AMCP (version 2.0.0)

chapter_3_table_9_raw: The data used for Chapter 3, Table 9 (raw data to produce the summary measures)

Description

Raw data on the number of drinks per day (and log of the number of drinks)

Usage

data(chapter_3_table_9_raw)

Format

An object of class data.frame with 88 rows and 3 columns.

Variables

Group

randomly assigned group membership (see details)

Drinks

number of standard drinks, on average, per week

LgDrinks

log of the number of standard drinks, on average, per week

Synonym

C3T9R

Author

Ken Kelley

Details

Average number of standard drinks per week at intake for a sample of homeless alcoholics at nine-month follow-up (Smith, Meyers, & Delaney, 1988). Note that the groups, 1-5, are, respectively, "T1 CRA-D", "T1 CRA+D", "T1Std", "T2 CRA-D", and "T2 Std," where CRA is "Community Reinforcement Approach (with or without Disulfiram) and where "Std" is standard therapy. Note that this is the same data as data(chapter_3_table_9_raw).

References

Maxwell, S. E., Delaney, H. D., & Kelley, K. (2027). Designing experiments and analyzing data: A model comparison perspective (4th ed.). New York, NY: Routledge.

Smith, J. E., Meyers, R. J. & Delaney, H. D. (1998). The community reinforcement approach with homeless alcohol-dependent individuals. Journal of Consulting and Clinical Psychology, 66, 541--548.

Examples

# Load the data
data(chapter_3_table_9_raw)

# Or, alternatively load the data as
data(C3T9R)

# View the structure
str(chapter_3_table_9_raw)

# ---------------------------------------------------------------------
# Optional: a factor-coded copy for model-comparison analyses / plotting.
# Group is stored as a numeric code so the book's examples reproduce
# exactly. To treat it as a grouping factor, build a *copy* (suffix
# "_factors") so the canonical data set is left unchanged. Labels are
# taken from the description above (CRA = Community Reinforcement
# Approach, with [+D] or without [-D] Disulfiram; Std = standard therapy).
C3T9R_factors <- chapter_3_table_9_raw
C3T9R_factors$Group <- factor(C3T9R_factors$Group, levels = 1:5,
  labels = c("T1 CRA-D", "T1 CRA+D", "T1 Std", "T2 CRA-D", "T2 Std"))

# These are the raw scores behind the chapter's summary measures, so the
# factor copy is mainly for grouping/plotting; Drinks and LgDrinks stay
# numeric.
str(C3T9R_factors)