Conducting - Experimental Research Methods - Lecture Slides, Slides of Research Methodology

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Designing, Conducting,

Analyzing, and Interpreting

Experiments with More Than Two

Groups

Experimental Design: Adding to the Basic Building

Block

• The two-group design is the basic building block.
  • Is the experimental group significantly different from the control group?
• Researchers typically want to move beyond two-

group designs so they can ask more complicated

and interesting questions.

The Multiple-Group Design

• How Many Groups?
  • This question marks the difference between the multiple-group design and the two-group design. - A multiple-group design compares three or more levels or amounts of an IV. - A multiple-group design can have a control group and two or more experimental groups. - We can compare three, four, five, or even more differing levels or amounts of an IV. - A multiple-group design does not have to have a control group.

The Multiple-Group Design

• Assigning Participants to Groups
  • After we decide to conduct a multiple-group experiment, we must decide about assignment or research participants to groups.
  • We may choose between independent groups or correlated groups.

The Multiple-Group Design

• Correlated Samples (Nonrandom Assignment to Groups)
  • Matched Sets: Participants are matched on a variable that will affect their performance on the DV (matching variable). - Then sets of participants are created who are essentially the same on the matching variable.
  • Repeated Measures: Each participant must participate in all of the treatment conditions.
  • Natural Sets: Analogous to using natural pairs except that sets must include more than two research participants. - Many animal researchers use littermates as natural sets.

Comparing Multiple-Group and Two-Group Designs

• All you have to do to change your two-group design into a multiple- group design is add another level (or more) to your IV.
• A two-group design can tell you whether your IV has an effect.
  • You should never conduct an experiment to determine whether a particular IV has an effect without first conducting a thorough literature search
  • If you find no answer in a library search, then you should consider conducting a two-group (presence vs. absence) study.
• A multiple-group design is appropriate when you find the answer to your basic question and wish to go further.

Analyzing Multiple-Group Designs

• Multiple-Groups designs are measured with the analysis of variance (ANOVA). - The ANOVA procedure used to analyze a multiple-group design with one IV is known as a one-way ANOVA. - A one-way ANOVA for independent groups is known as a Completely randomized ANOVA. - A one-way ANOVA for correlated groups is known as a Repeated-measures ANOVA.

Rationale of ANOVA

• Between-Groups Variability: Variability in DV scores

that is due to the effects of the IV.

• Error Variability (Within-Groups Variability): Variability in

DV scores that is due to factors other than the IV

(individual differences, measurement error, and

extraneous variation).

Interpreting Computer Statistical Output

• One-way ANOVA for Independent Samples
  • Source table
    • A table that contains the results of ANOVA. Source refers to the source of the different types of variation.
  • Sum of squares
    • The amount of variability in the DV attributable to each source.
  • Mean square
    • The “averaged” variability for each source.
    • The mean square is computed by dividing each source’s sum of squares by its degrees of freedom.

Within

Between

MS

MS

F 

Interpreting Computer Statistical Output

• One-way ANOVA for Independent Samples
  • To discern where the significance lies in a multiple-group experiment, we must do additional statistical tests known as post hoc comparisons (also known as follow- up tests). - Statistical comparisons made between group means after finding a significant F ratio.

The Continuing Research Problem

• Here are the logical steps taken in the hypothetical experiment discussed in your text: - After conducting a preliminary experiment and determining that salesclerks wait on well-dressed customers more quickly, we decided to further test the effects of different clothing (IV) on clerks’ response times (DV). - We chose to test only one IV (clothing) because our research is still preliminary. - We tested three different types of dress because they seemed to be likely ways that customers might dress. - With access to many salesclerks, we used random assignment to the three groups and, thus, a multiple-independent-groups design. We used a one-way ANOVA for independent groups and found that the clerks responded more quickly to customers in dressy or casual clothes than to customers in sloppy clothes.

The Continuing Research Problem

• Here are the logical steps taken in the hypothetical experiment discussed in your text: - (alternate) With smaller numbers of clerks, we chose to use repeated measures. Thus, we used a multiple-within-group design and a one-way ANOVA for correlated groups. Clerks responded to sloppily dressed customers more slowly than to well-dressed or casually dressed customers. - We concluded (hypothetically) that customers should not dress in sloppy clothes if they desire quick help in a store.

Between-Subjects ANOVA Example

10 mg 40 mg 70 mg All

SX 188 139 254 581

Mean 23.25 17.375 31.75 24.

SS 1391.5 653.875 665.5 3546.653docsity.com

Between-Subjects ANOVA Example

Source SS df MS F obt F crit

Between 835.75 2 417.875 3.24 3.

Within/ 2710.8753 21 129.

Error

Total 3546.6253 23

Look to Table D.2 to find Fc rit

This ANOVA fails to find any significant results.