Package 'psychTools'

16 ability items scored as correct or incorrect.

Description

16 multiple choice ability items 1525 subjects taken from the Synthetic Aperture Personality Assessment (SAPA) web based personality assessment project are saved as iqitems. Those data are shown as examples of how to score multiple choice tests and analyses of response alternatives. When scored correct or incorrect, the data are useful for demonstrations of tetrachoric based factor analysis irt.fa and finding tetrachoric correlations.

Usage

data(iqitems)

Format

A data frame with 1525 observations on the following 16 variables. The number following the name is the item number from SAPA.

reason.4

Basic reasoning questions

reason.16

Basic reasoning question

reason.17

Basic reasoning question

reason.19

Basic reasoning question

letter.7

In the following alphanumeric series, what letter comes next?

letter.33

In the following alphanumeric series, what letter comes next?

letter.34

In the following alphanumeric series, what letter comes next

letter.58

In the following alphanumeric series, what letter comes next?

matrix.45

A matrix reasoning task

matrix.46

A matrix reasoning task

matrix.47

A matrix reasoning task

matrix.55

A matrix reasoning task

rotate.3

Spatial Rotation of type 1.2

rotate.4

Spatial Rotation of type 1.2

rotate.6

Spatial Rotation of type 1.1

rotate.8

Spatial Rotation of type 2.3

Details

16 items were sampled from 80 items given as part of the SAPA (https://www.sapa-project.org/) project (Revelle, Wilt and Rosenthal, 2009; Condon and Revelle, 2014) to develop online measures of ability. These 16 items reflect four lower order factors (verbal reasoning, letter series, matrix reasoning, and spatial rotations. These lower level factors all share a higher level factor ('g').

This data set may be used to demonstrate item response functions, tetrachoric correlations, or irt.fa as well as omega estimates of of reliability and hierarchical structure.

In addition, the data set is a good example of doing item analysis to examine the empirical response probabilities of each item alternative as a function of the underlying latent trait. When doing this, it appears that two of the matrix reasoning problems do not have monotonically increasing trace lines for the probability correct. At moderately high ability (theta = 1) there is a decrease in the probability correct from theta = 0 and theta = 2.

Source

The example data set is taken from the Synthetic Aperture Personality Assessment personality and ability test at https://www.sapa-project.org/. The data were collected with David Condon from 8/08/12 to 8/31/12.

Similar data are available from the International Cognitive Ability Resource at https://www.icar-project.org/.

References

Condon, David and Revelle, William, (2014) The International Cognitive Ability Resource: Development and initial validation of a public-domain measure. Intelligence, 43, 52-64.

Revelle, William, Dworak, Elizabeth M. and Condon, David (2020) Cognitive ability in everyday life: the utility of open-source measures. Current Directions in Psychological Science, 29, (4) 358-363. Open access at doi:10.1177/0963721420922178.

Dworak, Elizabeth M., Revelle, William, Doebler, Philip and Condon, David (2021) Using the International Cognitive Ability Resource as an open source tool to explore individual differences in cognitive ability. Personality and Individual Differences, 169. Open access at doi:10.1016/j.paid.2020.109906. Revelle, William, Wilt, Joshua, and Rosenthal, Allen (2010) Personality and Cognition: The Personality-Cognition Link. In Gruszka, Alexandra and Matthews, Gerald and Szymura, Blazej (Eds.) Handbook of Individual Differences in Cognition: Attention, Memory and Executive Control, Springer.

Examples

data(ability)
cs<- psych::cs
keys <- list(ICAR16=colnames(ability),reasoning =  cs(reason.4,reason.16,reason.17,reason.19),
  letters=cs(letter.7, letter.33,letter.34,letter.58), 
  matrix=cs(matrix.45,matrix.46,matrix.47,matrix.55), 
  rotate=cs(rotate.3,rotate.4,rotate.6,rotate.8))
  psych::scoreOverlap(keys,ability)
    #this next step takes a few seconds to run and demonstrates IRT approaches
     ability.irt <- psych::irt.fa(ability)
     ability.scores <- psych::scoreIrt(ability.irt,ability)
     ability.sub.scores <- psych::scoreIrt.2pl(keys,ability) #demonstrate irt scoring

#It is sometimes asked how to handle missing data when finding scores
#this next example compares 3 ways of scoring ability items from icar
#Just sum the items
#Sum the means for the items
#IRT score the items

total <- rowSums(ability, na.rm=TRUE)
 means  <- rowMeans(ability, na.rm=TRUE)
irt <- psych::scoreIrt(items=ability)[1]
 df <- data.frame(total, means,irt)
 psych:: pairs.panels(df)

---

Two data sets of affect and arousal scores as a function of personality and movie conditions

Description

A recurring question in the study of affect is the proper dimensionality and the relationship to various personality dimensions. Here is a data set taken from two studies of mood and arousal using movies to induce affective states.

Usage

data(affect)

Details

These are data from two studies conducted in the Personality, Motivation and Cognition Laboratory at Northwestern University. Both studies used a similar methodology:

Collection of pretest data using 5 scales from the Eysenck Personality Inventory and items taken from the Motivational State Questionnaire (see msq. In addition, state and trait anxiety measures were given. In the “maps" study, the Beck Depression Inventory was given also.

Then subjects were randomly assigned to one of four movie conditions: 1: Frontline. A documentary about the liberation of the Bergen-Belsen concentration camp. 2: Halloween. A horror film. 3: National Geographic, a nature film about the Serengeti plain. 4: Parenthood. A comedy. Each film clip was shown for 9 minutes. Following this the MSQ was given again.

Data from the MSQ were scored for Energetic and Tense Arousal (EA and TA) as well as Positive and Negative Affect (PA and NA).

Study flat had 170 participants, study maps had 160.

These studies are described in more detail in various publications from the PMC lab. In particular, Revelle and Anderson, 1997 and Rafaeli and Revelle (2006). An analysis of these data has also appeared in Smillie et al. (2012).

For a much more complete data set involving film, caffeine, and time of day manipulations, see the msqR data set.

Source

Data collected at the Personality, Motivation, and Cognition Laboratory, Northwestern University.

References

Revelle, William and Anderson, Kristen Joan (1997) Personality, motivation and cognitive performance: Final report to the Army Research Institute on contract MDA 903-93-K-0008

Rafaeli, Eshkol and Revelle, William (2006), A premature consensus: Are happiness and sadness truly opposite affects? Motivation and Emotion, 30, 1, 1-12.

Smillie, Luke D. and Cooper, Andrew and Wilt, Joshua and Revelle, William (2012) Do Extraverts Get More Bang for the Buck? Refining the Affective-Reactivity Hypothesis of Extraversion. Journal of Personality and Social Psychology, 103 (2), 206-326.

Examples

data(affect)
psych::describeBy(affect[-1],group="Film")
psych::pairs.panels(affect[14:17],bg=c("red","black","white","blue")[affect$Film],pch=21,
    main="Affect varies by movies ")
psych::errorCircles("EA2","TA2",data=affect,group="Film",labels=c("Sad","Fear","Neutral","Humor")
, main="Enegetic and Tense Arousal by Movie condition")
psych::errorCircles(x="PA2",y="NA2",data=affect,group="Film",labels=c("Sad","Fear","Neutral","
Humor"),  main="Positive and Negative Affect by Movie condition")

---

Gender Role Self Concept data from Athenstaedt (2003)

Description

Athenstaedt (2003) examined Gender Role Self-Concept. She reports two independent dimensions of Male and Female behaviors. While there are large gender/sex differences on both of these dimensions, the two represent independent factorsl Eagly and Revelle (2022) have used these data to explore the power of aggregation when examining sex differences. This data set is also useful to show various graphical display procedures.

Usage

data("Athenstaedt")

Format

A data frame with 576 observations on the following 117 variables.

STUDIE

a numeric vector

gender

Male =1, Female= 2

V1 - V74

self report items (see Athenstaedt.dictionary)

V1

Gender (Male = 1, Female =2)

V2

To pay attention to ones appearance in the office

V3

Offer fire to somebody

V4

Paint an Apartment

V5

Mow the Lawn

V6

Make the Bed

V7

Hold the Door Open for your Partner

V8

Do the Dishes

V9

Do Extreme Sports

V10

Tinker with the Car

V11

Talk about Sports

V12

Assemble Prefabricated Furniture

V13

Drive a Car in a Risky Way

V14

Listen Attentively to Others

V15

Tell your Partner about Problems at Work

V16

Play on a Computer

V17

Set the Table

V18

Watch ones Weight

V19

Care for a Partner if he/she is Ill

V20

Play Chess

V21

Meet with friends at a Regulars Table

V22

Watch Soap Operas

V23

Take a Friends Arm

V24

Wrap Presents Beautifully

V25

In case of Vacation with Partner Packing the Luggage for Both

V26

To admit own Occupational Weekness

V27

Work Overtime

V28

Openly Show Vulnerability

V29

Babysit

V30

Change Fuses

V31

Clean a Drain

V32

Take Care of Somebody

V33

Do Repair Work

V34

Change Light Bulbs

V35

Wash the Car

V36

Ride a Motorcycle

V37

Cook Meat on the Grill

V38

Thump Carpets

V39

Dust the Furniture

V40

Buy Electric Appliances

V41

Go Dancing

V42

Go for a Walk through Town

V43

Go to the Ballet

V44

Hug a Friend

V45

Do Handiwork (e.g. Knitting)

V46

Change Bed Sheets

V47

Sew on a Button

V48

Do Aerobics

V49

Watch Sports on Television

V50

Talk about Problems

V51

Play Parlor Games

V52

Talk about Politics

V53

Take Care of Flowers

V54

Make Coffee in the Office

V55

Shovel Snow

V56

Read non-Fiction Books

V57

Organize Company Parties

V58

Do Home Improvement Jobs

V59

Plead for the Socially Disadvantaged

V60

Buy a Present for a Colleague

V61

To Talk with Colleagues about Family Matters

V62

Make Jam

V63

Frquently Ask Colleagues Questions

V64

Decorate the Office with Flowers

V65

Pick up the Dinner Bill

V66

Shop for the Family

V67

Have Problem using Technical Devices

V68

Care for Family Besides a Job

V69

Watch Action Movies

V70

Cook

V71

Help your Partner Put on His or Her Coat

V72

Wash Windows

V73

Do the Ironing

V74

Do the Laundry

V75

Put on Make-up

V76

Femininity Scale

V77

Masculinity Scale

V78

Femininity Scale

V79

Masculinity Scale

V80

Pooled Scale

MMINUS1 - MPLUS

see the original Athenstaedt paper

FBEHAV

a numeric vector

MBEHAV

a numeric vector

Femininity

a numeric vector

Masculinity

a numeric vector

MF

a numeric vector

Details

Ursala Athenstaedt (2003) reported several analyses of items and scales measuring Gender Role Self-Concept. Eagly and Revelle (2022) have used these data in an analysis of the power of aggregation. Here are the original items as well as the three scales Eagly and Revelle (2022). The accompanying Athenstaedt.dictionary may be used to see the items.

See the GERAS data set for a related example.

Source

Ursala Athenstaedt, personal communication, 2022, provided a SPSS sav file with the original data from which the complete cases in this set were selected.

References

Ursula Athenstaedt (2003) On the Content and Structure of the Gender Role Self-Concept: Including Gender-Stereotypical Behaviors in Addition to Traits. Psychology of Women Quarterly, 27, 309-318. doi: 10.1111/1471-6402.00111.

Alice Eagly and William Revelle (2022) Understanding the Magnitude of Psychological Differences Between Women and Men Requires Seeing the Forest and the Trees. Perspectives in Psychological Science doi:10.1177/17456916211046006.

Examples

data(Athenstaedt)
psych::scatterHist(Femininity ~ Masculinity + gender, data =Athenstaedt,
cex.point=.4,smooth=FALSE, correl=FALSE,d.arrow=TRUE,col=c("red","blue"),
   lwd=4,  cex.main=1.5,main="Scatter Plot and Density",cex.axis=2)
   
psych::cohen.d(Athenstaedt[2:76], group="gender", dictionary=Athenstaedt.dictionary)
#show the top 5 items for each scale
select <- c(psych::selectFromKeys(Athenstaedt.keys$MF10),"gender")
psych::corPlot(Athenstaedt[,select], main="F and M items from Athenstaedt")

---

25 Personality items representing 5 factors

Description

25 personality self report items taken from the International Personality Item Pool (ipip.ori.org) were included as part of the Synthetic Aperture Personality Assessment (SAPA) web based personality assessment project. The data from 2800 subjects are included here as a demonstration set for scale construction, factor analysis, and Item Response Theory analysis. Three additional demographic variables (sex, education, and age) are also included.

Usage

data(bfi)
data(bfi.dictionary)

Format

A data frame with 2800 observations on the following 28 variables. (The q numbers are the SAPA item numbers).

A1

Am indifferent to the feelings of others. (q_146)

A2

Inquire about others' well-being. (q_1162)

A3

Know how to comfort others. (q_1206)

A4

Love children. (q_1364)

A5

Make people feel at ease. (q_1419)

C1

Am exacting in my work. (q_124)

C2

Continue until everything is perfect. (q_530)

C3

Do things according to a plan. (q_619)

C4

Do things in a half-way manner. (q_626)

C5

Waste my time. (q_1949)

E1

Don't talk a lot. (q_712)

E2

Find it difficult to approach others. (q_901)

E3

Know how to captivate people. (q_1205)

E4

Make friends easily. (q_1410)

E5

Take charge. (q_1768)

N1

Get angry easily. (q_952)

N2

Get irritated easily. (q_974)

N3

Have frequent mood swings. (q_1099

N4

Often feel blue. (q_1479)

N5

Panic easily. (q_1505)

O1

Am full of ideas. (q_128)

O2

Avoid difficult reading material.(q_316)

O3

Carry the conversation to a higher level. (q_492)

O4

Spend time reflecting on things. (q_1738)

O5

Will not probe deeply into a subject. (q_1964)

gender

Males = 1, Females =2

education

1 = HS, 2 = finished HS, 3 = some college, 4 = college graduate 5 = graduate degree

age

age in years

Details

The first 25 items are organized by five putative factors: Agreeableness, Conscientiousness, Extraversion, Neuroticism, and Opennness. The scoring key is created using make.keys, the scores are found using score.items.

These five factors are a useful example of using irt.fa to do Item Response Theory based latent factor analysis of the polychoric correlation matrix. The endorsement plots for each item, as well as the item information functions reveal that the items differ in their quality.

The item data were collected using a 6 point response scale: 1 Very Inaccurate 2 Moderately Inaccurate 3 Slightly Inaccurate 4 Slightly Accurate 5 Moderately Accurate 6 Very Accurate

as part of the Synthetic Apeture Personality Assessment (SAPA https://www.sapa-project.org/) project. To see an example of the data collection technique, visit https://www.SAPA-project.org/ or the International Cognitive Ability Resource at https://icar-project.org. The items given were sampled from the International Personality Item Pool of Lewis Goldberg using the sampling technique of SAPA. This is a sample data set taken from the much larger SAPA data bank.

Note

The bfi data set and items should not be confused with the BFI (Big Five Inventory) of Oliver John and colleagues (John, O. P., Donahue, E. M., & Kentle, R. L. (1991). The Big Five Inventory–Versions 4a and 54. Berkeley, CA: University of California,Berkeley, Institute of Personality and Social Research.)

Source

The items are from the ipip (Goldberg, 1999). The data are from the SAPA project (Revelle, Wilt and Rosenthal, 2010) , collected Spring, 2010 ( https://www.sapa-project.org/).

References

Goldberg, L.R. (1999) A broad-bandwidth, public domain, personality inventory measuring the lower-level facets of several five-factor models. In Mervielde, I. and Deary, I. and De Fruyt, F. and Ostendorf, F. (eds) Personality psychology in Europe. 7. Tilburg University Press. Tilburg, The Netherlands.

