Beauty and teaching evaluations

Description

Dataset from Hamermesh, D. S., & Parker, A. (2005), "Beauty in the classroom: Instructors’ pulchritude and putative pedagogical productivity", Economics of Education Review, 24(4), 369–376.

Usage

data(Beauty)

Format

A data frame with the following variables:

n

The professor’s identification number.

score

Average professor evaluation score, ranging from 1 (very unsatisfactory) to 5 (excellent).

rank

Rank of professor: teaching, tenure track, or tenured.

ethnicity

Ethnicity of professor: not minority or minority.

gender

Gender of professor: female or male.

language

Language of the school where the professor received education: English or non-English.

age

Age of the professor.

cls_perc_eval

Percentage of students in the class who completed the evaluation.

cls_did_eval

Number of students in the class who completed the evaluation.

cls_students

Total number of students enrolled in the class.

cls_level

Class level: lower or upper.

cls_profs

Number of professors teaching sections of the course in the sample: single or multiple.

cls_credits

Number of credits of the class: one credit (e.g. lab, PE) or multi credit.

bty_f1lower

Beauty rating of professor from lower-level female students (1 = lowest, 10 = highest).

bty_f1upper

Beauty rating of professor from upper-level female students (1 = lowest, 10 = highest).

bty_f2upper

Beauty rating of professor from second upper-level female students (1 = lowest, 10 = highest).

bty_m1lower

Beauty rating of professor from lower-level male students (1 = lowest, 10 = highest).

bty_m1upper

Beauty rating of professor from upper-level male students (1 = lowest, 10 = highest).

bty_m2upper

Beauty rating of professor from second upper-level male students (1 = lowest, 10 = highest).

bty_avg

Average beauty rating of the professor.

pic_outfit

Outfit of professor in picture: not formal or formal.

pic_color

Color of professor’s picture: color or black and white.

Details

The dataset examines the relationship between instructors' physical attractiveness and student evaluation scores, controlling for demographic and class characteristics.

Source

Hamermesh, D. S., & Parker, A. (2005). Beauty in the classroom: Instructors’ pulchritude and putative pedagogical productivity. Economics of Education Review, 24(4), 369–376. doi:10.1016/j.econedurev.2004.07.013

My dataset from CSV

Description

This dataset was imported from a CSV file and included in the IPAG package for demonstration. Data are taken from the article by Augsburg, B., De Haas, R., Harmgart, H., & Meghir, C. (2015). The impacts of microcredit: Evidence from Bosnia and Herzegovina. American Economic Journal: Applied Economics, 7(1), 183-203.

Usage

data(Bosnia)

Format

A data frame with the following variables:

Income_0B

Household income for the control group before the experiment

Income_1B

Household income for the treatment group before the experiment

Income_0F

Household income for the control group after the experiment

Income_1F

Household income for the treatment group after the experiment

Details

doi: 10.1257/app.20130272

Content Marketing Dataset

Description

Dataset from Koob (2021), "Determinants of content marketing effectiveness: Conceptual framework and empirical findings from a managerial perspective." PloS ONE, 16(4), e0249457.

Usage

data(ContentMarketing)

Format

A data frame with the following variables:

Firm

The company’s identification number.

CMEFFECT

Effectiveness of the content marketing strategy. Marketing and communications executives rated the degree of effectiveness on a scale from 1 to 5 based on their perception and expertise.

CMSTRAT

Content marketing strategy context. Four-item scale measuring whether the organization had a defined, comprehensible, and long-term content marketing strategy. Rated from 1 ("totally disagree") to 5 ("totally agree").

CPROD

Content production context. Reflects the organization's efforts to optimize content value for customers, meet content quality standards, and plan and create content systematically.

CDIST1

Content distribution context / intermediate number of media platforms. Measures the number of media platforms used to distribute content.

CDIST2

Content distribution context / joint deployment of print and digital platforms. Measures the simultaneous use of print and digital media for content distribution.

CPROM

Content Promotion Context. Measures the importance attached to content promotion. Respondents indicated the share of total content marketing investment devoted to promotion activities.

CMPERME

Content Marketing Performance Measurement Context. Captures the frequency of content marketing performance measurement across print and digital platforms and the use of performance data to guide improvement.

CMORG

Content Marketing Organization. Captures structural specialization, autonomy in content marketing, and processes and systems that enable specialization.

SIZE

Organization size. Three dummy variables categorize organizations by number of employees: "Tiny" (250-499), "Small" (500-999), "Medium" (1,000-4,999), "Big" (>=5,000).

SECTOR

Sector affiliation. Dummy variable distinguishing organizations in the "industrial" or "service" sector.

Source

Koob, C. (2021). Determinants of content marketing effectiveness: Conceptual framework and empirical findings from a managerial perspective. PloS ONE, 16(4), e0249457.

Hedonic housing prices and environmental quality

Description

Dataset from Harrison Jr, D., & Rubinfeld, D. L. (1978), "Hedonic housing prices and the demand for clean air", Journal of Environmental Economics and Management, 5(1), 81–102.

Usage

data(Housing)

Format

A data frame with the following variables:

CRIM

Per capita crime rate by town.

ZN

Proportion of residential land zoned for lots over 25,000 square feet.

INDUS

Proportion of non-retail business acres per town.

CHAS

Charles River dummy variable: 1 if the tract bounds the river, 0 otherwise.

NOX

Nitric oxides concentration (parts per 10 million).

RM

Average number of rooms per dwelling.

AGE

Proportion of owner-occupied units built prior to 1940.

DIS

Weighted distances to five Boston employment centres.

RAD

Index of accessibility to radial highways.

TAX

Full-value property tax rate per $10,000.

PTRATIO

Pupil–teacher ratio by town.

