single-factor experimental designs slides prepared by alison l. o’malley passer chapter 8

Single-FactorExperimentalDesigns

Slides Prepared by Alison L. O’Malley

Passer Chapter 8

What does it mean to have experimental control?

Here, the focus in on experiments with one independent variable (i.e., single-factor designs).

Experimental Control

•Manipulation of one or more IVs•Measured DV(s) •Everything else held constant

Need to rule out all possible influences on the DV other than the IV!

Experimentation: Some Review

•What are the three criteria that must be met in order to make a causal inference?

•What is a confounding variable?

Causality and Confounds

•What are the three criteria that must be met in order to make a causal inference?• Covariation of X and Y• Temporal order•Absence of plausible alternative explanations

•What is a confounding variable? •A factor that covaries with the IV• Cannot tell whether the IV or the confound

affects the DV

How can confounding factors be reduced?

Confounding Variables

•Many environmental factors can be held constant•This is why laboratory settings are so

attractive!

•Those environmental factors that cannot be held constant (e.g., time of day) can be balanced across different experimental conditions

Confounding Variables

•Confounds dealing with participant characteristics (e.g., personality, age) are further addressed through experimental design •One major distinction to attend to is whether psychological scientists employ a between-subjects design or a within-subjects design

Research Design Description

Confounds minimized through…

Between-subjects

Different participants in each condition

Random assignment

Within-subjects

Participants encounter all levels of experiment

Counterbalancing

Between-Subjects Designs

Age, cognitive ability, personality, mood, ethnicity, eating habits…and

so on

In theory, randomly assigning participants to levels of the IV (1, 2, or

3) will distribute individual differences throughout the conditions so

experimental groups are equivalent in every way except the IV.

Level 1Participant ID: 12345






Within- Subjects Designs

Participants serve as their own controls

Counterbalancing ensures that participants experience the

IV levels in different orders.







Manipulating an IV

The Case for Multilevel Designs A two-group design would miss this nonlinear effect

Experimental and Control Conditions

Participants in an experimental condition are exposed to a “treatment,” whereas participants in a control condition do not receive the treatment.

E.g., In goal setting theory research, participants in the experimental condition are given a difficult, specific goal to achieve, whereas participants in the control condition are simply told to do their best.

More experimental terminology

Between-Subjects Designs: Advantages

•No carryover effects •Less likely that participants will catch on to the hypothesis•Exposure to multiple levels of the IV may be impossible or ethically and practically difficult

Between-Subjects Designs: Disadvantages

•Different people in each condition generate more “noise” (i.e., variability), making it more difficult to establish an effect of the IV on the DV •More participants required

Between-Subjects Designs: Independent-Groups

•Participants randomly assigned to conditions •What are potential issues with random assignment? •Block randomization helps overcome these issues • Run through random order of blocks

(rounds of conditions) until desired sample size reached

Between-Subjects Designs: Matched-Groups

•Identify a relevant characteristic (a matching variable) and randomly assign participants to conditions based on their standing (e.g., high, average, low) on this characteristic•Wise to use possible confounds as matching variables

Between-Subjects Designs: Natural-Groups

•Rather than manipulate IV, create different groups of participants based on naturally occurring attributes called subject variables (e.g., age, gender, personality)•Subject variables often referred to as quasi-IVs •But is this truly an experimental design?

Concept Clarification

What is the difference between random sampling and random assignment?

Random Sampling vs. Random Assignment

Within-Subjects Designs:Advantages

•Also called repeated-measures designs •Need fewer participants•Can collect more data per condition •Required to investigate certain research questions •Don’t have to worry about non-equivalent groups

Within-Subjects Designs:Disadvantages

•Greater likelihood that participants will catch on to experiment’s purpose •Cannot accommodate all research questions•Order/sequence effects occur when participants are affected by the order in which they encounter conditions

Within-Subjects Designs:More on order effects

•Progressive effects occur when participants are altered by the sequence of conditions they encounter (e.g., acquiring experience with the task and thus improving performance) •Carryover effects occur when participants’ responses in one condition are affected by the prior condition (e.g., taste of stimulus from Trial 1 lingers during Trial 2) •The cure? COUNTERBALANCING

Within-Subjects Designs:Counterbalancing Goals

1.Every condition of the IV appears equally often in each position2.Every condition appears equally often before and after every other condition3.Every condition appears with equal frequency before and after every other condition within each pair of positions in the overall sequence

Within-Subjects Designs:All Possible Orders

•Also known as complete counterbalancing •Establish all possible sequences (n!) of IV conditions, and assign equal number of participants to each sequence •Every possible confounding effect is counterbalanced (hence the name!) •Requires a large number of participants to satisfy all counterbalancing goals

Within-Subjects Designs:Latin Square

•Matrix design wherein matrix structured so that each condition appears only once in each column and each row

Within-Subjects Designs:Latin Square

•Accomplishes goals of all-possible-orders design except Goal 3 •When IV has odd number of conditions, cannot construct a single matrix that accomplishes Goal 2 if using a Williams Latin Square •Alternative method is random starting order with rotation

Within-Subjects Designs:Random-Selected-Orders

•Subset of orders is randomly selected from the set of all possible orders•Each order administered to one participant •Refrain from using with small number of participants

Within-Subjects Designs:Other Options

•Two designs wherein participants encounter each condition more than once

•May be more practical than other counterbalancing designs, lets you examine reliability of participants’ responses, and extends generalizability of results

Within-Subjects Designs:Block-Randomization-Design

•Participants encounter all conditions within a block and are exposed to multiple blocks •Each block contains a newly randomized order of conditions

How does this block randomization design differ from that applied in between-subjects designs?

Within-Subjects Designs:Reverse-Counterbalancing

•Also called an ABBA-counterbalancing design •Participants first receive random order of all conditions•Participants then receive conditions once more in reverse order •Watch out for nonlinear order effects!

Making Sense of Experimental Data

• First compute descriptive statistics (e.g., mean for each condition)

• Inferential statistics then used to establish whether findings are statistically significant

Are differences between conditions due to chance?Remember that p < .05 criterion?


• t test examines mean difference between two conditions

• ANOVA (analysis of variance) used with three or more conditions

Would researchersuse a t test or ANOVAhere?


• An ANOVA would be preferable given that there are 3 conditions

• If ANOVA reveals a statistically significant overall pattern of mean differences, post-hoc tests can reveal which which means differ

single-factor experimental designs slides prepared by alison l. o’malley passer chapter 8

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