Revelle, W., Wilt, J., and Rosenthal, A. (2010) Individual Differences in Cognition: New Methods for examining the Personality-Cognition Link In Gruszka, A. and Matthews, G. and Szymura, B. (Eds.) Handbook of Individual Differences in Cognition: Attention, Memory and Executive Control, Springer.

Revelle, W, Condon, D.M., Wilt, J., French, J.A., Brown, A., and Elleman, L.G. (2016) Web and phone based data collection using planned missing designs. In Fielding, N.G., Lee, R.M. and Blank, G. (Eds). SAGE Handbook of Online Research Methods (2nd Ed), Sage Publcations.

See Also

bi.bars to show the data by age and gender, irt.fa for item factor analysis applying the irt model.

Examples

data(bfi)
psych::describe(bfi)
# create the bfi.keys (actually already saved in the data file)
 bfi.keys <-
  list(agree=c("-A1","A2","A3","A4","A5"),conscientious=c("C1","C2","C3","-C4","-C5"),
extraversion=c("-E1","-E2","E3","E4","E5"),neuroticism=c("N1","N2","N3","N4","N5"),
openness = c("O1","-O2","O3","O4","-O5")) 

 scores <- psych::scoreItems(bfi.keys,bfi,min=1,max=6) #specify the minimum and maximum values
 scores
 #show the use of the keys.lookup with a dictionary
 psych::keys.lookup(bfi.keys,bfi.dictionary[,1:4])

---

Dictionary for the 100 Big Five Adjectives

Description

Lew Goldberg organized 100 adjectives to measure 5 factors of personality (The Big5). 500 hundred participants were given these adjectives along with other personality measures. This dictionary allows for easy item labeling of the results. ~

Usage

data("BFI.adjectives.dictionary")

Format

A data frame with 100 observations on the following 2 variables.

numer

a character vector of the item label

Item

a character vector of the actual adjectives

Details

Keying information for the 100 adjectives:

Source

Data collected at the Personality, Motivation, and Cognition Laboratory, Northwestern University.

References

Lewis R. Goldberg,(1992) The development of markers for the Big-Five factor structure, Psychological Assessment, 4 (1) 26-42.

See Also

big5.100.adjectives for examples of the data. msqR for 3896 participants with scores on five scales of the EPI. affect for an example of the use of some of these adjectives in a mood manipulation study.

Examples

data(BFI.adjectives.dictionary) #this includes the bfi.adjectives.keys
bfi.adjectives.keys <- list(
Agreeableness =																				
psych::cs(V2,	-V11, V14, V15, -V19,	-V21, V29,	-V31, V32,	V48, V55,-V61,	-V63,	
V69, V76, -V78,	-V79, -V90,	-V94,	V99), 
Conscientiousness	= psych::cs(V9,	-V10,	V13, -V20,	V22, -V30, -V37, -V38, -V39,	
     V50,  -V51, V53, V56, V57, -V67,	V68, V70, V73, -V82, -V95),
Extraversion = psych::cs(V1,V5,	-V6,V7,	V17, V24, V26, -V40,-V45, -V58,	-V60,-V65,
     V71,  -V74,	-V77,	V92, -V96,	V97, V98, -V100),
Neuroticism= psych::cs(V3, V23, V25, V27,V28, V33,-V36, V42, V46,V47, V49, V52,-V59,V62,
 V72, V75, -V81,-V83,-V84, -V85),
Openness = psych::cs(V4,V8,V12, V16, V18,V34, -V35,V41, V43, V44, V54,	-V64,-V66, -V80,
-V86, -V87, -V88, -V89, -V91, -V93)
	)
	
psych::lookupFromKeys(bfi.adjectives.keys,bfi.adjectives.dictionary,20)

---

100 adjectives describing the "big 5" for 502 subjects

Description

Lew Goldberg organized 100 adjectives to measure 5 factors of personality (The Big5). 500 hundred participants were given these adjectives along with other personality measures in the Personality, Motivation and Cognition (PMC) lab. This data set is for demonstrations of factor and cluster analysis.

Usage

data("big5.100.adjectives")

Format

A data frame with 554 observations on the following 102 variables.

study

a character vector

id

a numeric vector

V1

numeric vector (see big5.adjectives.dictionary)

V100

A numeric vector. (see big5.adjectives.dictionary)

bfi.adjectives.keys

a key list

Details

Procedure. The data were collected over nine years in the Personality, Motivation and Cognition laboratory at Northwestern, as part of a series of studies examining the effects of personality and situational factors on motivational state and subsequent cognitive performance. In each of 38 studies, prior to any manipulation of motivational state, participants signed a consent form and in some studies, consumed 0 or 4mg/kg of caffeine. In caffeine studies, they waited 30 minutes and then filled out the MSQ as well as other personality trait measures (e.g. the Big 5 adjectives)

Source

Data collected at the Personality, Motivation, and Cognition Laboratory, Northwestern University.

References

Lewis R. Goldberg,(1992) The development of markers for the Big-Five factor structure, Psychological Assessment, 4 (1) 26-42.

Revelle, W. and Anderson, K.J. (1998) Personality, motivation and cognitive performance: Final report to the Army Research Institute on contract MDA 903-93-K-0008. (https://www.personality-project.org/revelle/publications/ra.ari.98.pdf).

Examples

data(big5.100.adjectives)
five.scores <- psych::scoreItems(big5.adjectives.keys,big5.100.adjectives)
summary(five.scores)

---

A 29 x 29 matrix that produces weird factor analytic results

Description

Normally, min.res factor analysis and maximum likelihood produce very similar results. This data set (from Alexandra Blant) does not. Warnings are given for the min.res solution, the pa solution, but not the old.min nor the mle solution. Included as a test case for the factor analysis function.

Usage

data("blant")

Format

The format is: num [1:29, 1:29] 1 0.77 0.813 0.68 0.717 ... - attr(*, "dimnames")=List of 2 ..$ : NULL ..$ : chr [1:29] "V1" "V2" "V3" "V4" ...

Details

This data matrix was sent by Alexandra Blant as an example of a problem with the minres solution in the fa function. The default solution, using fm="minres" issues a warning that the solution has improper factor score weights. This is not the case for the fm="old.min" and fm="mle" options, but is for fm="pa", fm="ols".

The residuals are indeed smaller for fm="minres" than for fm="old.min" or fm="mle".

"old.min" attempts to find the minimum residual but uses the gradient for mle. This was the approach until version 1.7.5 but was changed (see the help page for fa) following extensive communication with Hao Wu.

The problem with this matrix is probably that it is almost singular, with some smcs approaching 1 and the smallest three eigenvalues of .006, .004 and .001.

This problem matrix was provided by Alexandra Blant.

Source

Alexandra Blant, personal communication

Examples

data(blant)
#compare
f5 <- psych::fa(blant,5,rotate="none")  #the default minres 
f5.old <- psych::fa(blant,5, fm="old.min",rotate="none") #old version of minres
f5.mle <- psych::fa(blant,5,fm="mle",rotate= "none") #maximum likelihood
#compare solutions
psych::factor.congruence(list(f5,f5.old,f5.mle))
#compare sums of squared residuals
sum(residuals(f5,diag=FALSE)^2,na.rm=TRUE) # 1.355489
sum(residuals(f5.old,diag=FALSE)^2,na.rm=TRUE) # 1.539757
sum(residuals(f5.mle,diag=FALSE)^2,na.rm=TRUE) # 2.402092

#but, when we divide the squared residuals by the original (squared) correlations, we find 
#a different ordering of fit
f5$fit     # 0.9748177
f5.old$fit  # 0.9752774
f5.mle$fit   # 0.9603324

---

Bond's Logical Operations Test – BLOT

Description

35 items for 150 subjects from Bond's Logical Operations Test. A good example of Item Response Theory analysis using the Rasch model. One parameter (Rasch) analysis and two parameter IRT analyses produce somewhat different results.

Usage

data(blot)

Format

A data frame with 150 observations on 35 variables. The BLOT was developed as a paper and pencil test for children to measure Logical Thinking as discussed by Piaget and Inhelder.

Details

Bond and Fox apply Rasch modeling to a variety of data sets. This one, Bond's Logical Operations Test, is used as an example of Rasch modeling for dichotomous items. In their text (p 56), Bond and Fox report the results using WINSTEPS. Those results are consistent (up to a scaling parameter) with those found by the rasch function in the ltm package. The WINSTEPS seem to produce difficulty estimates with a mean item difficulty of 0, whereas rasch from ltm has a mean difficulty of -1.52. In addition, rasch seems to reverse the signs of the difficulty estimates when reporting the coefficients and is effectively reporting "easiness".

However, when using a two parameter model, one of the items (V12) behaves very differently.

This data set is useful when comparing 1PL, 2PL and 2PN IRT models.

Source

The data are taken (with kind permission from Trevor Bond) from the webpage https://www.winsteps.com/BF3/bondfox3.htm and read using read.fwf.

References

T.G. Bond. BLOT:Bond's Logical Operations Test. Townsville, Australia: James Cook Univer- sity. (Original work published 1976), 1995.

T. Bond and C. Fox. (2007) Applying the Rasch model: Fundamental measurement in the human sciences. Lawrence Erlbaum, Mahwah, NJ, US, 2 edition.

See Also

See also the irt.fa and associated plot functions.

Examples

data(blot)
 
 #ltm is not required by psychTools, but if available, may be run to show a Rasch model

#do the same thing with functions in psych
blot.fa <- psych::irt.fa(blot)  # a 2PN model
plot(blot.fa)

---

11 emotional variables from Burt (1915)

Description

Cyril Burt reported an early factor analysis with a circumplex structure of 11 emotional variables in 1915. 8 of these were subsequently used by Harman in his text on factor analysis. Unfortunately, it seems as if Burt made a mistake for the matrix is not positive definite. With one change from .87 to .81 the matrix is positive definite.

Usage

data(burt)

Format

A correlation matrix based upon 172 "normal school age children aged 9-12".

Sociality

Sociality

Sorrow

Sorrow

Tenderness

Tenderness

Joy

Joy

Wonder

Wonder

Elation

Elation

Disgust

Disgust

Anger

Anger

Sex

Sex

Fear

Fear

Subjection

Subjection

Details

The Burt data set is interesting for several reasons. It seems to be an early example of the organizaton of emotions into an affective circumplex, a subset of it has been used for factor analysis examples (see Harman.Burt, and it is an example of how typos affect data. The original data matrix has one negative eigenvalue. With the replacement of the correlation between Sorrow and Tenderness from .87 to .81, the matrix is positive definite.

Alternatively, using cor.smooth, the matrix can be made positive definite as well, although cor.smooth makes more (but smaller) changes.

Source

(retrieved from the web at https://www.biodiversitylibrary.org/item/95822#790) Following a suggestion by Jan DeLeeuw.

References

Burt, C.General and Specific Factors underlying the Primary Emotions. Reports of the British Association for the Advancement of Science, 85th meeting, held in Manchester, September 7-11, 1915. London, John Murray, 1916, p. 694-696 (retrieved from the web at https://www.biodiversitylibrary.org/item/95822#790)

See Also

Harman.Burt in the Harman dataset and cor.smooth

Examples

data(burt)
eigen(burt)$values  #one is negative!
burt.new <- burt
burt.new[2,3] <- burt.new[3,2] <- .81
eigen(burt.new)$values  #all are positive
bs <- psych::cor.smooth(burt)
round(burt.new - bs,3)

---

Distances between 11 US cities

Description

Airline distances between 11 US cities may be used as an example for multidimensional scaling or cluster analysis.

Usage

data(cities)

Format

A data frame with 11 observations on the following 11 variables.

ATL

Atlana, Georgia

BOS

Boston, Massachusetts

ORD

Chicago, Illinois

DCA

Washington, District of Columbia

DEN

Denver, Colorado

LAX

Los Angeles, California

MIA

Miami, Florida

JFK

New York, New York

SEA

Seattle, Washington

SFO

San Francisco, California

MSY

New Orleans, Lousianna

Details

An 11 x11 matrix of distances between major US airports. This is a useful demonstration of multiple dimensional scaling.

city.location is a dataframe of longitude and latitude for those cities.

Note that the 2 dimensional MDS solution does not perfectly capture the data from these city distances. Boston, New York and Washington, D.C. are located slightly too far west, and Seattle and LA are slightly too far south.

Source

https://www.timeanddate.com/worldclock/distance.html

Examples

data(cities)
city.location[,1] <- -city.location[,1] #included in the cities data set
plot(city.location, xlab="Dimension 1", ylab="Dimension 2",
   main ="Multidimensional scaling of US cities")
#do the mds   
city.loc <- cmdscale(cities, k=2) #ask for a 2 dimensional solution  round(city.loc,0) 
city.loc <- -city.loc  #flip the axes
 city.loc <- psych::rescale(city.loc,apply(city.location,2,mean),apply(city.location,2,sd))
points(city.loc,type="n") #add the date point to the map
text(city.loc,labels=names(cities))

## Not run:    #we need the maps package to be available
#an overlay map can be added if the package maps is available
if(require(maps)) {
  map("usa",add=TRUE)
}

## End(Not run)

---

Correlations of 14 ability tests from the Spanish version of the WAIS (taken from Colom et al. 2002.)

Description

Colom et al. analyze 14 tests from the Spanish version of the WAIS. This is a nice example of a hierarchical structure using the omega function. Here are the correlation matrices of the variables (colom), for 4 levels of education.

Usage

data("colom")
 data("colom.ed0")
 data("colom.ed1")
 data("colom.ed2")
 data("colom.ed3")

Format

The format is: num [1:14, 1:14] 1 0.755 0.608 0.555 0.715 0.729 0.627 0.616 0.606 0.598 ... - attr(*, "dimnames")=List of 2 ..$ : chr [1:14] "Vocabulary" "Similarities" "Arithmetic" "Digit_span" ... ..$ : chr [1:14] "Vocabulary" "Similarities" "Arithmetic" "Digit_span" ...

Details

The Wechsler Adult Intelligence Scale (WAIS) is the "gold standard" measure of intelligence. Here is an example of the correlational structure of 14 tests. It was used by Colom and his colleagues to find correlations of WAIS scores as a function of education. Here we show the complete standardization sample.

The colom data set is the complete correlation matrix for all subjects (703 females, 666 males). The four subset data sets for four levels of education. Ns = 301, 432, 525, and 111.

Source

Colom et al, 2002

References

Roberto Colom and Francisco J Abad and Luis F Garc and Manuel Juan-Espinosa, 2002, Education, Wechsler's Full Scale IQ, and g. Intelligence, 30, 449-462,

Examples

data(colom)
psych::lowerMat(colom)
psych::omega(colom, 4)    #do the omega analysis

---

Galton's example of the relationship between height and 'cubit' or forearm length

Description

Francis Galton introduced the 'co-relation' in 1888 with a paper discussing how to measure the relationship between two variables. His primary example was the relationship between height and forearm length. The data table (cubits) is taken from Galton (1888). Unfortunately, there seem to be some errors in the original data table in that the marginal totals do not match the table.

The data frame, heights, is converted from this table.

Usage

data(cubits)

Format

A data frame with 9 observations on the following 8 variables.

16.5

Cubit length < 16.5

16.75

16.5 <= Cubit length < 17.0

17.25

17.0 <= Cubit length < 17.5

17.75

17.5 <= Cubit length < 18.0

18.25

18.0 <= Cubit length < 18.5

18.75

18.5 <= Cubit length < 19.0

19.25

19.0 <= Cubit length < 19.5

19.75

19.5 <= Cubit length

Details

Sir Francis Galton (1888) published the first demonstration of the correlation coefficient. The regression (or reversion to mediocrity) of the height to the length of the left forearm (a cubit) was found to .8. There seem to be some errors in the table as published in that the row sums do not agree with the actual row sums. These data are used to create a matrix using table2matrix for demonstrations of analysis and displays of the data.