B

Computed as 1000(B_k - 0.63)^2, where B_k is the proportion of Black residents by town.

LSTAT

Percentage of lower-status population.

MEDV

Median value of owner-occupied homes in thousands of US dollars.

Details

The dataset is a cross-section of housing values in Boston suburbs and is widely used to study hedonic pricing models and the demand for environmental quality.

Source

Harrison Jr, D., & Rubinfeld, D. L. (1978). Hedonic housing prices and the demand for clean air. Journal of Environmental Economics and Management, 5(1), 81–102. doi:10.1016/0095-0696(78)90006-2

McKinsey / OECD Education Dataset

Description

Dataset combining information from:

• McKinsey, "Valuing the merit of teachers", Direction interministérielle de la transformation publique.

• OECD (2012), "Does Performance-Based Pay Improve Teaching?", PISA in Focus, No. 16, OECD Publishing, Paris.

Usage

data(McKinsey)

Format

A data frame with the following variables:

COUNTRIES

The name of the country.

READING

Teacher efficiency measured by PISA reading tests.

YSALARY

Teacher salaries in relation to GDP per capita. 0 means salaries equal GDP per capita, 0.5 means 1.5 times higher than GDP per capita, 1 means 2 times higher than GDP per capita.

YGDP

GDP per capita in USD 1,000.

EXPEND

Cumulative expenditure by educational establishments in USD 1,000.

PERF

Teacher merit pay (y = yes, n = no).

Details

The dataset contains teacher efficiency as measured by reading performance on PISA tests, along with explanatory variables related to salary, GDP, expenditures, and performance-based pay.

Source

• McKinsey, "Valuing the merit of teachers", Direction interministérielle de la transformation publique.

• OECD (2012), "Does Performance-Based Pay Improve Teaching?", PISA in Focus, No. 16, OECD Publishing, Paris, doi:10.1787/5k98q27r2stb-en

My dataset from CSV

Description

This dataset was imported from a CSV file and included in the IPAG package for demonstration. The reference article is Escobar, L. E., Molina-Cruz, A., & Barillas-Mury, C. (2020). BCG vaccine protection from severe coronavirus disease 2019 (COVID-19). Proceedings of the National Academy of Sciences, 117(30), 17720-17726.

Usage

data(covid19)

Format

A data frame with the following variables:

total_deaths_per_million

Number of deaths per million inhabitants as of April 22, 2020.

country

The name of the country.

Cal2013

Daily caloric intake.

ca2014

Per capita CO2 emissions in 2014.

BMI

Body mass index in 2016 (male population).

Sras

Number of people who died of SARS in 2004.

dtp3_2011

Proportion of children under one year of age vaccinated with the DTP vaccine (diphtheria, tetanus, poliomyelitis) in 2011.

BCG_policy

BCG vaccination policy: "current", "never" or "interrupted".

lati

Latitude of the country's capital.

longi

Longitude of the country's capital.

Trade2018

Imported and exported goods as a percentage of GDP in 2018.

H2015

Health expenditure per capita in 2015.

Health2010

Percentage of the state budget allocated to health in 2010.

TB

Number of tuberculosis cases per 100,000 inhabitants.

PIBhab

GDP per capita.

Superf

Area of the country.

Demo

Democracy index of the country.

HDI_2018

Human Development Index in 2018.

Expectancy

Life expectancy at birth.

Children

Number of children per woman.

PopulationD

Population density of the country.

Pop

Total population of the country.number of children per woman

Gini

Measure of income inequality (0 = perfect equality, 1 = perfect inequality).

AgeMed

Median age of the population.

debut

Number of days between the first confirmed Covid-19 case in China and the first confirmed case in the country.

Details

https://doi.org/10.1073/pnas.2008410117

Source

Various international public databases (WHO, World Bank, etc.)

Linear regression summary

Description

This function performs a linear regression and returns a summary including:

• Adjusted R-squared

• Overall F-test p-value

• Table with parameter estimates, confidence intervals (default 99%), p-values, and significance stars (*, **, ***)

Usage

linear_regress(formula, data, level = 0.99)

Arguments

Value

Object of class 'linear_regress'

Examples

data(Housing, package = "IPAG")
linear_regress(MEDV ~ RM + LSTAT, data = Housing)

Confidence interval for a mean

Description

Confidence interval for a mean

Usage

mean_ci(x, level = 0.99, na.rm = TRUE)

Arguments

Value

Object of class 'mean_ci'

Examples

x <- c(4.2, 5.1, 6.3, 5.8, 4.9)
mean_ci(x)
mean_ci(x, level = 0.95)

Confidence interval for the difference of means

Description

Confidence interval for the difference of means

Usage

mean_diff_ci(x, y, level = 0.99, paired = FALSE, na.rm = TRUE)

Arguments

Value

Object of class 'mean_diff_ci'

Examples

x <- c(5.1, 4.9, 6.2, 5.8, 5.4)
y <- c(4.8, 4.7, 5.9, 5.2, 5.0)
mean_diff_ci(x, y)
mean_diff_ci(x, y, paired = TRUE)

Confidence interval for odds ratio from a 2x2 table

Description

Confidence interval for odds ratio from a 2x2 table

Usage

oddsratio_ci(a, b, c, d, level = 0.99)

Arguments

Value

Object of class 'oddsratio_ci'

Examples

oddsratio_ci(a = 12, b = 5, c = 4, d = 15)
oddsratio_ci(a = 12, b = 5, c = 4, d = 15, level = 0.95)

Confidence interval for a proportion

Description

Confidence interval for a proportion

Usage

prop_ci(trials, successes, level = 0.99)

Arguments

Value

Object of class 'prop_ci'

Examples

# 45 successes out of 100 trials
prop_ci(trials = 100, successes = 45)
prop_ci(trials = 100, successes = 45, level = 0.95)