AMCP (version 2.0.0)

chapter_7_table_24: The data used in Chapter 7, Table 24

Description

The data used in Chapter 7, Table 24

Usage

data(chapter_7_table_24)

Format

An object of class data.frame with 45 rows and 3 columns.

Variables

Therapy

the type of therapy

Severity

the severity of the therapy

Score

the score of the individual

Synonym

C7T24

Author

Ken Kelley

Details

Suppose that a clinical psychologist is interested in comparing the relative effectiveness of three forms of psychotherapy for alleviating depression. Fifteen individuals are randomly assigned to one of each of three treatment groups: cognitive-behavioral, Rogerian, and assertiveness training. The Depression Scale of the MMPI serves as the dependent variable. After the fact, these individuals where placed into one of three categories based on the severity of their depression. Thus, this data set represents a 3 by 3 nonorthogonal factorial design with post hoc blocking. Table 7.24 shows hypothetical MMPI scores for 45 participants, each of whom is placed in one cell of a 3x3 design. One factor (A, the row factor) is type of therapy. The other factor (B, the column factor) is degree of severity.

The data represents the relative effectiveness of three forms of psychotherapy for alleviating depression. Fifteen individuals were randomly assigned to one of three groups. After the fact, these individuals where placed into one of three categories based on the severity of their depression. Thus, this data set represents a 3 by 3 nonorthogonal factorial design with post hoc blocking.

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.

Examples

# Load the data
data(chapter_7_table_24)

# Or, alternatively load the data as
data(C7T24)

# View the structure
str(chapter_7_table_24)

# ---------------------------------------------------------------------
# Optional: a factor-coded copy for model-comparison analyses / plotting.
# Therapy and Severity are stored as numeric codes so the book's examples
# reproduce exactly. The "Variables" section does not give level labels,
# so the numeric codes are kept as the factor levels. Build a *copy*
# (suffix "_factors") so the canonical data set is left unchanged; Score
# stays numeric.
C7T24_factors <- chapter_7_table_24
C7T24_factors$Therapy  <- factor(C7T24_factors$Therapy)
C7T24_factors$Severity <- factor(C7T24_factors$Severity)

# This is a NONORTHOGONAL (unbalanced) two-way design with post hoc
# blocking, so the sums of squares are order-dependent; the book reports
# the appropriate tests. Build the factor copy, then follow the book's
# procedure (e.g., car::Anova() for Type II/III).
str(C7T24_factors)