Source

Galton (1888)

References

Galton, Francis (1888) Co-relations and their measurement. Proceedings of the Royal Society. London Series,45,135-145,

See Also

table2matrix, table2df, ellipses, heights, peas,galton

Examples

data(cubits)
cubits
heights <- psych::table2df(cubits,labs = c("height","cubit"))
psych::ellipses(heights,n=1,main="Galton's co-relation data set")
psych::ellipses(jitter(heights$height,3),jitter(heights$cubit,3),pch=".",
     main="Galton's co-relation data set",xlab="height",
     ylab="Forearm (cubit)") #add in some noise to see the points
psych::pairs.panels(heights,jiggle=TRUE,main="Galton's cubits data set")

---

A data set from Cushny and Peebles (1905) on the effect of three drugs on hours of sleep, used by Student (1908)

Description

The classic data set used by Gossett (publishing as Student) for the introduction of the t-test. The design was a within subjects study with hours of sleep in a control condition compared to those in 3 drug conditions. Drug1 was 06mg of L Hscyamine, Drug 2L and Drug2R were said to be .6 mg of Left and Right isomers of Hyoscine. As discussed by Zabell (2008) these were not optical isomers. The detal1, delta2L and delta2R are changes from the baseline control.

Usage

data(cushny)

Format

A data frame with 10 observations on the following 7 variables.

Control

Hours of sleep in a control condition

drug1

Hours of sleep in Drug condition 1

drug2L

Hours of sleep in Drug condition 2

drug2R

Hours of sleep in Drug condition 3 (an isomer of the drug in condition 2

delta1

Change from control, drug 1

delta2L

Change from control, drug 2L

delta2R

Change from control, drug 2R

Details

The original analysis by Student is used as an example for the t-test function, both as a paired t-test and a two group t-test. The data are also useful for a repeated measures analysis of variance.

Source

Cushny, A.R. and Peebles, A.R. (1905) The action of optical isomers: II hyoscines. The Journal of Physiology 32, 501-510.

Student (1908) The probable error of the mean. Biometrika, 6 (1) , 1-25.

References

See also the data set sleep and the examples for the t.test

S. L. Zabell. On Student's 1908 Article "The Probable Error of a Mean" Journal of the American Statistical Association, Vol. 103, No. 481 (Mar., 2008), pp. 1- 20

Examples

data(cushny)
with(cushny, t.test(drug1,drug2L,paired=TRUE)) #within subjects 

psych::error.bars(cushny[1:4],within=TRUE,ylab="Hours of sleep",xlab="Drug condition", 
       main="95% confidence of within subject effects")

---

Convert a data frame, correlation matrix, or factor analysis output to a LaTeX or rtf table

Description

A set of handy helper functions to convert data frames or matrices to LaTeX or rtf tables. Although Sweave is the preferred means of converting R output to LaTeX, it is sometimes useful to go directly from a data.frame or matrix to a LaTeX table. cor2latex will find the correlations and then create a lower (or upper) triangular matrix for latex output. cor2rtf will do the same for rtf output. fa2latex and fa2rtf will create the latex commands for showing the loadings and factor intercorrelations. As the default option, tables are prepared in an approximation of APA format.

Usage

df2latex(x,digits=2,rowlabels=TRUE,apa=TRUE,short.names=TRUE,font.size ="scriptsize",
       big.mark=NULL,drop.na=TRUE, heading="A table from the psych package in R",
   caption="df2latex",label="default", char=FALSE, 
    stars=FALSE,silent=FALSE,file=NULL,append=FALSE,cut=0,big=0,abbrev=NULL,long=FALSE)
cor2latex(x,use = "pairwise", method="pearson", adjust="holm",stars=FALSE,
       digits=2,rowlabels=TRUE,lower=TRUE,apa=TRUE,short.names=TRUE,
     font.size ="scriptsize", heading="A correlation table from the psych package in R.",
      caption="cor2latex",label="default",silent=FALSE,file=NULL,append=FALSE,cut=0,big=0)
fa2latex(f,digits=2,rowlabels=TRUE,apa=TRUE,short.names=FALSE,cumvar=FALSE,
       cut=0,big=.3,alpha=.05,font.size ="scriptsize",long=FALSE,
       heading="A factor analysis table from the psych package in R",
       caption="fa2latex",label="default",silent=FALSE,file=NULL,append=FALSE) 
omega2latex(f,digits=2,rowlabels=TRUE,apa=TRUE,short.names=FALSE,cumvar=FALSE,cut=.2,
        big=.3,font.size ="scriptsize", 
        heading="An omega analysis table from the psych package in R",
        caption="omega2latex",label="default",silent=FALSE,file=NULL,append=FALSE)

irt2latex(f,digits=2,rowlabels=TRUE,apa=TRUE,short.names=FALSE,
       font.size ="scriptsize", heading="An IRT factor analysis table from R",
       caption="fa2latex",label="default",silent=FALSE,file=NULL,append=FALSE)
ICC2latex(icc,digits=2,rowlabels=TRUE,apa=TRUE,ci=TRUE,
   font.size ="scriptsize",big.mark=NULL, drop.na=TRUE,
    heading="A table from the psych package in R",
   caption="ICC2latex",label="default",char=FALSE,silent=FALSE,file=NULL,append=FALSE) 

#not all options are yet implemented in these next three functions.   
df2rtf(x,file=NULL, digits=2,rowlabels=TRUE,width=8.5,old=NULL, apa=TRUE,short.names=TRUE,
	font.size =10,big.mark=NULL, drop.na=TRUE, 
	heading="A table from the psych package in R",
	caption="Created with df2rtf",label="default",char=FALSE,stars=FALSE,silent=FALSE,
	append=FALSE,cut=0,big=.0,abbrev=NULL,long=FALSE) 

cor2rtf(x,file=NULL, use = "pairwise", method="pearson", adjust="holm", digits=2,
	rowlabels=TRUE,width=8.5,lower=TRUE,old=NULL, apa=TRUE,short.names=TRUE,
	font.size =10,big.mark=NULL, drop.na=TRUE, 
	heading="A correlation matrix from the psych package in R",
	caption="Created with cor2rtf.   left justify output if stars", 
	label="default",char=FALSE,stars=FALSE,silent=FALSE,
	append=FALSE,cut=0,big=.0,abbrev=NULL,long=FALSE) 
	
fa2rtf(f,file=NULL, use = "pairwise", method="pearson", adjust="holm", digits=2,
	rowlabels=TRUE,width=8.5,lower=TRUE,old=NULL, apa=TRUE,short.names=TRUE,
	font.size =10,big.mark=NULL, drop.na=TRUE, 
	heading="A Factor analysis   from the psych package in R",
	caption="Created with fa2rtf. ",label="default",char=FALSE,silent=FALSE,
	append=FALSE,cut=0,big=.0,abbrev=NULL)

Arguments

x	A data frame or matrix to convert to LaTeX. If non-square, then correlations will be found prior to printing in cor2latex
digits	Round the output to digits of accuracy. NULL for formatting character data
abbrev	How many characters should be used in column names –defaults to digits + 3
rowlabels	If TRUE, use the row names from the matrix or data.frame
short.names	Name the columns with abbreviated rownames to save space
apa	If TRUE formats table in APA style
cumvar	For factor analyses, should we show the cumulative variance accounted for?
font.size	e.g., "scriptsize", "tiny" or anyother acceptable LaTeX font size.
heading	The label appearing at the top of the table
caption	The table caption
lower	in cor2latex, just show the lower triangular matrix
f	The object returned from a factor analysis using fa or irt.fa.
label	The label for the table
big.mark	Comma separate numbers large numbers (big.mark=",")
drop.na	Do not print NA values
method	When finding correlations, which method should be used (pearson)
use	use="pairwise" is the default when finding correlations in cor2latex
adjust	If showing probabilities, which adjustment should be used (holm)
stars	Should probability 'magic astericks' be displayed in cor2latex (FALSE)
char	char=TRUE allows printing tables with character information, but does not allow for putting in commas into numbers
cut	In omega2latex, df2latex and fa2latex, do not print abs(values) < cut
big	In fa2latex and df2latex boldface those abs(values) > big
alpha	If fa has returned confidence intervals, then what values of loadings should be boldfaced?
icc	Either the output of an ICC, or the data to be analyzed.
ci	Should confidence intervals of the ICC be displayed
silent	If TRUE, do not print any output, just return silently – useful if using Sweave
file	If specified, write the output to this file
append	If file is specified, then should we append (append=TRUE) or just write to the file
long	if TRUE, then do long tables. (requires the longtables package in latex)
old	When appending output with df2rtf, old is the output from the prior run.
width	page width in inches for df2rtf

Value

A LaTeX table. Note that if showing "stars" for correlations, then one needs to use the siunitx package in LaTex. The entire LaTeX output is also returned invisibly. If using Sweave to create tables, then the silent option should be set to TRUE and the returned object saved as a file. See the last example.

Finally, some users have asked for the ability to convert these output tables into HTML. This may be done using the tth package.

Three functions to write to rtf files (for use in various proprietary word processing languages) have been added with version 2.4.3. These will write to an rtf file and may be formatted directly. df2rtf takes a data frame and writes it as a table with header information.

cor2rtf will take either a data matrix (and find the correlations) or just a correlation matrix. "magic astericks " can be added to the correlations using the stars=TRUE option. In this case, the result table can be left justified in a word processing language to get the numbers to appear correctly justified.

fa2latex and fa2rtf can take the output from either a factor analysis or from fa.lookup.

Author(s)

William Revelle with suggestions from Jason French and David Condon and Davide Morselli

See Also

The many LaTeX conversion routines in Hmisc.

To convert these LaTex objects to HTML, you should install the tth package.

Consider the last example for creating HTML

Examples

df2latex(psych::Thurstone,rowlabels=FALSE,apa=FALSE,short.names=FALSE,
        caption="Thurstone Correlation matrix")
df2latex(psych::Thurstone,heading="Thurstone Correlation matrix in APA style")

df2latex(psych::describe(psych::sat.act)[2:10],short.names=FALSE)
cor2latex(psych::Thurstone)
cor2latex(psych::sat.act,short.names=FALSE)
fa2latex(psych::fa(psych::Thurstone,3),heading="Factor analysis from R in quasi APA style")


#to write to rtf file
#replace the temporary file name with something more useful
fn <- tempfile(pattern="example",fileext=".rtf")  #create a temporary file
#better is to create a local file
# e.g. fn <- "rtf_example.rtf"

cor2rtf(sat.act, file=fn)   #write to the file

dd <- psych::describe(sat.act)
temp <- df2rtf(dd, file=fn, append=TRUE, width=12) #write and keep open
temp1 <-  cor2rtf(sat.act,old=temp,caption=date(), append=TRUE)  #use date as caption 
cor2rtf(sat.act, old=temp1, stars=TRUE) #close the file
#now open this with your word processor and reformat with left justify

#now write a factor analysis output to an output file
# e.g. fn <- "rtf_example.rtf"
f5 <- psych::fa(bfi,5)
temp <- fa2rtf(f5, width=12, file=fn, append=TRUE)  #a normal fa output
fl <- psych::fa.lookup(f5, dictionary=bfi.dictionary)
fa2rtf(fl, old = temp)
##now open this with your word processor

#To convert these latex tables to HTML

#f3.lat <- fa2latex(psych::fa(psych::Thurstone,3),
#    heading="Factor analysis from R in quasi APA style")
#library(tth)
#f3.ht <- tth(f3.lat)
#print(as.data.frame(f3.ht),row.names=FALSE)

###

 #If using Sweave to create a LateX table as a separate file then set silent=TRUE
#e.g., 
#LaTex preamble 
#....
#<<print=FALSE,echo=FALSE>>= 
#f3 <- fa(Thurstone,3)
#fa2latex(f3,silent=TRUE,file='testoutput.tex')
#@
#
#\input{testoutput.tex}

---

Sort (order) a dataframe or matrix by multiple columns

Description

Although order will order a vector, and it is possible to order several columns of a data.frame by specifying each column individually in the call to order, dfOrder will order a dataframe or matrix by as many columns as desired. The default is to sort by columns in lexicographic order. If the object is a correlation matrix, then the selected columns are sorted by the (abs) max value across the columns (similar to fa.lookup in psych). If object is a correlation matrix, rows and columns are sorted.

Usage

dfOrder(object, columns,absolute=FALSE,ascending=TRUE)

Arguments

object	The data.frame or matrix to be sorted
columns	Column numbers or names to use for sorting. If positive, then they will be sorted in increasing order. If negative, then in decreasing order
absolute	If TRUE, then sort the absolute values
ascending	By default, order from smallest to largest.

Details

This is just a simple helper function to reorder data.frames and correlation matrices. Originally developed to organize IRT output from the ltm package. It is a basic add on to the order function.

(Completely rewritten for version 1.8.1. and then again for 2.2.1 to allow sorting correlation matrices by numeric values.)

Value

The original data frame is now in sorted order. If the input is a correlation matrix, the output is sorted by rows and columns.

Author(s)

William Revelle

See Also

Other useful file manipulation functions include read.file to read in data from a file or read.clipboard from the clipboard, fileScan, filesList, filesInfo, and fileCreate

dfOrder code is used in the test.irt function to combine ltm and sim.irt output.

Examples

#create a data frame and then sort it in lexicographic order
set.seed(42)
x <- matrix(sample(1:4,64,replace=TRUE),ncol=4)
dfOrder(x)  # sort by all columns
dfOrder(x,c(1,4))  #sort by the first and 4th column
x.df <- data.frame(x)
dfOrder(x.df,c(1,-2))  #sort by the first in increasing order, 
   #the second in decreasing order

#now show sorting correlation matrices  
r <- cor(sat.act,use="pairwise")
r.ord <- dfOrder(r,columns=c("education","ACT"),ascending=FALSE)
psych::corPlot(r.ord)

---

Eminence of 69 American Psychologists

Description

Marco Del Giudice criticized an earlier study by Simonton for using partial regression weights to estimate the importance of various predictors of rated eminence. This is a nice example of the (mis)interpretation of beta weights of highly correlated predictors.

Usage

data("eminence")

Format

A data frame with 69 observations on the following 9 variables.

name

a character vector

reputation

Log of rated reputation

birth.year

Year of birth

first.year

Year of first cited publicatin

last.year

Year of last cited publication

works

Log of number of publications

citations

Log of number of citations

composite

A composite index of publications

h

The 'h' index of citations

Details

Simonton (1997, 2014) discusses various estimates of eminence among 69 psychologists born between 1842 and 1912 and reports that the regression weights are small and interprets this as meaning number of publications and citations are not very important. Del Giudice (2020) points out that citations and the number of publications are highly collinear and thus while their independent contributions are small, their joint effect is quite large (R= .69 ). These data are given here as an example of multiple correlation and partial correlation

Source

Del Giudice (2020) links to a web page with the data.

References

Marco Del Giudice (2020). How Well Do Bibliometric Indicators Correlate With Scientific Eminence? A Comment on Simonton (2016). Perspective in Psychological Science, 15, 202-203.

Simonton, D. K. (1992). Leaders of American psychology, 1879-1967: Career development, creative output, and professional achievement. Journal of Personality and Social Psychology, 62, 5-17.

Simonton, D. K. (2016). Giving credit where credit is due: Why it's so hard to do in psychological science. Perspectives on Psychological Science, 11, 888-892.

Examples

data(eminence)
psych::lowerCor(eminence)
cs <- psych::cs
psych::partial.r(eminence, x= cs(reputation, works, citations),y=cs(birth.year))
psych::setCor(reputation ~ works + h +  first.year,data=eminence)

---

Eysenck Personality Inventory (EPI) data for 3570 participants

Description

The EPI is and has been a very frequently administered personality test with 57 measuring two broad dimensions, Extraversion-Introversion and Stability-Neuroticism, with an additional Lie scale. Developed by Eysenck and Eysenck, 1964. Eventually replaced with the EPQ which measures three broad dimensions. This data set represents 3570 observations collected in the early 1990s at the Personality, Motivation and Cognition lab at Northwestern. An additional data set (epiR) has test and retest information for 474 participants. The data are included here as demonstration of scale construction and test-retest reliability.

