R Dataset / Package psych / bfi.dictionary

How To Create a Barplot

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How To Create a Stacked Barplot

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How To Compute the Mean

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How To Create a Plot

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How to Compute the Median

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Boxplot

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Correlation Coefficient

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Cumulative Frequency Histogram

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Dotplot

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Hollow Histogram

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Numerical Summaries

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Pie Chart

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Plot

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Ratio

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Regression

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Stem and Leaf Plots

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Visual Summaries

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<iframe src="https://embed.picostat.com/r-dataset-package-psych-bfidictionary.html" frameBorder="0" width="100%" height="307px" />
Attachment Size
dataset-37624.csv 2.47 KB
Dataset License
GNU General Public License v2.0
Documentation License
GNU General Public License v2.0
Documentation

On this Picostat.com statistics page, you will find information about the bfi.dictionary data set which pertains to 25 Personality items representing 5 factors. The bfi.dictionary data set is found in the psych R package. Try to load the bfi.dictionary data set in R by issuing the following command at the console data("bfi.dictionary"). This may load the data into a variable called bfi.dictionary. If R says the bfi.dictionary data set is not found, you can try installing the package by issuing this command install.packages("psych") and then attempt to reload the data with library("psych") followed by data("bfi.dictionary"). Perhaps strangley, if R gives you no output after entering a command, it means the command succeeded. If it succeeded you can see the data by typing bfi.dictionary at the command-line which should display the entire dataset.

If you need to download R, you can go to the R project website. You can download a CSV (comma separated values) version of the bfi.dictionary R data set. The size of this file is about 2,532 bytes.


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 http://sapa-project.org) project. To see an example of the data collection technique, visit http://SAPA-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 ( http://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.

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

--

Dataset imported from https://www.r-project.org.
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