Between-Subjects Design Overview
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Questions and Answers

What term describes the variance caused by differences among the means of different treatment groups in a between-subjects design?

  • Chance variance
  • Individual variance
  • Non-systematic variance
  • Systematic variance (correct)

In a between-subjects design, which type of variance must be minimized to ensure accuracy in results?

  • Random variance
  • Non-systematic variance (correct)
  • Confounding variance
  • Systematic variance

What is considered the test-statistic used to calculate the treatment index in a between-subjects design?

  • F-ratio (correct)
  • M-value
  • Z-score
  • T-statistic

If all groups in an experiment are treated the same way, what is expected regarding the scores on aggression?

<p>Scores will be similar across all groups. (D)</p> Signup and view all the answers

What is referred to as the scores varying within groups due to individual differences occurring by chance?

<p>Non-systematic variance (D)</p> Signup and view all the answers

Which of the following is a potential cause for differences in means among treatment groups?

<p>Both treatment effects and random chance factors (D)</p> Signup and view all the answers

What is the primary limitation of a between-subjects design with infants?

<p>Limited attention span of infants (A)</p> Signup and view all the answers

What is systematic variance composed of?

<p>Treatment effects plus experimental error (D)</p> Signup and view all the answers

In the context of aggressive acts, what does a higher mean suggest about the influence of treatment effects?

<p>Treatment effects are likely significant (C)</p> Signup and view all the answers

What does systematic variance compare in a between-subjects design?

<p>Mean scores across multiple treatment groups (A)</p> Signup and view all the answers

What characterizes non-systematic variance?

<p>Variability due to chance factors within the same treatment (D)</p> Signup and view all the answers

What does experimental error include?

<p>Variations in testing environment and individual differences (A)</p> Signup and view all the answers

When subjects are treated alike, what variations are expected?

<p>Variations due to experimental error (C)</p> Signup and view all the answers

Which of the following is true about treatment effects?

<p>They influence the differences in group means (A)</p> Signup and view all the answers

In a study with three conditions (A, B, C), if participants in Condition A have scores of 16, 18, and 10, what does this indicate?

<p>There is non-systematic variance among participants (D)</p> Signup and view all the answers

What is the primary distinction between systematic and non-systematic variance?

<p>Systematic variance arises from different treatment groups, while non-systematic arises from identical groups (D)</p> Signup and view all the answers

What is a potential consequence of having an extraneous variable in a study?

<p>It may prevent accurate conclusions about the treatment's effects. (A)</p> Signup and view all the answers

Which method is considered the most powerful for controlling pre-existing differences among groups?

<p>Randomization of participants into groups. (A)</p> Signup and view all the answers

What does randomization in a study specifically refer to?

<p>The assignment of participants to experimental or control groups. (D)</p> Signup and view all the answers

What is the purpose of matching participants in research?

<p>To equalize groups on critical variables related to the dependent variable. (C)</p> Signup and view all the answers

Which of the following is NOT a characteristic of randomization?

<p>It eliminates the need for matching participants. (D)</p> Signup and view all the answers

What could be a limitation of randomization in small sample sizes?

<p>It can lead to unequal allocation of critical variables. (B)</p> Signup and view all the answers

What must be achieved to create equivalent groups in a between-groups design?

<p>Groups must be created, treated, and composed equally. (D)</p> Signup and view all the answers

Which example would be most relevant for matching participants on critical variables in a study?

<p>Gender, age, and severity of illness. (A)</p> Signup and view all the answers

How does random sampling differ from randomization?

<p>Random sampling selects participants from a population; randomization assigns them to groups. (D)</p> Signup and view all the answers

What is the main goal of random assignment in research?

<p>To distribute individual differences evenly across treatment conditions. (B)</p> Signup and view all the answers

What is the purpose of matching subjects in an experiment?

<p>To ensure that individual differences do not influence the results. (A), To minimize the effects of confounding variables. (D)</p> Signup and view all the answers

Which of the following is a potential issue with differential attrition?

<p>It can lead to biased results if one group loses more participants than another. (D)</p> Signup and view all the answers

How can matching be extended beyond pairs of individuals?

<p>By grouping individuals into blocks based on shared characteristics. (C)</p> Signup and view all the answers

In the context of matching, what is a confounding variable?

<p>A variable that can influence both the treatment and outcome, thus skewing results. (B)</p> Signup and view all the answers

What does the process of random assignment seek to achieve in a matched pairs design?

<p>Randomly distributing participants within matched pairs to conditions. (B)</p> Signup and view all the answers

What characteristic would NOT typically be a basis for matching subjects in an experiment?

<p>Treatment preferences (D)</p> Signup and view all the answers

What effect does attrition have on a study's internal validity?

<p>It has no effect if percentages are the same for all groups. (D)</p> Signup and view all the answers

Which of the following correctly describes the pairing of subjects in the given marijuana study?