Usage

data(epi)
data(epi.dictionary)
data(epiR)

Format

A data frame with 3570 observations on the following 57 variables.

id

The identification number within the study

time

First (group testing) or 2nd time (before a lab experiment) for the epiR data set.

study

Four lab based studies and their pretest data

V1

a numeric vector

V2

a numeric vector

V3

a numeric vector

V4

a numeric vector

V5

a numeric vector

V6

a numeric vector

V7

a numeric vector

V8

a numeric vector

V9

a numeric vector

V10

a numeric vector

V11

a numeric vector

V12

a numeric vector

V13

a numeric vector

V14

a numeric vector

V15

a numeric vector

V16

a numeric vector

V17

a numeric vector

V18

a numeric vector

V19

a numeric vector

V20

a numeric vector

V21

a numeric vector

V22

a numeric vector

V23

a numeric vector

V24

a numeric vector

V25

a numeric vector

V26

a numeric vector

V27

a numeric vector

V28

a numeric vector

V29

a numeric vector

V30

a numeric vector

V31

a numeric vector

V32

a numeric vector

V33

a numeric vector

V34

a numeric vector

V35

a numeric vector

V36

a numeric vector

V37

a numeric vector

V38

a numeric vector

V39

a numeric vector

V40

a numeric vector

V41

a numeric vector

V42

a numeric vector

V43

a numeric vector

V44

a numeric vector

V45

a numeric vector

V46

a numeric vector

V47

a numeric vector

V48

a numeric vector

V49

a numeric vector

V50

a numeric vector

V51

a numeric vector

V52

a numeric vector

V53

a numeric vector

V54

a numeric vector

V55

a numeric vector

V56

a numeric vector

V57

a numeric vector

Details

The original data were collected in a group testing framework for screening participants for subsequent studies. The participants were enrolled in an introductory psychology class between Fall, 1991 and Spring, 1995.

The actual items may be found in the epi.dictionary.

The structure of the E scale has been shown by Rocklin and Revelle (1981) to have two subcomponents, Impulsivity and Sociability. These were subsequently used by Revelle, Humphreys, Simon and Gilliland (1980) to examine the relationship between personality, caffeine induced arousal, and cognitive performance.

The epiR data include the original group testing data and matched data for 474 participants collected several weeks later. This is useful for showing that internal consistency estimates (e.g. alpha or omega) can be low even though the test is stable across time. For more demonstrations of the distinction between immediate internal consistency and delayed test-retest reliability see the msqR and sai data sets and testRetest.

Source

Data from the PMC laboratory at Northwestern.

References

Eysenck, H.J. and Eysenck, S. B.G. (1968). Manual for the Eysenck Personality Inventory.Educational and Industrial Testing Service, San Diego, CA.

Revelle, W. and Humphreys, M. S. and Simon, L. and Gilliland, K. (1980) Interactive effect of personality, time of day, and caffeine: A test of the arousal model, Journal of Experimental Psychology General, 109, 1, 1-31,

Examples

data(epi)
epi.keys <- list(E = c("V1",  "V3",  "V8",  "V10", "V13", "V17", "V22", "V25", "V27", "V39",
  "V44", "V46", "V49", "V53", "V56", "-V5", "-V15", "-V20", "-V29", "-V32", "-V34","-V37",
   "-V41", "-V51"),
N = c( "V2", "V4", "V7", "V9", "V11", "V14", "V16", "V19", "V21", "V23", "V26", "V28", 
"V31", "V33", "V35", "V38", "V40","V43", "V45", "V47", "V50", "V52","V55", "V57"),
L = c("V6",  "V24", "V36", "-V12", "-V18", "-V30", "-V42", "-V48", "-V54"),
Imp = c( "V1",  "V3",  "V8",  "V10", "V13", "V22", "V39", "-V5", "-V41"),
Soc = c( "V17", "V25", "V27", "V44", "V46", "V53", "-V11", "-V15", "-V20", 
"-V29", "-V32", "-V37", "-V51")
)
scores <- psych::scoreItems(epi.keys,epi)

psych::keys.lookup(epi.keys[1:3],epi.dictionary) #show the items and keying information

#a variety of demonstrations (not run) of test retest reliability versus alpha versus omega

E <- psych::selectFromKeys(epi.keys$E)
#look at the testRetest help file for more examples

---

13 personality scales from the Eysenck Personality Inventory and Big 5 inventory

Description

A small data set of 5 scales from the Eysenck Personality Inventory, 5 from a Big 5 inventory, a Beck Depression Inventory, and State and Trait Anxiety measures. Used for demonstrations of correlations, regressions, graphic displays.

Usage

data(epi.bfi)

Format

A data frame with 231 observations on the following 13 variables.

epiE

EPI Extraversion

epiS

EPI Sociability (a subset of Extraversion items

epiImp

EPI Impulsivity (a subset of Extraversion items

epilie

EPI Lie scale

epiNeur

EPI neuroticism

bfagree

Big 5 inventory (from the IPIP) measure of Agreeableness

bfcon

Big 5 Conscientiousness

bfext

Big 5 Extraversion

bfneur

Big 5 Neuroticism

bfopen

Big 5 Openness

bdi

Beck Depression scale

traitanx

Trait Anxiety

stateanx

State Anxiety

Details

Self report personality scales tend to measure the “Giant 2" of Extraversion and Neuroticism or the “Big 5" of Extraversion, Neuroticism, Agreeableness, Conscientiousness, and Openness. Here is a small data set from Northwestern University undergraduates with scores on the Eysenck Personality Inventory (EPI) and a Big 5 inventory taken from the International Personality Item Pool.

Source

Data were collected at the Personality, Motivation, and Cognition Lab (PMCLab) at Northwestern by William Revelle)

References

https://personality-project.org/pmc.html

Examples

data(epi.bfi)
psych::pairs.panels(epi.bfi[,1:5])
psych::describe(epi.bfi)

---

Galton's Mid parent child height data

Description

Two of the earliest examples of the correlation coefficient were Francis Galton's data sets on the relationship between mid parent and child height and the similarity of parent generation peas with child peas. This is the data set for the Galton height.

Usage

data(galton)

Format

A data frame with 928 observations on the following 2 variables.

parent

Mid Parent heights (in inches)

child

Child Height

Details

Female heights were adjusted by 1.08 to compensate for sex differences. (This was done in the original data set)

Source

This is just the galton data set from UsingR, slightly rearranged.

References

Stigler, S. M. (1999). Statistics on the Table: The History of Statistical Concepts and Methods. Harvard University Press. Galton, F. (1886). Regression towards mediocrity in hereditary stature. Journal of the Anthropological Institute of Great Britain and Ireland, 15:246-263. Galton, F. (1869). Hereditary Genius: An Inquiry into its Laws and Consequences. London: Macmillan.

Wachsmuth, A.W., Wilkinson L., Dallal G.E. (2003). Galton's bend: A previously undiscovered nonlinearity in Galton's family stature regression data. The American Statistician, 57, 190-192.

See Also

The other Galton data sets: heights, peas,cubits

Examples

data(galton)
psych::describe(galton)
 #show the scatter plot and the lowess fit 
psych::pairs.panels(galton,main="Galton's Parent child heights")  
#but this makes the regression lines look the same
psych::pairs.panels(galton,lm=TRUE,main="Galton's Parent child heights") 
 #better is to scale them 
psych::pairs.panels(galton,lm=TRUE,xlim=c(62,74),ylim=c(62,74),
              main="Galton's Parent child heights")

---

Data from Gruber et al, 2020, Study 2: Gender Related Attributes Survey

Description

Gruber et al. (2020) report on the psychometric properties of a multifaceted Gender Related Attributes Survey. Here are the data from their 3 domains (Personality, Cognition and Activities and Interests from their study 2. Eagly and Revelle (2022) include these data in their review of the power of aggregation. The data are included here as demonstrations of the cohen.d and scatterHist functions in the psych package and may be used to show the power of aggregation.

Usage

data("GERAS")
#These other objects are included in the file
# data("GERAS.scales")
# data("GERAS.dictionary")
# data("GERAS.items")
# data("GERAS.keys")

Format

A data frame with 471 observations on the following 51 variables (selected from the original 93) The code numbers are item numbers from the bigger set.

V15

reckless

V22

willing to take risks

V11

courageous

V6

a adventurous

V19

dominant

V14

controlling

V20

boastful

V21

rational

V23

analytical

V9

pragmatic

V44

to find an address for the first time

V45

to find a way again

V46

to understand equations

V50

to follow directions

V51

to understand equations

V53

day-to-day calculations

V48

to write a computer program

V69

paintball

V73

driving go-cart

V71

drinking beer

V68

watching action movies

V75

playing cards (poker)

V72

watching sports on TV

V67

doing certain sports (e.g. soccer, ...)

V74

Gym (weightlifting)

V27

warm-hearted

V28

loving

V29

caring

V26

compassionate

V32

delicate

V30

tender

V24

familiy-oriented

V40

anxious

V39

thin-skinned

V41

careful

V55

to explain foreign words

V58

to find the right words to express certain content

V59

synonyms for a word in order to avoid repetitions

V60

to phrase a text

V54

remembering events from your own life

V63

to notice small changes

V57

to remember names and faces

V89

shopping

V92

gossiping

V81

watching a romantic movie

V80

talking on the phone with a friend

V90

yoga

V83

rhythmic gymnastics

V84

going for a walk

V86

dancing

gender

gender (M=1 F=2)

Details

These 50 items (+ gender) may be formed into scales using the GERAS.keys The first 10 items are Male Personality, the next 10 are Female Personality, then 7 and 7 M and F Cognition, then 8 and 8 M and F Activity items. The Pers, Cog and Act scales are formed from the M-F scales for the three domains. M and F are the composites of the Male and then the Female scales. MF.all is the composite of the M - F scales. See the GERAS.keys object for scoring directions.

"M.pers" "F.pers" "M.cog" "F.cog" "M.act" "F.act" "Pers" "Cog" "Act" "M" "F" "MF.all" "gender"

See the Athenstaedt data set for a related data set.

Source

Study 2 data downloaded from the Open Science Framework https://osf.io/42jhr/ Used by kind permission of Freya M. Gruber, Tullia Ortner, and Belinda A. Pletzer.

References

Alice H. Eagly and William Revelle (2022), Understanding the Magnitude of Psychological Differences Between Women and Men Requires Seeing the Forest and the Tree. Perspectives in Psychological Science doi:10.1177/17456916211046006

Gruber, Freya M. and Distlberger, Eva and Scherndl, Thomas and Ortner, Tuulia M. and Pletzer, Belinda (2020) Psychometric properties of the multifaceted Gender-Related Attributes Survey (GERAS) European Journal of Psychological Assessment, 36, (4) 612-623.

Examples

data(GERAS)
GERAS.keys  #show the keys
#show the items from the dictionary
psych::lookupFromKeys(GERAS.keys, GERAS.dictionary[,4,drop=FALSE])


#now, use the GERAS.scales to show a scatterHist  plot showing univariate d and bivariate 
# Mahalanobis D.

psych::scatterHist(F ~ M + gender, data=GERAS.scales, cex.point=.3,smooth=FALSE, 
xlab="Masculine Scale",ylab="Feminine Scale",correl=FALSE, 
d.arrow=TRUE,col=c("red","blue"), bg=c("red","blue"), lwd=4, 
title="Combined  M and F scales",cex.cor=2,cex.arrow=1.25, cex.main=2)

---

7 attitude items about Global Warming policy from Erik Nisbet

Description

Erik Nisbet reported the relationship between emotions, ideology, and party affiliation as predictors of attitudes towards government action on climate change. The data were used by Hayes (2013) in a discussion of regression. They are available as the glbwarm data set in the processR package. They are copied here for examples of mediation.

Usage

data("globalWarm")

Format

A data frame with 815 observations on the following 7 variables.

govact

Support for govermment action

posemot

Positive emotions about climate change

negemot

Negative emotions about climate change

ideology

Political ideology (Liberal to conservative)

age

age

sex

female =0, male =1

partyid

Democratic =1, Independent =2, Republican =3

Details

This data set is discussed as an example of regression in Hayes (2013) p 24 - 30 and elsewhere. It is a nice example of moderated regression. It was collected by Erik Nisbet (no citation) who studies communication and the media. E. Nisbet is currently on the faculty at Northwestern School of Communication.

Source

The raw data are available from the processR package (Keon-Woong Moon, 2020) as the glbwarm data set as well as from Hayes' website. The data set is used by Hayes in several examples. Used here by kind permission of Erik Nisbet.

Although the processR package has been removed from CRAN, an earlier version had the data.

References

Hayes, Andrew F. (2013) Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. Guilford Press.

Moon K (2023). processR: Implementation of the 'PROCESS' Macro_. R package version 0.2.8,

Examples

data(globalWarm)
psych::lowerCor(globalWarm)
#compare to Hayes p 254-258
psych::lmCor(govact ~ negemot * age + posemot +ideology+sex,data=globalWarm,std=FALSE)

---

A data.frame of the Galton (1888) height and cubit data set.

Description

Francis Galton introduced the 'co-relation' in 1888 with a paper discussing how to measure the relationship between two variables. His primary example was the relationship between height and forearm length. The data table (cubits) is taken from Galton (1888). Unfortunately, there seem to be some errors in the original data table in that the marginal totals do not match the table.

The data frame, heights, is converted from this table using table2df.

Usage

data(heights)

Format

A data frame with 348 observations on the following 2 variables.

height

Height in inches

cubit

Forearm length in inches

Details

Sir Francis Galton (1888) published the first demonstration of the correlation coefficient. The regression (or reversion to mediocrity) of the height to the length of the left forearm (a cubit) was found to .8. The original table cubits is taken from Galton (1888). There seem to be some errors in the table as published in that the row sums do not agree with the actual row sums. These data are used to create a matrix using table2matrix for demonstrations of analysis and displays of the data.

Source

Galton (1888)

References

Galton, Francis (1888) Co-relations and their measurement. Proceedings of the Royal Society. London Series,45,135-145,

See Also

table2matrix, table2df, cubits, ellipses, galton

Examples

data(heights)
psych::ellipses(heights,n=1,main="Galton's co-relation data set")

---

The raw and transformed data from Holzinger and Swineford, 1939

Description

A classic data set in psychometrics is that from Holzinger and Swineford (1939). A 4 and 5 factor solution to 24 of these variables problem is presented by Harman (1976), and 9 of these are used by the lavaan package. The two data sets were supplied by Keith Widaman.

Usage

data(holzinger.swineford)
     data(holzinger.raw)
     data(holzinger.dictionary)

Format

A data frame with 301 observations on the following 33 variables. Longer descriptions taken from Thompson, (1998).

case

a numeric vector

school

School Pasteur or Grant-White

grade

Grade (7 or 8)

female

male = 1, female = 2

ageyr

age in years

mo

months over year

agemo

Age in months

t01_visperc

Visual perception test from Spearman VPT Part I

t02_cubes

Cubes, Simplification of Brighams Spatial Relations Test

t03_frmbord

Paper formboard-Shapes that can be combined to form a target

t04_lozenges

Lozenges from Thorndike-Shapes flipped over then identify target

t05_geninfo

General Information Verbal Test

t06_paracomp

Paragraph Comprehension Test

t07_sentcomp

Sentence Completion Test

t08_wordclas

Word clasification-Which word not belong in set

t09_wordmean

Word Meaning Test

t10_addition

Speeded addition test

t11_code

Speeded codetest-Transform shapes into alpha with code

t12_countdot

Speeded counting of dots in shap

t13_sccaps

Speeded discrimation of straight and curved caps

t14_wordrecg

Memory of Target Words

t15_numbrecg

Memory of Target Numbers

t16_figrrecg

Memory of Target Shapes

t17_objnumb

Memory of object-Number association targets

t18_numbfig

Memory of number-Object association targets

t19_figword

Memory of figure-Word association target

t20_deduction

Deductive Math Ability

t21_numbpuzz

Math number puzzles

t22_probreas

Math word problem reasoning

t23_series

Completion of a Math Number Series

t24_woody

Woody-McCall mixed math fundamentals test

t25_frmbord2

Revision of t3-Paper form board

t26_flags

Flags-possible substitute for t4 lozenges

Details

The following commentary was provided by Keith Widaman:

“The Holzinger and Swineford (1939) data have been used as a model data set by many investigators. For example, Harman (1976) used the “24 Psychological Variables" example prominently in his authoritative text on multiple factor analysis, and the data presented under this rubric consisted of 24 of the variables from the Grant-White school (N = 145). Meredith (1964a, 1964b) used several variables from the Holzinger and Swineford study in his work on factorial invariance under selection. Joreskog (1971) based his work on multiple-group confirmatory factor analysis using the Holzinger and Swineford data, subsetting the data into four groups.

