Nutrition data

Share:

Description

This observational dataset involves three factors, but where several factor combinations are missing. It is used as a case study in Milliken and Johnson, Chapter 17, p.202. (You may also find it in the second edition, p.278.)

Usage

1
nutrition

Format

A data frame with 107 observations on the following 4 variables.

age

a factor with levels 1, 2, 3, 4. Mother's age group.

group

a factor with levels FoodStamps, NoAid. Whether or not the family receives food stamp assistance.

race

a factor with levels Black, Hispanic, White. Mother's race.

gain

a numeric vector (the response variable). Gain score (posttest minus pretest) on knowledge of nutrition.

Details

A survey was conducted by home economists β€œto study how much lower-socioeconomic-level mothers knew about nutrition and to judge the effect of a training program designed to increase therir knowledge of nutrition.” This is a messy dataset with several empty cells.

Source

Milliken, G. A. and Johnson, D. E. (1984) Analysis of Messy Data – Volume I: Designed Experiments. Van Nostrand, ISBN 0-534-02713-7.

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
require(lsmeans)
nutr.aov <- aov(gain ~ (group + age + race)^2, data = nutrition)

# Summarize predictions for age group 3
nutr.lsm <- lsmeans(nutr.aov, ~ race * group, 
                at = list(age="3"))
                   
lsmip(nutr.lsm, race ~ group)

# Hispanics seem exceptional; but, this doesn't test out due to very sparse data
cld(nutr.lsm, by = "group")
cld(nutr.lsm, by = "race")