# R Dataset / Package psych / neo

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## Description

Describes how to create a bar plot based on count data. For an example of count data, see the email50 curated data set which was taken from the Open Intro AHSS textbook (not affiliated). An example of count data in this dataset would be the spam column.

## Usage

Select one (1) column to create its barplot and then click 'Submit'. If you do not choose count data, you may get unexpected results.

Students may also be interested in creating barplots for contingency tables.

For a stacked side-by-side barplot, see the other barplot app.

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## Usage

Select 1 (one) column from a contingency table like the Gender and Politics or VADeaths curated datasets.

If you do not choose a contingency table, you may get unexpected results. You can import a dataset if you are logged-in.

## Details

Shows the student how to create a single stacked bar plot based on a column in a contingency table.

For a basic barplot (single column) based on count data see the count data barplot app.

For a stacked side-by-side barplot see the other stacked barplot app for categorical data.

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## Usage

Select 1 (one) column from a contingency table. If you don't have your own dataset, you can choose the Gender and Politics or VADeaths curated datasets. If a contingency table is not chosen, you may get unexpected results.

A contingency table has columns like a regular dataset, but the first row contains row names that categorize and "split-up" the dataset. An example of a contingency table would be something like this:

LIBERAL CONSERVATIVE
F 762 468
M 484 477


This contingency table is take from the Gender and Politics dataset. You can get a preview by selecting the dataset from the Curated Data dropdown above.

## Details

This app shows the student how to create a pie chart from a contingency table by hand using a Quadstat dataset.

A pie chart shows proportions of a sample or population. Each piece of a pie chart corresponds to some subset of the sample or population. In this case, we will use the contingency table rows to subset the sample.

Students may also want to view the app for creating a pie chart from count data.

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## Usage

Click "Submit" after selecting one column to see how to compute the arithmetic mean (average) of data (vectors).

## Description

If all the values of a sample were plotted on a number line, the average would be the point in the middle that would balance the two sides.

The average is greatly influenced by outliers, meaning extreme points can pull the average to the left or right.

If we are referring to the average of population (all observations), the symbol for the average (arithmetic mean) is $\mu$.

If we are referring to the average of a sample (a subset of the population), the symbol for the average (arithmetic mean) is $\bar{x}$.

## Computing the average

Suppose we have a sample consisting of $x_1, x_2, x_3,...,x_n$. This means we have $n$ observations. Then,

$$\bar{x}=\frac{x_1, x_2, x_3,...,x_n}{n}.$$

The formula tells us that we need to add all the observations and then divide by the number of observations to compute the mean.

## Example 1

Compute the mean of $A = \{1,2,3\}$.

$$\bar{x} = \frac{1+2+3}{3} = 2.$$
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## Usage

Select two columns which are to be used in the scatterplot. The first column clicked will be the independent variable (X-axis).

## Description

This web application describes how to create a scatterplot of two dataset variables plotted on the xy-axes.

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## Median Value

### Description

Compute the sample median.

### Usage

median(x, na.rm = FALSE, ...)


### Arguments

 x an object for which a method has been defined, or a numeric vector containing the values whose median is to be computed. na.rm a logical value indicating whether NA values should be stripped before the computation proceeds. ... potentially further arguments for methods; not used in the default method.

### Value

The default method returns a length-one object of the same type as x, except when x is logical or integer of even length, when the result will be double.

If there are no values or if na.rm = FALSE and there are NA values the result is NA of the same type as x (or more generally the result of x[FALSE][NA]).

### References

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.

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## Visual Summaries

Attachment Size
5.09 KB
GNU General Public License v2.0
Documentation

## 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.

### 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

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.

### Examples

data(neo)
n5 <- fa(neo,5)
neo.keys <- 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 <- target.rot(n5,neo.keys) #show a targeted rotation for simple structure
n5p
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Dataset imported from https://www.r-project.org.