Rosseel, who developed the ‘lavaan’ package for R, included 9 of the manifest variables from Holzinger and Swineford (1939) as a “resident" data set when one downloads the ‘lavaan’ package. Several background variables are included in this “resident" data set in addition to 9 of the psychological tests (which are named x1 – x9 in the data set). When analyzing these data, I found the distributions of the variables (means, SDs) did not match the sample statistics from the original article. For example, in the “resident" data set in ‘lavaan’, scores on all manifest variables ranged between 0 and 10, sample means varied between 3 and 6, and sample SDs varied between 1.0 and 1.5. In the original data set, scores ranges were rather different across tests, with some variables having scores that ranged between 0 and 20, but other manifest variables having scores ranging from 50 to over 300 – with obvious attendant differences in sample means and SDs.

After a bit of snooping (i.e., data analysis), I discovered that the 9 variables in the “resident" data set in ‘lavaan’ had been rescored through ratio transformations. The ratio transformations involved dividing the raw score for each person on a given test by a particular constant for that test that transformed scores on the test to have the desired range.

I decided to perform transformations of all 26 variables so that two data sets could be available to interested researchers:"

holzinger.raw are the raws scores on all variables from Holzinger & Swineford (1939)

holzinger.swineford are rescaled scores on all variables from Holzinger & Swineford.

holzinger.dictionary is a list of the variable names in short and long form.

... Widaman continues:

“As several persons have noted, Harman (1976) used data only from the Grant-White school (N = 145) for his 24 Psychological Variables data set. In doing so, Harman replaced t03_frmbord and t04_lozenges with t25_frmbord2 and t26_flags, because the latter two tests were experimental tests that were designed to be more appropriate for this age level. This substitution is fine, as long as one analyzes data from only the Grant- White school. If one wishes to perform multiple-group analyses and uses school as a grouping variable (as Meredith, 1964a, 1964b, and Joreskog, 1971, did), then tests 25 and 26 should not be used."

“As have others, Gorsuch (1983) mentioned that analyses based on the raw data reported by Holzinger and Swineford (1939) will not produce statistics (means, SDs, correlations) that match precisely the values reported by Holzinger and Swineford or Harman (1976). Following Gorsuch, I have assumed that the raw data are correct. Applying factor analytic techniques to the raw data from the Grant-White school and to the summary data reported by Harman (1976) will produce slightly different results, but results that differ in only minor, unimportant details."

These data are interesting not just for the historical completeness of having the original data, but also as an example of suppressor variables. Age and grade are positively correlated, and scores are higher in the 8th grade than in the 7th grade. But age (particularly in months) is negatively correlated with many of the cognitive tasks, and when grade and age are both entered into regression, this negative correlation is enhanced. That is, although increasing grade increases cognitive performance, younger children in both grades do better than the older children.

Note

As discussed by Widaman, the descriptive values reported in Harman (1967) (p 124) do not quite match the descriptive statistics in holzinger.raw. Further note that the correlation matrix and factor loadings are trivially different from the Harman.24 factor loadings in the GPA rotation package.

The purpose behind presenting both the raw and transformed data is to show that the fit statistics from factor analysis are identical for these two data sets.

The variables v1 ... v9 in the lavaan package correspond to tests 1, 2, 4, 6, 7, 9, 10, 12 and 13.

Source

Keith Widaman (2019, personal communication). Original data from Holzinger and Swineford (1939).

References

Gorsuch, R. L. (1983). Factor analysis (2nd ed.). Hillsdale, NJ: Erlbaum.

Harman, Harry Horace (1967), Modern factor analysis. Chicago, University of Chicago Press.

Holzinger, K. J., & Swineford, F. (1939). A study in factor analysis: The stability of a bi-factor solution. Supplementary Educational Monographs, no. 48. Chicago: University of Chicago, Department of Education.

Joreskog, K. G. (1971). Simultaneous factor analysis in several populations. Psychometrika, 36, 409-426.

Meredith, W. (1964a). Notes on factorial invariance. Psychometrika, 29, 177-185.

Meredith, W. (1964b). Rotation to achieve factorial invariance. Psychometrika, 29, 177-206.

Meredith, W. (1977). On weighted Procrustes and hyperplane fitting in factor analytic rotation. Psychometrika, 42, 491-522.

Thompson, Bruce. Five Methodology Errors in Educational Research:The Pantheon of Statistical Significance and Other Faux Pas. Paper presented at the Annual Meeting of the American Educational Research Association(San Diego, CA, April 13-17,1998)

See Also

psych::Holzinger

Examples

data(holzinger.raw)
psych::describe(holzinger.raw)
data(holzinger.dictionary)
holzinger.dictionary  #to see the longer names for these data (taken from Thompson)

#Compare these to the lavaan correlation matrix
psych::lowerCor(holzinger.swineford[ 7+ c(1, 2, 4, 6, 7, 9, 10, 12,  13)])

psych::lmCor(t01_visperc + t05_geninfo + t08_wordclas ~ grade + agemo,data = holzinger.raw)
psych::lmCor( t06_paracomp ~ grade + agemo, data=holzinger.swineford)
psych::mediate(t06_paracomp  ~ grade + (agemo),data = holzinger.raw,std=TRUE)

#show the omega structure of the 24 variables
 om4 <- psych::omega(holzinger.swineford[8:31],4)
psych::omega.diagram(om4,sl=FALSE,main="26 variables from Holzinger-Swineford")

#these data also show an interesting suppression effect

psych::lowerCor(holzinger.swineford[c(3,7,12:14)])
psych::lmCor( t06_paracomp ~ grade + agemo, data=holzinger.swineford)
#or show as a mediation effect
mod <- psych::mediate(t06_paracomp  ~ grade + (agemo),data = holzinger.raw,std=TRUE,n.iter=50)
summary(mod)

#now, show a plot of these effets
plot(t07_sentcomp ~ agemo, col=c("red","blue")[holzinger.swineford$grade -6],
  pch=26-holzinger.swineford$grade,data=holzinger.swineford,
   ylab="Sentence Comprehension",xlab="Age in Months",
   main="Sentence Comprehension varies by age and grade")
   #we use lmCor to figure out the lines 
   #note that we need to not plot the default graph
by(holzinger.swineford,holzinger.swineford$grade -6,function(x) abline(
     psych::lmCor(t07_sentcomp ~ agemo, data=x, std=FALSE, plot=FALSE), 
     lty=c("dashed","solid")[x$grade-6]))
text(190,3.3,"grade = 8")
text(190,2,"grade = 7")

---

US family income from US census 2008

Description

US census data on family income from 2008

Usage

data(income)

Format

A data frame with 44 observations on the following 4 variables.

value

lower boundary of the income group

count

Number of families within that income group

mean

Mean of the category

prop

proportion of families

Details

The distribution of income is a nice example of a log normal distribution. It is also an interesting example of the power of graphics. It is quite clear when graphing the data that income statistics are bunched to the nearest 5K. That is, there is a clear sawtooth pattern in the data.

The all.income set is interpolates intervening values for 100-150K, 150-200K and 200-250K

Source

US Census: Table HINC-06. Income Distribution to $250,000 or More for Households: 2008

https://www.census.gov/hhes/www/cpstables/032009/hhinc/new06_000.htm

Examples

data(income)
with(income[1:40,], plot(mean,prop, main="US family income for 2008",xlab="income", 
        ylab="Proportion of families",xlim=c(0,100000)))
with (income[1:40,], points(lowess(mean,prop,f=.3),typ="l"))
psych::describe(income)


with(all.income, plot(mean,prop, main="US family income for 2008",xlab="income", 
                ylab="Proportion of families",xlim=c(0,250000)))
with (all.income[1:50,], points(lowess(mean,prop,f=.25),typ="l"))

---

16 multiple choice IQ items

Description

16 multiple choice ability items taken from the Synthetic Aperture Personality Assessment (SAPA) web based personality assessment project. The data from 1525 subjects are included here as a demonstration set for scoring multiple choice inventories and doing basic item statistics. For more information on the development of an open source measure of cognitive ability, consult the readings available at the https://personality-project.org/.

Usage

data(iqitems)

Format

A data frame with 1525 observations on the following 16 variables. The number following the name is the item number from SAPA.

reason.4

Basic reasoning questions

reason.16

Basic reasoning question

reason.17

Basic reasoning question

reason.19

Basic reasoning question

letter.7

In the following alphanumeric series, what letter comes next?

letter.33

In the following alphanumeric series, what letter comes next?

letter.34

In the following alphanumeric series, what letter comes next

letter.58

In the following alphanumeric series, what letter comes next?

matrix.45

A matrix reasoning task

matrix.46

A matrix reasoning task

matrix.47

A matrix reasoning task

matrix.55

A matrix reasoning task

rotate.3

Spatial Rotation of type 1.2

rotate.4

Spatial Rotation of type 1.2

rotate.6

Spatial Rotation of type 1.1

rotate.8

Spatial Rotation of type 2.3

Details

16 items were sampled from 80 items given as part of the SAPA (https://www.sapa-project.org/) project (Revelle, Wilt and Rosenthal, 2009; Condon and Revelle, 2014) to develop online measures of ability. These 16 items reflect four lower order factors (verbal reasoning, letter series, matrix reasoning, and spatial rotations. These lower level factors all share a higher level factor ('g'). Similar data are available from the International Cognitive Abiity Resource at https://www.icar-project.org/ .

This data set and the associated data set (ability based upon scoring these multiple choice items and converting them to correct/incorrect may be used to demonstrate item response functions, tetrachoric correlations, or irt.fa as well as omega estimates of of reliability and hierarchical structure.

In addition, the data set is a good example of doing item analysis to examine the empirical response probabilities of each item alternative as a function of the underlying latent trait. When doing this, it appears that two of the matrix reasoning problems do not have monotonically increasing trace lines for the probability correct. At moderately high ability (theta = 1) there is a decrease in the probability correct from theta = 0 and theta = 2.

Source

The example data set is taken from the Synthetic Aperture Personality Assessment personality and ability test at https://www.sapa-project.org/. The data were collected with David Condon from 8/08/12 to 8/31/12.

References

Condon, David and Revelle, William, (2014) The International Cognitive Ability Resource: Development and initial validation of a public-domain measure. Intelligence, 43, 52-64.

Revelle, William, Dworak, Elizabeth M. and Condon, David (2020) Cognitive ability in everyday life: the utility of open-source measures. Current Directions in Psychological Science, 29, (4) 358-363. Open access at doi:10.1177/0963721420922178.

Dworak, Elizabeth M., Revelle, William, Doebler, Philip and Condon, David (2021) Using the International Cognitive Ability Resource as an open source tool to explore individual differences in cognitive ability. Personality and Individual Differences, 169. Open access at doi:10.1016/j.paid.2020.109906.

Revelle, W., Wilt, J., and Rosenthal, A. (2010) Individual Differences in Cognition: New Methods for examining the Personality-Cognition Link In Gruszka, A. and Matthews, G. and Szymura, B. (Eds.) Handbook of Individual Differences in Cognition: Attention, Memory and Executive Control, Springer.

Revelle, W, Condon, D.M., Wilt, J., French, J.A., Brown, A., and Elleman, L.G. (2016) Web and phone based data collection using planned missing designs. In Fielding, N.G., Lee, R.M. and Blank, G. (Eds). SAGE Handbook of Online Research Methods (2nd Ed), Sage Publcations.

Examples

data(iqitems)
iq.keys <- c(4,4,4, 6,  6,3,4,4,   5,2,2,4,   3,2,6,7)
psych::score.multiple.choice(iq.keys,iqitems)   #this just gives summary statisics
#convert them to true false 
iq.scrub <- psych::scrub(iqitems,isvalue=0)  #first get rid of the zero responses
iq.tf <-  psych::score.multiple.choice(iq.keys,iq.scrub,score=FALSE) 
              #convert to wrong (0) and correct (1) for analysis
psych::describe(iq.tf) 
#see the ability data set for these analyses
#now, for some item analysis
iq.irt <- psych::irt.fa(iq.tf)  #do a basic irt
iq.sc <- psych::scoreIrt(iq.irt,iq.tf)  #find the scores
op <- par(mfrow=c(4,4))
psych::irt.responses(iq.sc[,1], iq.tf)  
op <- par(mfrow=c(1,1))

---

75 mood items from the Motivational State Questionnaire for 3896 participants

Description

Emotions may be described either as discrete emotions or in dimensional terms. The Motivational State Questionnaire (MSQ) was developed to study emotions in laboratory and field settings. The data can be well described in terms of a two dimensional solution of energy vs tiredness and tension versus calmness. Additional items include what time of day the data were collected and a few personality questionnaire scores.

Usage

data(msq)

Format

A data frame with 3896 observations on the following 92 variables.

active

a numeric vector

afraid

a numeric vector

alert

a numeric vector

angry

a numeric vector

anxious

a numeric vector

aroused

a numeric vector

ashamed

a numeric vector

astonished

a numeric vector

at.ease

a numeric vector

at.rest

a numeric vector

attentive

a numeric vector

blue

a numeric vector

bored

a numeric vector

calm

a numeric vector

cheerful

a numeric vector

clutched.up

a numeric vector

confident

a numeric vector

content

a numeric vector

delighted

a numeric vector

depressed

a numeric vector

determined

a numeric vector

distressed

a numeric vector

drowsy

a numeric vector

dull

a numeric vector

elated

a numeric vector

energetic

a numeric vector

enthusiastic

a numeric vector

excited

a numeric vector

fearful

a numeric vector

frustrated

a numeric vector

full.of.pep

a numeric vector

gloomy

a numeric vector

grouchy

a numeric vector

guilty

a numeric vector

happy

a numeric vector

hostile

a numeric vector

idle

a numeric vector

inactive

a numeric vector

inspired

a numeric vector

intense

a numeric vector

interested

a numeric vector

irritable

a numeric vector

jittery

a numeric vector

lively

a numeric vector

lonely

a numeric vector

nervous

a numeric vector

placid

a numeric vector

pleased

a numeric vector

proud

a numeric vector

quiescent

a numeric vector

quiet

a numeric vector

relaxed

a numeric vector

sad

a numeric vector

satisfied

a numeric vector

scared

a numeric vector

serene

a numeric vector

sleepy

a numeric vector

sluggish

a numeric vector

sociable

a numeric vector

sorry

a numeric vector

still

a numeric vector

strong

a numeric vector

surprised

a numeric vector

tense

a numeric vector

tired

a numeric vector

tranquil

a numeric vector

unhappy

a numeric vector

upset

a numeric vector

vigorous

a numeric vector

wakeful

a numeric vector

warmhearted

a numeric vector

wide.awake

a numeric vector

alone

a numeric vector

kindly

a numeric vector

scornful

a numeric vector

EA

Thayer's Energetic Arousal Scale

TA

Thayer's Tense Arousal Scale

PA

Positive Affect scale

NegAff

Negative Affect scale

Extraversion

Extraversion from the Eysenck Personality Inventory

Neuroticism

Neuroticism from the Eysenck Personality Inventory

Lie

Lie from the EPI

Sociability

The sociability subset of the Extraversion Scale

Impulsivity

The impulsivity subset of the Extraversions Scale

MSQ_Time

Time of day the data were collected

MSQ_Round

Rounded time of day

TOD

a numeric vector

TOD24

a numeric vector

ID

subject ID

condition

What was the experimental condition after the msq was given

scale

a factor with levels msq r original or revised msq

exper

Which study were the data collected: a factor with levels AGES BING BORN CART CITY COPE EMIT FAST Fern FILM FLAT Gray imps item knob MAPS mite pat-1 pat-2 PATS post RAFT Rim.1 Rim.2 rob-1 rob-2 ROG1 ROG2 SALT sam-1 sam-2 SAVE/PATS sett swam swam-2 TIME VALE-1 VALE-2 VIEW

Details

The Motivational States Questionnaire (MSQ) is composed of 72 items, which represent the full affective space (Revelle & Anderson, 1998). The MSQ consists of 20 items taken from the Activation-Deactivation Adjective Check List (Thayer, 1986), 18 from the Positive and Negative Affect Schedule (PANAS, Watson, Clark, & Tellegen, 1988) along with the items used by Larsen and Diener (1992). The response format was a four-point scale that corresponds to Russell and Carroll's (1999) "ambiguous–likely-unipolar format" and that asks the respondents to indicate their current standing (“at this moment") with the following rating scale:
0—————-1—————-2—————-3
Not at all A little Moderately Very much

The original version of the MSQ included 70 items. Intermediate analyses (done with 1840 subjects) demonstrated a concentration of items in some sections of the two dimensional space, and a paucity of items in others. To begin correcting this, 3 items from redundantly measured sections (alone, kindly, scornful) were removed, and 5 new ones (anxious, cheerful, idle, inactive, and tranquil) were added. Thus, the correlation matrix is missing the correlations between items anxious, cheerful, idle, inactive, and tranquil with alone, kindly, and scornful.

