experiment on visual working memory storage capacity. The left-right electrode voltages were averaged. The sampling frequency was originally 2 Hz. There were 4 conditions in the experiment: Set Size 1 - Ipsilateral Activity; Set Size 1 - Contralateral Activity; Set Size 3 - Ipsilateral Activity; Set Size 3 - Contralateral Activity. Memory display from 0-300 msec, consolidation period 300-1200 msec, after 1200 msec test period.
Usage
data("storcap")
Format
A data frame containing the following variables
idSubject ID
channelEEG channel (13 in total)
timeTime
condExperimental conditions
voltageVoltage electrode
Note
Thanks to Hrag Pailian for sharing this dataset.
Examples
data("storcap")
str(storcap)
Perceived Tension in Music Over Time
Description
This dataset comes from an experiment described Vines et al. (2006; the data were slightly modified). The authors were interested in how physical gestures of professional musicians contribute to the perception of emotion in a musical performance. 29 participants were exposed to the performance by either just listening (condition "auditory"), just seeing (condition "visual""), or both (condition "auditory-visual"). During the performance the participants had to move a slider to indicate the experienced tension they felt. They listened to the piece for 80 sec; every 10 msec the tension score (0 to 127) was recorded. This results in 800 tension measurement points per person (here provided as z-scores).
Usage
data("tension")
Format
A data frame with 29 individuals and 800 measurement points. The last column condition contains the experimental conditions (auditory, visual, auditory-visual).
Source
Vines, B. W., Krumhansl, C. L., Wanderley, M. M., Levitin, D. J. (2006). Cross-modal interactions in the perception of musical performance. Cognition, 101, 80-113.
Levitin, D. J., Nuzzo, R. L., Wines, B. W., & Ramsay, J. O. (2007). Introduction to functional data analysis. Canadian Psychology, 48, 135-155.
Examples
data("tension")
str(tension)
YAASS dataset
Description
Contains 30 participants of which 17 are of high risk psychosis and 13 are healthy controls. We have three variables pertaining to behavioral measures (factor scores): affective empathy (AE), positive social experience (PSE), and perspective taking (PT). Two additional measures come from fMRI scans (right hand fRH and left/right foot fLRF).
Usage
data("yaass")
Format
A data frame with 30 observations and 6 variables.
Examples
data("yaass")
str(yaass)
Neuropsychological Test Battery for Number Processing and Calculation in Children
Description
ZAREKI-R test battery (von Aster et al., 2006) for the assessment of dyscalculia in children. Includes subsets of 8 summation and 8 subtraction items, dichotomously scored, and 2 covariates.
Usage
data("zareki")
Format
A data frame with 341 and 18 variables. Variables starting with addit are summation items, variables starting with subtr are subtraction items. class denotes elementary school class, time the time in min require to complete the test.
Source
Koller, I., & Alexandrowicz, R. W. (2010) Eine psychometrische Analyse der ZAREKI-R mittels Rasch-Modellen [A psychometric analysis of the ZAREKI-R using Rasch-models]. Diagnostica 56, 57-67.
von Aster, M., Weinhold Zulauf, M., & Horn, R. (2006) Neuropsychologische Testbatterie fuer Zahlenverarbeitung und Rechnen bei Kindern (ZAREKI-R) [Neuropsychological Test Battery for Number Processing and Calculation in Children]. Harcourt Test Services, Frankfurt, Germany.
Examples
data(zareki)
str(zareki)