Procedure. The data were collected over nine years, as part of a series of studies examining the effects of personality and situational factors on motivational state and subsequent cognitive performance. In each of 38 studies, prior to any manipulation of motivational state, participants signed a consent form and filled out the MSQ. (The procedures of the individual studies are irrelevant to this data set and could not affect the responses to the MSQ, since this instrument was completed before any further instructions or tasks). Some MSQ post test (after manipulations) is available in affect.

The EA and TA scales are from Thayer, the PA and NA scales are from Watson et al. (1988). Scales and items:

Energetic Arousal: active, energetic, vigorous, wakeful, wide.awake, full.of.pep, lively, -sleepy, -tired, - drowsy (ADACL)

Tense Arousal: Intense, Jittery, fearful, tense, clutched up, -quiet, -still, - placid, - calm, -at rest (ADACL)

Positive Affect: active, alert, attentive, determined, enthusiastic, excited, inspired, interested, proud, strong (PANAS)

Negative Affect: afraid, ashamed, distressed, guilty, hostile, irritable , jittery, nervous, scared, upset (PANAS)

The PA and NA scales can in turn can be thought of as having subscales: (See the PANAS-X) Fear: afraid, scared, nervous, jittery (not included frightened, shaky) Hostility: angry, hostile, irritable, (not included: scornful, disgusted, loathing guilt: ashamed, guilty, (not included: blameworthy, angry at self, disgusted with self, dissatisfied with self) sadness: alone, blue, lonely, sad, (not included: downhearted) joviality: cheerful, delighted, energetic, enthusiastic, excited, happy, lively, (not included: joyful) self-assurance: proud, strong, confident, (not included: bold, daring, fearless ) attentiveness: alert, attentive, determined (not included: concentrating)

The next set of circumplex scales were taken (I think) from Larsen and Diener (1992). High activation: active, aroused, surprised, intense, astonished Activated PA: elated, excited, enthusiastic, lively Unactivated NA : calm, serene, relaxed, at rest, content, at ease PA: happy, warmhearted, pleased, cheerful, delighted Low Activation: quiet, inactive, idle, still, tranquil Unactivated PA: dull, bored, sluggish, tired, drowsy NA: sad, blue, unhappy, gloomy, grouchy Activated NA: jittery, anxious, nervous, fearful, distressed.

Keys for these separate scales are shown in the examples.

In addition to the MSQ, there are 5 scales from the Eysenck Personality Inventory (Extraversion, Impulsivity, Sociability, Neuroticism, Lie). The Imp and Soc are subsets of the the total extraversion scale.

Source

Data collected at the Personality, Motivation, and Cognition Laboratory, Northwestern University.

References

Larsen, R. J., & Diener, E. (1992). Promises and problems with the circumplex model of emotion. In M. S. Clark (Ed.), Review of personality and social psychology, No. 13. Emotion (pp. 25-59). Thousand Oaks, CA, US: Sage Publications, Inc.

Rafaeli, Eshkol and Revelle, William (2006), A premature consensus: Are happiness and sadness truly opposite affects? Motivation and Emotion, 30, 1, 1-12.

Revelle, W. and Anderson, K.J. (1998) Personality, motivation and cognitive performance: Final report to the Army Research Institute on contract MDA 903-93-K-0008. (https://www.personality-project.org/revelle/publications/ra.ari.98.pdf).

Thayer, R.E. (1989) The biopsychology of mood and arousal. Oxford University Press. New York, NY.

Watson,D., Clark, L.A. and Tellegen, A. (1988) Development and validation of brief measures of positive and negative affect: The PANAS scales. Journal of Personality and Social Psychology, 54(6):1063-1070.

See Also

msqR for a larger data set with repeated measures for 3032 participants measured at least once, 2753 measured twice, 446 three times and 181 four times. affect for an example of the use of some of these adjectives in a mood manipulation study.

make.keys, scoreItems and scoreOverlap for instructions on how to score multiple scales with and without item overlap. Also see fa and fa.extension for instructions on how to do factor analyses or factor extension.

Examples

data(msq)
   #in in the interests of time
#basic descriptive statistics
psych::describe(msq)

#score them for 20 short scales -- note that these have item overlap
#The first 2 are from Thayer
#The next 2 are classic positive and negative affect
#The next 9 are circumplex scales
#the last 7 are msq estimates of PANASX scales (missing some items)
keys.list <- list(
EA = c("active", "energetic", "vigorous", "wakeful", "wide.awake", "full.of.pep",
       "lively", "-sleepy", "-tired", "-drowsy"),
TA =c("intense", "jittery", "fearful", "tense", "clutched.up", "-quiet", "-still", 
       "-placid", "-calm", "-at.rest") ,
PA =c("active", "excited", "strong", "inspired", "determined", "attentive", 
          "interested", "enthusiastic", "proud", "alert"),
NAf =c("jittery", "nervous", "scared", "afraid", "guilty", "ashamed", "distressed",  
         "upset", "hostile", "irritable" ),
HAct = c("active", "aroused", "surprised", "intense", "astonished"),
aPA = c("elated", "excited", "enthusiastic", "lively"),
uNA = c("calm", "serene", "relaxed", "at.rest", "content", "at.ease"),
pa = c("happy", "warmhearted", "pleased", "cheerful", "delighted" ),
LAct = c("quiet", "inactive", "idle", "still", "tranquil"),
uPA =c( "dull", "bored", "sluggish", "tired", "drowsy"),
naf = c( "sad", "blue", "unhappy", "gloomy", "grouchy"),
aNA = c("jittery", "anxious", "nervous", "fearful", "distressed"),
Fear = c("afraid" , "scared" , "nervous" , "jittery" ) ,
Hostility = c("angry" ,  "hostile", "irritable", "scornful" ), 
Guilt = c("guilty" , "ashamed" ),
Sadness = c( "sad"  , "blue" , "lonely",  "alone" ),
Joviality =c("happy","delighted", "cheerful", "excited", "enthusiastic", "lively", "energetic"), 
Self.Assurance=c( "proud","strong" , "confident" , "-fearful" ),
Attentiveness = c("alert" , "determined" , "attentive" )
#, acquiscence = c("sleepy" ,  "wakeful" ,  "relaxed","tense")   
#dropped because it has a negative alpha and throws warnings
   )
       
msq.scores <- psych::scoreItems(keys.list,msq)

#show a circumplex structure for the non-overlapping items
fcirc <- psych::fa(msq.scores$scores[,5:12],2)  
psych::fa.plot(fcirc,labels=colnames(msq.scores$scores)[5:12])

#now, find the correlations corrected for item overlap
msq.overlap <- psych::scoreOverlap(keys.list,msq)
#a warning is thrown by smc  because of some NAs in the matrix

f2 <- psych::fa(msq.overlap$cor,2)
psych::fa.plot(f2,labels=colnames(msq.overlap$cor),
      title="2 dimensions of affect, corrected for overlap")

#extend this solution to EA/TA  NA/PA space
fe  <- psych::fa.extension(cor(msq.scores$scores[,5:12],msq.scores$scores[,1:4]),fcirc)
psych::fa.diagram(fcirc,fe=fe,
          main="Extending the circumplex structure to  EA/TA and PA/NA ")

#show the 2 dimensional structure
f2 <- psych::fa(msq[1:72],2)
psych::fa.plot(f2,labels=colnames(msq)[1:72],
     title="2 dimensions of affect at the item level",cex=.5)

#sort them by polar coordinates
round(psych::polar(f2),2)

---

75 mood items from the Motivational State Questionnaire for 3032 unique participants

Description

Emotions may be described either as discrete emotions or in dimensional terms. The Motivational State Questionnaire (MSQ) was developed to study emotions in laboratory and field settings. The data can be well described in terms of a two dimensional solution of energy vs tiredness and tension versus calmness. Alternatively, this space can be organized by the two dimensions of Positive Affect and Negative Affect. Additional items include what time of day the data were collected and a few personality questionnaire scores. 3032 unique participants took the MSQ at least once, 2753 at least twice, 446 three times, and 181 four times. The 3032 participants also took the sai state anxiety inventory at the same time. Some studies manipulated arousal by caffeine, others manipulations included affect inducing movies.

Usage

data("msqR")

Format

A data frame with 6411 observations on the following 88 variables.

active

a numeric vector

afraid

a numeric vector

alert

a numeric vector

alone

a numeric vector

angry

a numeric vector

aroused

a numeric vector

ashamed

a numeric vector

astonished

a numeric vector

at.ease

a numeric vector

at.rest

a numeric vector

attentive

a numeric vector

blue

a numeric vector

bored

a numeric vector

calm

a numeric vector

clutched.up

a numeric vector

confident

a numeric vector

content

a numeric vector

delighted

a numeric vector

depressed

a numeric vector

determined

a numeric vector

distressed

a numeric vector

drowsy

a numeric vector

dull

a numeric vector

elated

a numeric vector

energetic

a numeric vector

enthusiastic

a numeric vector

excited

a numeric vector

fearful

a numeric vector

frustrated

a numeric vector

full.of.pep

a numeric vector

gloomy

a numeric vector

grouchy

a numeric vector

guilty

a numeric vector

happy

a numeric vector

hostile

a numeric vector

inspired

a numeric vector

intense

a numeric vector

interested

a numeric vector

irritable

a numeric vector

jittery

a numeric vector

lively

a numeric vector

lonely

a numeric vector

nervous

a numeric vector

placid

a numeric vector

pleased

a numeric vector

proud

a numeric vector

quiescent

a numeric vector

quiet

a numeric vector

relaxed

a numeric vector

sad

a numeric vector

satisfied

a numeric vector

scared

a numeric vector

serene

a numeric vector

sleepy

a numeric vector

sluggish

a numeric vector

sociable

a numeric vector

sorry

a numeric vector

still

a numeric vector

strong

a numeric vector

surprised

a numeric vector

tense

a numeric vector

tired

a numeric vector

unhappy

a numeric vector

upset

a numeric vector

vigorous

a numeric vector

wakeful

a numeric vector

warmhearted

a numeric vector

wide.awake

a numeric vector

anxious

a numeric vector

cheerful

a numeric vector

idle

a numeric vector

inactive

a numeric vector

tranquil

a numeric vector

kindly

a numeric vector

scornful

a numeric vector

Extraversion

Extraversion from the EPI

Neuroticism

Neuroticism from the EPI

Lie

Lie from the EPI

Sociability

Sociability from the EPI

Impulsivity

Impulsivity from the EPI

gender

1= male, 2 = female (coded on presumed x chromosome). Slowly being added to the data set.

TOD

Time of day that the study was run

drug

1 if given placebo, 2 if given caffeine

film

1-4 if given a film: 1=Frontline, 2= Halloween, 3=Serengeti, 4 = Parenthood

time

Measurement occasion (1 and 2 are same session, 3 and 4 are the same, but a later session)

id

a numeric vector

form

msq versus msqR

study

a character vector of the experiment name

Details

The Motivational States Questionnaire (MSQ) is composed of 75 items, which represent the full affective space (Revelle & Anderson, 1998). The MSQ consists of 20 items taken from the Activation-Deactivation Adjective Check List (Thayer, 1986), 18 from the Positive and Negative Affect Schedule (PANAS, Watson, Clark, & Tellegen, 1988) along with the affective circumplex items used by Larsen and Diener (1992). The response format was a four-point scale that corresponds to Russell and Carroll's (1999) "ambiguous–likely-unipolar format" and that asks the respondents to indicate their current standing (“at this moment") with the following rating scale:
0—————-1—————-2—————-3
Not at all A little Moderately Very much

The original version of the MSQ included 70 items. Intermediate analyses (done with 1840 subjects) demonstrated a concentration of items in some sections of the two dimensional space, and a paucity of items in others. To begin correcting this, 3 items from redundantly measured sections (alone, kindly, scornful) were removed, and 5 new ones (anxious, cheerful, idle, inactive, and tranquil) were added. Thus, the correlation matrix is missing the correlations between items anxious, cheerful, idle, inactive, and tranquil with alone, kindly, and scornful.

2605 individuals took Form 1 version, 3806 the Form 2 version. 3032 people (1218 form 1, 1814 form 2) took the MSQ at least once. 2086 at least twice, 1112 three times, and 181 four times.

To see the relative frequencies by time and form, see the first example.

Procedure. The data were collected over nine years in the Personality, Motivation and Cognition laboratory at Northwestern, as part of a series of studies examining the effects of personality and situational factors on motivational state and subsequent cognitive performance. In each of 38 studies, prior to any manipulation of motivational state, participants signed a consent form and in some studies, consumed 0 or 4mg/kg of caffeine. In caffeine studies, they waited 30 minutes and then filled out the MSQ. (Normally, the procedures of the individual studies are irrelevant to this data set and could not affect the responses to the MSQ at time 1, since this instrument was completed before any further instructions or tasks. However, caffeine does have an effect.) The MSQ post test following a movie manipulation) is available in affect as well as here.

The XRAY study crossed four movie conditions with caffeine. The first MSQ measures are showing the effects of the movies and caffeine, but after an additional 30 minutes, the second MSQ seems to mainly show the caffeine effects. The movies were 9 minute clips from 1) a BBC documentary on British troops arriving at the Bergen-Belsen concentration camp (sad); 2) an early scene from Halloween in which the heroine runs around shutting doors and windows (terror); 3) a documentary about lions on the Serengeti plain, and 4) the "birthday party" scene from Parenthood.

The FLAT study measured affect before, immediately after, and then after 30 minutes following a movie manipulation. See the affect data set.

To see which studies used which conditions, see the second and third examples.

The EA and TA scales are from Thayer, the PA and NA scales are from Watson et al. (1988). Scales and items:

Energetic Arousal: active, energetic, vigorous, wakeful, wide.awake, full.of.pep, lively, -sleepy, -tired, - drowsy (ADACL)

Tense Arousal: Intense, Jittery, fearful, tense, clutched up, -quiet, -still, - placid, - calm, -at rest (ADACL)

Positive Affect: active, alert, attentive, determined, enthusiastic, excited, inspired, interested, proud, strong (PANAS)

Negative Affect: afraid, ashamed, distressed, guilty, hostile, irritable , jittery, nervous, scared, upset (PANAS)

The PA and NA scales can in turn can be thought of as having subscales: (See the PANAS-X) Fear: afraid, scared, nervous, jittery (not included frightened, shaky) Hostility: angry, hostile, irritable, (not included: scornful, disgusted, loathing guilt: ashamed, guilty, (not included: blameworthy, angry at self, disgusted with self, dissatisfied with self) sadness: alone, blue, lonely, sad, (not included: downhearted) joviality: cheerful, delighted, energetic, enthusiastic, excited, happy, lively, (not included: joyful) self-assurance: proud, strong, confident, (not included: bold, daring, fearless ) attentiveness: alert, attentive, determined (not included: concentrating)

The next set of circumplex scales were taken from Larsen and Diener (1992). High activation: active, aroused, surprised, intense, astonished Activated PA: elated, excited, enthusiastic, lively Unactivated NA : calm, serene, relaxed, at rest, content, at ease PA: happy, warmhearted, pleased, cheerful, delighted Low Activation: quiet, inactive, idle, still, tranquil Unactivated PA: dull, bored, sluggish, tired, drowsy NA: sad, blue, unhappy, gloomy, grouchy Activated NA: jittery, anxious, nervous, fearful, distressed.

Keys for these separate scales are shown in the examples.

In addition to the MSQ, there are 5 scales from the Eysenck Personality Inventory (Extraversion, Impulsivity, Sociability, Neuroticism, Lie). The Imp and Soc are subsets of the the total extraversion scale based upon a reanalysis of the EPI by Rocklin and Revelle (1983). This information is in the msq data set as well.

Note

In December, 2018 the caffeine, film and personality conditions were added. In the process of doing so, it was discovered that the EMIT data had been incorrectly entered. This has been fixed.

Source

Data collected at the Personality, Motivation, and Cognition Laboratory, Northwestern University.

References

Larsen, R. J., & Diener, E. (1992). Promises and problems with the circumplex model of emotion. In M. S. Clark (Ed.), Review of personality and social psychology, No. 13. Emotion (pp. 25-59). Thousand Oaks, CA, US: Sage Publications, Inc.

Rafaeli, Eshkol and Revelle, William (2006), A premature consensus: Are happiness and sadness truly opposite affects? Motivation and Emotion, 30, 1, 1-12.

Revelle, W. and Anderson, K.J. (1998) Personality, motivation and cognitive performance: Final report to the Army Research Institute on contract MDA 903-93-K-0008. (https://www.personality-project.org/revelle/publications/ra.ari.98.pdf).

Smillie, Luke D. and Cooper, Andrew and Wilt, Joshua and Revelle, William (2012) Do Extraverts Get More Bang for the Buck? Refining the Affective-Reactivity Hypothesis of Extraversion. Journal of Personality and Social Psychology, 103 (2), 206-326.

Thayer, R.E. (1989) The biopsychology of mood and arousal. Oxford University Press. New York, NY.

Watson,D., Clark, L.A. and Tellegen, A. (1988) Development and validation of brief measures of positive and negative affect: The PANAS scales. Journal of Personality and Social Psychology, 54(6):1063-1070.

See Also

msq for 3896 participants with scores on five scales of the EPI. affect for an example of the use of some of these adjectives in a mood manipulation study.

make.keys, scoreItems and scoreOverlap for instructions on how to score multiple scales with and without item overlap. Also see fa and fa.extension for instructions on how to do factor analyses or factor extension.

Given the temporal ordering of the sai data and the msqR data, these data are useful for demonstrations of testRetest reliability. See the examples in testRetest for how to combine the sai tai and msqR datasets.

Examples

data(msqR)
table(msqR$form,msqR$time) #which forms?
table(msqR$study,msqR$drug) #Drug studies
table(msqR$study,msqR$film) #Film studies
table(msqR$study,msqR$TOD) #To examine time of day


#score them for 20 short scales -- note that these have item overlap
#The first 2 are from Thayer
#The next 2 are classic positive and negative affect 
#The next 9 are circumplex scales
#the last 7 are msq estimates of PANASX scales (missing some items)
keys.list <- list(
EA = c("active", "energetic", "vigorous", "wakeful", "wide.awake", "full.of.pep",
       "lively", "-sleepy", "-tired", "-drowsy"),
TA =c("intense", "jittery", "fearful", "tense", "clutched.up", "-quiet", "-still", 
       "-placid", "-calm", "-at.rest") ,
PA =c("active", "excited", "strong", "inspired", "determined", "attentive", 
          "interested", "enthusiastic", "proud", "alert"),
NAf =c("jittery", "nervous", "scared", "afraid", "guilty", "ashamed", "distressed",  
         "upset", "hostile", "irritable" ),
HAct = c("active", "aroused", "surprised", "intense", "astonished"),
aPA = c("elated", "excited", "enthusiastic", "lively"),
uNA = c("calm", "serene", "relaxed", "at.rest", "content", "at.ease"),
pa = c("happy", "warmhearted", "pleased", "cheerful", "delighted" ),
LAct = c("quiet", "inactive", "idle", "still", "tranquil"),
uPA =c( "dull", "bored", "sluggish", "tired", "drowsy"),
naf = c( "sad", "blue", "unhappy", "gloomy", "grouchy"),
aNA = c("jittery", "anxious", "nervous", "fearful", "distressed"),
Fear = c("afraid" , "scared" , "nervous" , "jittery" ) ,
Hostility = c("angry" ,  "hostile", "irritable", "scornful" ), 
Guilt = c("guilty" , "ashamed" ),
Sadness = c( "sad"  , "blue" , "lonely",  "alone" ),
Joviality =c("happy","delighted", "cheerful", "excited", "enthusiastic", "lively", "energetic"), 
Self.Assurance=c( "proud","strong" , "confident" , "-fearful" ),
Attentiveness = c("alert" , "determined" , "attentive" ))

#acquiscence = c("sleepy" ,  "wakeful" ,  "relaxed","tense"))
#Yik Russell and Steiger list the following items
Yik.keys <- list(
pleasure =psych::cs(happy,content,satisfied, pleased),
act.pleasure =psych::cs(proud,enthusiastic,euphoric),
pleasant.activation = psych::cs(energetic,full.of.pep,excited,wakeful,attentive,
   wide.awake,active,alert,vigorous),
activation = psych::cs(aroused,hyperactivated,intense),
unpleasant.act = psych::cs(anxious,frenzied,jittery,nervous),
activated.displeasure =psych::cs(scared,upset,shaky,fearful,clutched.up,tense,
    ashamed,guilty,agitated,hostile),
displeaure =psych::cs(troubled,miserable,unhappy,dissatisfied),
Ueactivated.Displeasure = psych::cs(sad,down,gloomy,blue,melancholy),
Unpleasant.Deactivation = psych::cs(droopy,drowsy,dull,bored,sluggish,tired),
Deactivation =psych::cs( quiet,still),
pleasant.deactivation = psych::cs(placid,relaxed,tranquil, at.rest,calm),
deactived.pleasure =psych::cs( serene,soothed,peaceful,at.ease,secure)
)

#of these 60 items, 46 appear in the msqR
Yik.msq.keys <- list(
Pleasure =psych::cs(happy,content,satisfied, pleased),
Activated.Pleasure =psych::cs(proud,enthusiastic),
Pleasant.Activation = psych::cs(energetic,full.of.pep,excited,wakeful,attentive,
    wide.awake,active,alert,vigorous),
Activation = psych::cs(aroused,intense),
Unpleasant.Activation = psych::cs(anxious,jittery,nervous),
Activated.Displeasure =psych::cs(scared,upset,fearful,
          clutched.up,tense,ashamed,guilty,hostile),
Displeasure = psych::cs(unhappy),
Deactivated.Displeasure = psych::cs(sad,gloomy,blue),
Unpleasant.Deactivation = psych::cs(drowsy,dull,bored,sluggish,tired),
Deactivation =psych::cs( quiet,still),
Pleasant.Deactivation = psych::cs(placid,relaxed,tranquil, at.rest,calm),
Deactivated.Pleasure =psych::cs( serene,at.ease)
)   
yik.scores <- psych::scoreItems(Yik.msq.keys,msqR)
yik <- yik.scores$scores
f2.yik <- psych::fa(yik,2) #factor the yik scores
psych::fa.plot(f2.yik,labels=colnames(yik),title="Yik-Russell-Steiger circumplex",cex=.8,
      pos=(c(1,1,2,1,1,1,3,1,4,1,2,4)))

       
msq.scores <- psych::scoreItems(keys.list,msqR)

#show a circumplex structure for the non-overlapping items
fcirc <- psych::fa(msq.scores$scores[,5:12],2)  
psych::fa.plot(fcirc,labels=colnames(msq.scores$scores)[5:12])


#now, find the correlations corrected for item overlap
msq.overlap <- psych::scoreOverlap(keys.list,msqR)
f2 <- psych::fa(msq.overlap$cor,2)
psych::fa.plot(f2,labels=colnames(msq.overlap$cor),
          title="2 dimensions of affect, corrected for overlap")

#extend this solution to EA/TA  NA/PA space
fe  <- psych::fa.extension(cor(msq.scores$scores[,5:12],msq.scores$scores[,1:4]),fcirc)
psych::fa.diagram(fcirc,fe=fe,main="Extending the circumplex structure to  EA/TA and PA/NA ")

#show the 2 dimensional structure
f2 <- psych::fa(msqR[1:72],2)
psych::fa.plot(f2,labels=colnames(msqR)[1:72],title="2 dimensions of affect at the item level")

#sort them by polar coordinates
round(psych::polar(f2),2)

#the msqR and sai data sets have 10 overlapping items which can be used for
#testRetest analysis.  We need to specify the keys, and then choose the appropriate
#data sets  
sai.msq.keys <- list(pos =c( "at.ease" ,  "calm" , "confident", "content","relaxed"),
  neg = c("anxious", "jittery", "nervous" ,"tense"  ,   "upset"),
  anx = c("anxious", "jittery", "nervous" ,"tense", "upset","-at.ease" ,  "-calm" ,
  "-confident", "-content","-relaxed"))
   
select <- psych::selectFromKeys(sai.msq.keys$anx)
#The following is useful for examining test retest reliabilities
msq.control <- subset(msqR,is.element( msqR$study , c("Cart", "Fast", "SHED", "SHOP")))
msq.film <- subset(msqR,(is.element( msqR$study ,  c("FIAT", "FILM","FLAT","MIXX","XRAY"))
    & (msqR$time < 3) )) 

msq.film[((msq.film$study == "FLAT") & (msq.film$time ==3)) ,] <- NA 
msq.drug <- subset(msqR,(is.element( msqR$study ,  c("AGES","SALT", "VALE", "XRAY")))
   &(msqR$time < 3))

msq.day <- subset(msqR,is.element( msqR$study ,  c("SAM", "RIM")))

---

NEO correlation matrix from the NEO_PI_R manual

Description

The NEO.PI.R is a widely used personality test to assess 5 broad factors (Neuroticism, Extraversion, Openness, Agreeableness and Conscientiousness) with six facet scales for each factor. The correlation matrix of the facets is reported in the NEO.PI.R manual for 1000 subjects.

Usage

data(neo)

Format

A data frame of a 30 x 30 correlation matrix with the following 30 variables.

N1

Anxiety

N2

AngryHostility

N3

Depression

N4

Self-Consciousness

N5

Impulsiveness

N6

Vulnerability

E1

Warmth

E2

Gregariousness

E3

Assertiveness

E4

Activity

E5

Excitement-Seeking

E6

PositiveEmotions

O1

Fantasy

O2

Aesthetics

O3

Feelings

O4

Ideas

O5

Actions

O6

Values

A1

Trust

A2

Straightforwardness

A3

Altruism

A4

Compliance

A5

Modesty

A6

Tender-Mindedness

C1

Competence

C2

Order

C3

Dutifulness

C4

AchievementStriving

C5

Self-Discipline

C6

Deliberation

Details

The past thirty years of personality research has led to a general consensus on the identification of major dimensions of personality. Variously known as the “Big 5" or the “Five Factor Model", the general solution represents 5 broad domains of personal and interpersonal experience. Neuroticism and Extraversion are thought to reflect sensitivity to negative and positive cues from the environment and the tendency to withdraw or approach. Openness is sometimes labeled as Intellect and reflects an interest in new ideas and experiences. Agreeableness and Conscientiousness reflect tendencies to get along with others and to want to get ahead.

The factor structure of the NEO suggests five correlated factors as well as two higher level factors. The NEO was constructed with 6 “facets" for each of the five broad factors.

For a contrasting structure, examine the items of the link{spi} data set (Condon, 2017).

Source

Costa, Paul T. and McCrae, Robert R. (1992) (NEO PI-R) professional manual. Psychological Assessment Resources, Inc. Odessa, FL. (with permission of the author and the publisher)

References

Condon, D. (2017) The SAPA Personality Inventory:An empirically-derived, hierarchically-organized self-report personality assessment model

Digman, John M. (1990) Personality structure: Emergence of the five-factor model. Annual Review of Psychology. 41, 417-440.

John M. Digman (1997) Higher-order factors of the Big Five. Journal of Personality and Social Psychology, 73, 1246-1256.

McCrae, Robert R. and Costa, Paul T., Jr. (1999) A Five-Factor theory of personality. In Pervin, Lawrence A. and John, Oliver P. (eds) Handbook of personality: Theory and research (2nd ed.) 139-153. Guilford Press, New York. N.Y.

Revelle, William (1995), Personality processes, Annual Review of Psychology, 46, 295-328.

Joshua Wilt and William Revelle (2009) Extraversion and Emotional Reactivity. In Mark Leary and Rick H. Hoyle (eds). Handbook of Individual Differences in Social Behavior. Guilford Press, New York, N.Y.

Joshua Wil and William Revelle (2016) Extraversion. In Thomas Widiger (ed) The Oxford Handbook of the Five Factor Model. Oxford University Press.

Examples

data(neo)
n5 <- psych::fa(neo,5)
neo.keys <- psych::make.keys(30,list(N=c(1:6),E=c(7:12),O=c(13:18),A=c(19:24),C=c(25:30)))
n5p <- psych::target.rot(n5,neo.keys) #show a targeted rotation for simple structure
n5p

---

Galton's Peas

Description

Francis Galton introduced the correlation coefficient with an analysis of the similarities of the parent and child generation of 700 sweet peas.

Usage

data(peas)

Format

A data frame with 700 observations on the following 2 variables.

parent

The mean diameter of the mother pea for 700 peas

child

The mean diameter of the daughter pea for 700 sweet peas

Details

Galton's introduction of the correlation coefficient was perhaps the most important contribution to the study of individual differences. This data set allows a graphical analysis of the data set. There are two different graphic examples. One shows the regression lines for both relationships, the other finds the correlation as well.

Source

Stanton, Jeffrey M. (2001) Galton, Pearson, and the Peas: A brief history of linear regression for statistics intstructors, Journal of Statistics Education, 9. (retrieved from the web from https://www.amstat.org/publications/jse/v9n3/stanton.html) reproduces the table from Galton, 1894, Table 2.

The data were generated from this table.

References

Galton, Francis (1877) Typical laws of heredity. paper presented to the weekly evening meeting of the Royal Institution, London. Volume VIII (66) is the first reference to this data set. The data appear in

Galton, Francis (1894) Natural Inheritance (5th Edition), New York: MacMillan).

See Also

The other Galton data sets: heights, galton,cubits

Examples

data(peas)
psych::pairs.panels(peas,lm=TRUE,xlim=c(14,22),ylim=c(14,22),main="Galton's Peas")
psych::describe(peas)
psych::pairs.panels(peas,main="Galton's Peas")

---

Pollack et al (2012) correlation matrix for mediation example

Description

A correlation matrix taken from Pollack (2012) with 9 variables. Primarily used as an example for setCor and mediation.

Usage

data("Pollack")

Format

A correlation matrix based upon 262 participants.

sex

Male = 1, Female = 0, 62% male

age

mean =33

tenure

length of employent, mean = 5.9 years

self.efficacy

self ratings

competence

self rating of competence

social.ties

Contact with business-related social ties

economic.stress

mean of two items on economic stress

depression

6 items from MAACL measuring depression

withdrawal

Withdrawal intentions in domain of entrepreneurship

Details

This is the correlation matrix from Pollack et al. (2012) p 797. The raw data are available from the processR package (Keon-Woong Moon, 2020). The data set is used by Hayes in example p 179 in example 3.

Source

Pollack et al. 2012

References

Pollack, Jeffrey M. and Vanepps, Eric M. and Hayes, Andrew F. (2012). The moderating role of social ties on entrepreneurs' depressed affect and withdrawal intentions in response to economic stress, Journal of Organizational Behavior 33 (6) 789-810.

Hayes, Andrew F. (2013) Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. Guilford Press.

Examples

psych::lowerMat(Pollack)

---

psychTools: datasets and utility functions to accompany the psych package

Description

PsychTools includes the larger data sets used by the psych package and also includes a few general utility functions such as the read.file and read.clipboard functions. The data sets ara made available for demonstrations of a variety of psychometric functions.

Details

See the various helpfiles listed in the index or as links from here. Also see the main functions in the psych package 00.psych-package.

Data sets from the SAPA/ICAR project:

ability	16 ICAR ability items scored as correct or incorrect for 1525 participants.
iqitems	multiple choice IQ items (raw responses)
affect	Two data sets of affect and arousal scores as a function of personality and movie conditions
bfi	25 Personality items representing 5 factors from the SAPA project for 2800 participants
bfi.dictionary	Dictionary of the bfi
big5.100.adjectives 100 adjectives describing the "big 5" for 502 subjects (from Goldberg) colom	Correlations from the Spanish WAIS (14 scales)
eminence	Eminence of 69 American Psychologists
epi	Eysenck Personality Inventory (EPI) data for 3570 participants
epi.dictionary	The items for the epi
epi.bfi	13 personality scales from the Eysenck Personality Inventory and Big 5 inventory
epiR	474 participants took the epi twice
msq	75 mood items from the Motivational State Questionnaire for 3896 participants
msqR	75 mood items from the Motivational State Questionnaire for 3032 unique participants
tai	Trait Anxiety data from the PMC lab matching the sai sample. 3032 unique subjects
sai	State Anxiety data from the PMC lab over multiple occasions. 3032 unique subjects.
sai.dictionary	items used in the sai
spi	4000 cases from the SAPA Personality Inventory (135 items, 10 demographics) including an item dictionary and scoring keys.
spi.dictionary	The items for the spi
spi.keys	Scoring keys for the spi

Historically interesting data sets

burt	11 emotional variables from Burt (1915)
galton	Galtons Mid parent child height data
heights	A data.frame of the Galton (1888) height and cubit data set
cubits	Galtons example of the relationship between height and cubit or forearm length
peas	Galtons Peas
cushny	The data set from Cushny and Peebles (1905) on the effect of three drugs on hours of sleep, used by Student (1908)
holzinger.swineford	26 cognitive variables + 7 demographic variables for 301 cases from Holzinger and Swineford.

Miscellaneous example data sets

blant	A 29 x 29 matrix that produces weird factor analytic results
blot	Bonds Logical Operations Test - BLOT
cities	Distances between 11 US cities
city.location	and their geograpical location
income	US family income from US census 2008
all.income	US family income from US census 2008
neo	NEO correlation matrix from the NEO_PI_R manual
Schutz	The Schutz correlation matrix example from Shapiro and ten Berge
Spengler	The Spengler and Damian correlation matrix example from Spengler, Damian and Roberts (2018)
Damian	Another correlation matrix from Spengler, Damian and Roberts (2018)
usaf	A correlation of 17 body size (anthropometric) measures from the US Air Force. Adapted from the Anthropometric package.
veg	Paired comparison of preferences for 9 vegetables (scaling example)

Functions to convert various objects to latex

fa2latex	Convert a data frame, correlation matrix, or factor analysis output to a LaTeX table
df2latex	Convert a data frame, correlation matrix, or factor analysis output to a LaTeX table
ICC2latex	Convert an ICC analyssis output to a LaTeX table
irt2latex	Convert an irt analysis output to a LaTeX table
cor2latex	Convert a correlation matrix output to a LaTeX table
omega2latex	Convert a data frame, correlation matrix, or factor analysis output to a LaTeX table

File manipulation functions

fileCreate	Create a file
fileScan	Show the first few lines of multitple files
filesInfo	Show the information for all files in a directory
filesList	Show the names of all files in a directory

dfOrder Sorts a data frame vJoin Combine two matrices or data frames into one based upon variable labels combineMatricesTakes a square matrix (x) and combines with a rectangular matrix y to produce a larger xy matrix.

File input/output functions

read.clipboard	Shortcuts for reading from the clipboard or a file
read.clipboard.csv
read.clipboard.fwf
read.clipboard.lower
read.clipboard.tab
read.clipboard.upper
read.file	Read a file according to its suffix
read.file.csv
read.https
write.file	Write data to a file
write.file.csv

Examples

psych::describe(ability)

---

Convert all Rd files in a directory to HTML files in a new directory

Description

Just a wrapper for tools::RdHTML to find a directory (e.g., the Man directory of help files) and convert them to HTML files in a new directory. Useful for adding HTML help files to a local web page.

Usage

rd2html(inDir =NULL,outDir=NULL, nfiles=NULL,package="psych",file=NULL)

Arguments

inDir	The input directory. If NULL,then a file in a directory will be searched for using file.choose()
outDir	Where to write the output files
nfiles	If not NULL, then how many files should be written
package	name of package
file	If specified, just convert this one file to HTML

Details

Just a wrapper for Rd2HTML calling some file tools. An interesting use of the function is to precheck whether all the help files are syntactically correct.

Author(s)

William Revelle

See Also

See Also as filesList, filesInfo

Examples

if(interactive()) {
#This is an  interactive function whic require interactive input and thus is not given as examples
rd2html()

}

---

Shortcuts for reading from the clipboard or a file

Description

Input from a variety of sources may be read. Matrices or data.frames may be read from files with suffixes of .txt, .text, .TXT, .dat, .DATA,.data, .csv, .rds, rda, .xpt, XPT, or .sav (i.e., data from SPSS sav files may be read as can files saved by SAS using the .xpt option). Data exported by JMP or EXCEL in the csv format are also able to be read. Fixed Width Files saved in .txt mode may be read if the widths parameter is specified. Files saved with writeRDS have suffixes of .rds or Rds, and are read using readRDS. Files associated with objects with suffixes .rda and .Rda are loaded (following a security prompt). The default values for read.spss are adjusted for more standard input from SPSS files. Input from the clipboard is easy but a bit obscure, particularly for Mac users. read.clipboard and its variations are just an easier way to do so. Data may be copied to the clipboard from Excel spreadsheets, csv files, or fixed width formatted files and then into a data.frame. Data may also be read from lower (or upper) triangular matrices and filled out to square matrices. Writing text files may be done using write.file which will prompt for a file name (if not given) and then write or save to that file depending upon the suffix (text, txt, or csv will call write.table, R, or r will dput, rda, Rda will save, Rds,rds will saveRDS).

Usage

read.file(file=NULL,header=TRUE,use.value.labels=FALSE,to.data.frame=TRUE,sep=",",
    quote="\"", widths=NULL,f=NULL, filetype=NULL,...)
   #for .txt, .text, TXT, .csv, .sav, .xpt, XPT,  R, r, Rds, .rds, or .rda, 
  # .Rda, .RData, .Rdata, .dat and .DAT  files

read.clipboard(header = TRUE, ...)   #assumes headers and tab or space delimited
read.clipboard.csv(header=TRUE,sep=',',...)   #assumes headers and comma delimited
read.clipboard.tab(header=TRUE,sep='\t',...)   #assumes headers and tab delimited
 #read in a matrix given the lower off diagonal
 read.clipboard.lower(diag=TRUE,names=FALSE,...) 
 read.clipboard.upper(diag=TRUE,names=FALSE,...)

#read in data using a fixed format width (see read.fwf for instructions)
read.clipboard.fwf(header=FALSE,widths=rep(1,10),...)  

read.https(filename,header=TRUE)
read.file.csv(file=NULL,header=TRUE,f=NULL,...)

#For output: 
#be sure to specify the file type in name
write.file(x,file=NULL,row.names=FALSE,f=NULL,...)
write.file.csv(x,file=NULL,row.names=FALSE,f=NULL,...)

Arguments

header	Does the first row have variable labels (generally assumed to be TRUE).
sep	What is the designated separater between data fields? For typical csv files, this will be a comma, but if commas designate decimals, then a ; can be used to designate different records.
quote	Specified to
diag	for upper or lower triangular matrices, is the diagonal specified or not
names	for read.clipboard.lower or upper, are colnames in the the first column
widths	how wide are the columns in fixed width input. The default is to read 10 columns of size 1.
filename	Name or address of remote https file to read.
...	Other parameters to pass to read
f	A file name to read from or write to. If omitted, file.choose is called to dynamically get the file name.
file	A file name to read from or write to. (same as f, but perhaps more intuitive). If omitted and if f is omitted,then file.choose is called to dynamically get the file name.
x	The data frame or matrix to write to f
row.names	Should the output file include the rownames? By default, no.
to.data.frame	Should the spss input be converted to a data frame?
use.value.labels	Should the SPSS input values be converted to numeric?
filetype	If specified the reading will use this term rather than the suffix.

Details

A typical session of R might involve data stored in text files, generated online, etc. Although it is easy to just read from a file (particularly if using read.file), an alternative is to use one's local system to copy from the file to the clipboard and then read from the clipboard using read.clipboard. This is very convenient (and somewhat more intuitive to the naive user). This is particularly useful when copying from a text book or article and just moving a section of text into R. However, copying from a file and then reading the clipboard is hard to automate in a script. Thus, read.file will read from a file.

The read.file function combines the file.choose and either read.table, read.fwf, read.spss or read.xport(from foreign) or load or readRDS commands. By examining the file suffix, it chooses the appropriate way to read the file. For more complicated file structures, see the foreign package. For even more complicated file structures, see the rio or haven packages.

Note that read.file assumes by default that the first row has column labels (header =TRUE). If this is not true, then make sure to specify header = FALSE. If the file is fixed width, the assumption is that it does not have a header field. In the unlikely case that a fwf file does have a header, then you probably should try fn <- file.choose() and then my.data <- read.fwf(fn,header=TRUE,widths= widths).

Further note: If the file is a .Rda, .rda, etc. file, the read.file command will return the name and location of the file. It will prompt the user to load this file. In this case, it is necessary to either assign the output (the file name) to an object that has a different name than any of the objects in the file, or to call read.file() without any specification. Notice that loading an .Rda file can overwrite existing objects. Thus the warning and the need to do the second step.

If the file has no suffix the default action is to quit with a warning. However, if the filetype is specified, it will use that type in the reading (e.g. filetype="txt" will read as text file, even if there is no suffix).

If the file is specified and has a prefix of http:// or https:// it will be downloaded and then read.

Currently supported input formats are

.sav	SPSS.sav files
.csv	A comma separated file (e.g. from Excel or Qualtrics)
.txt	A typical text file
.TXT	A typical text file
.text	A typical text file
.data	A data file
.dat	A data file
.rds	A R data file
.Rds	A R data file (created by a write)
.Rda	A R data structure (created using save)
.rda	A R data structure (created using save)
.RData	A R data structure (created using save)
.rdata	A R data structure (created using save)
.R	A R data structure created using dput
.r	A R data structure created using dput
.xpt	A SAS data file in xport format
.XPT	A SAS data file in XPORT format

Some data files have an extra ' in the data ( e.g. the NYT covid data base). These files can be read specifying quote ""

The foreign function read.spss is used to read SPSS .sav files using the most common options. Just as read.spss issues various warnings, so does read.file. In general, these can be ignored. For more detailed information about using read.spss, see the help pages in the foreign package.

If you have a file written by JMP, you must first export to a csv or text file.

The write.file function combines the file.choose and either write.table or saveRDS. By examining the file suffix, it chooses the appropriate way to write. For more complicated file structures, see the foreign package, or the save function in R Base. If no suffix is added, it will write as a .txt file. write.file.csv will write in csv format to an arbitrary file name.

Currently supported output formats are

.csv	A comma separated file (e.g. for reading into Excel)
.txt	A typical text file
.text	A typical text file
.rds	A R data file
.Rds	A R data file (created by a write)
.Rda	A R data structure (created using save)
.rda	A R data structure (created using save)
.R	A R data structure created using dput
.r	A R data structure created using dput

Many Excel based files specify missing values as a blank field. When reading from the clipboard, using read.clipboard.tab will change these blank fields to NA.

Sometimes missing values are specified as "." or "999", or some other values. These can be converted by the read.file command specifying what values are missing (e.g., na ="."). See the example for the reading from the remote mtcars.csv file.

read.clipboard was based upon a suggestion by Ken Knoblauch to the R-help listserve.

If the input file that was copied into the clipboard was an Excel file with blanks for missing data, then read.clipboard.tab() will correctly replace the blanks with NAs. Similarly for a csv file with blank entries, read.clipboard.csv will replace empty fields with NA.

read.clipboard.lower and read.clipboard.upper are adapted from John Fox's read.moments function in the sem package. They will read a lower (or upper) triangular matrix from the clipboard and return a full, symmetric matrix for use by factanal, fa , ICLUST, pca. omega , etc. If the diagonal is false, it will be replaced by 1.0s. These two function were added to allow easy reading of examples from various texts and manuscripts with just triangular output.

Many articles will report lower triangular matrices with variable labels in the first column. read.clipboard.lower will handle this case. Names must be in the first column if names=TRUE is specified.

Other articles will report upper triangular matrices with variable labels in the first row. read.clipboard.upper will handle this. Note that labels in the first column will not work for read.clipboard.upper. The names, if present, must be in the first row.

Consider the following lower triangular matrix. To read it, copy it to the clipboard and read.clipboard.lower(names=TRUE)

A1 1.00

A2 -0.34 1.00

A3 -0.27 0.49 1.00

A4 -0.15 0.34 0.36 1.00

A5 -0.18 0.39 0.50 0.31 1.00

C1 0.03 0.09 0.10 0.09 0.12 1.00

However, if the data are strung out e.g.,

-.34
-.27
-.15
-.18
.03
.49
.34
.39
.09
.36
.50
.10
.31
.09
.12

Then one needs to read it using the read.clipboard.upper(names=FALSE,diag=FALSE) option.

read.clipboard.fwf will read fixed format files from the clipboard. It includes a patch to read.fwf which will not read from the clipboard or from remote file. See read.fwf for documentation of how to specify the widths.

Value

The contents of the file to be read or of the clipboard. Saved as a data.frame.

Author(s)

William Revelle

Examples

#All of these functions are meant for interactive Input
#Because these are dynamic functions, they need to be run interactively and 
# can not be run as examples.
#Thus they are not to be tested by CRAN

if(interactive()) {
 my.data <- read.file()  #search the directory for a file and then read it.
                         #return the result into an object 
#or, if the file is a rda, etc. file
my.data <- read.file()  #return the path and instructions of how to load
  # without assigning a value.

filesList()  #search the system for a particular file and then list all the files in that directory
fileCreate() #search for a particular directory and create a file there.
write.file(Thurstone) #open the search window, choose a location and name the output file,
# write the data file (e.g., Thurstone ) to the file chosen

#the example data set from read.delim in the readr package to read a remote csv file
my.data <-read.file(
"https://github.com/tidyverse/readr/raw/master/inst/extdata/mtcars.csv", 
na=".")   #the na option is used for an example, but is not needed for these data


#These functions read from the local clipboard and thus are interactive
my.data <- read.clipboard()   #space delimited columns
my.data <- read.clipboard.csv()  # , delimited columns 
my.data <- read.clipboard.tab()  #typical input if copied from a spreadsheet
my.data <- read.clipboad(header=FALSE)  #data start on line 1
my.matrix <- read.clipboard.lower()
}

---