<p>Subjects matched according to their cancer stage. (A)</p> Signup and view all the answers

What does a large between-group variance indicate in an experiment?

<p>It suggests a significant treatment effect. (C)</p> Signup and view all the answers

What is the primary purpose of comparing between-group variance to within-group variance?

<p>To determine statistical significance. (D)</p> Signup and view all the answers

What does a small F-ratio typically indicate in a statistical analysis?

<p>Small between-group variance or large within-group variance. (A)</p> Signup and view all the answers

Which of the following strategies can help minimize within-group variance?

<p>Standardizing experimental procedures. (D)</p> Signup and view all the answers

What is assignment bias?

<p>Differences in personal characteristics leading to group disparities. (B)</p> Signup and view all the answers

What should researchers aim to achieve when designing a between-group experiment?

<p>Standardize all experimental conditions. (A)</p> Signup and view all the answers

How does a large within-group variance affect the visibility of treatment effects?

<p>It obscures treatment effects. (C)</p> Signup and view all the answers

What is the relationship between the F-ratio and treatment index?

<p>The F-ratio represents the treatment index. (B)</p> Signup and view all the answers

Which of the following describes the purpose of a single-factor ANOVA?

<p>To provide evidence for causal relationships among groups. (B)</p> Signup and view all the answers

What factor is most affected by environmental variables during an experiment?

<p>Uniformity of treatment across participants. (A)</p> Signup and view all the answers

In a study comparing driving performance under different telephone conditions, what is a critical aspect of the design?

<p>Ensuring distinct differences in participant treatment. (B)</p> Signup and view all the answers

What is a common individual difference that researchers may need to control for during an experiment?

<p>Participants' sex and age. (C)</p> Signup and view all the answers

When the presence of the experimental variable is indicated by a high F-ratio, what does this imply?

<p>There are significant systematic differences between groups. (A)</p> Signup and view all the answers

What is a potential consequence of communication between groups in a study?

<p>True treatment effects being obscured (B)</p> Signup and view all the answers

How can inequities between groups lead to alternate explanations for observed differences?

<p>Through resentful demoralization of one group (B)</p> Signup and view all the answers

Which of the following is an advantage of a between-subjects design?

<p>Reduction of contamination from carryover effects (D)</p> Signup and view all the answers

What is a key reason for the popularity of between-subjects designs?

<p>They help minimize the duration of carryover effects (D)</p> Signup and view all the answers

What is a potential risk of having groups receiving different compensations in an experiment?

<p>It may foster resentment and affect participant behavior (B)</p> Signup and view all the answers

Why is the independence of scores an important feature in between-subjects designs?

<p>It reduces bias from individual participant variability (A)</p> Signup and view all the answers

Which of the following best describes 'diffusion' in experimental groups?

<p>The transfer of information between control and treatment groups (C)</p> Signup and view all the answers

What does the clean design of between-subjects studies primarily avoid?

<p>Carryover and practice effects (C)</p> Signup and view all the answers

Flashcards

Between-subjects design

A research design where different participants are assigned to different conditions or groups.

Systematic variance

The variance in the dependent variable (DV) between groups created by the independent variable (IV).

Non-systematic variance

Variance in the DV within a group due to individual differences or random factors.

Treatment effect

Change in the DV caused by the manipulation of the IV.

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F-ratio

A statistical test comparing the variance between groups to the variance within groups.

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Between-subject variance

Differences in the mean scores of the treatment groups caused by the treatment.

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Experimental Error

Random or unpredictable factors influencing the DV, not related to the treatment.

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Dependent Variable (DV)

The variable measured to see if the independent variable has an effect.

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Within-Group Variance

Variance within each treatment group, due to chance factors (experimental error).

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Experimental Error vs. Treatment Effect

Distinguishing whether differences observed are due to the treatment or random error within the groups.

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Participant Condition

Different treatments or circumstances assigned to individual participants in an experiment.

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Matching in Research

A technique used to control for extraneous variables by creating groups of participants with similar characteristics.

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Matched Pairs

Two participants who are similar in terms of a specific variable, but are assigned to different treatment conditions.

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Confounding Variable

A variable that systematically influences the dependent variable, obscuring the true effect of the independent variable.

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Matching Across Blocks

Extending matching to groups larger than pairs, creating blocks of individuals with similar characteristics.

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Attrition in Research

Participants dropping out of a study before completion, which can threaten internal validity.

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Differential Attrition

Unequal dropout rates between groups in a study, potentially influencing the results.

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Threats to Internal Validity

Factors that can undermine the confidence in concluding that the independent variable caused the observed changes in the dependent variable.

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Internal Validity

The extent to which a study's results can be confidently attributed to the independent variable, rather than other factors.

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Extraneous Variable

A variable that is not the independent variable but can affect the dependent variable, potentially confusing the results of the study. It can be a factor in the environment or a characteristic of the participants.

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Confound

An extraneous variable that systematically varies with the independent variable, making it impossible to determine whether the observed changes in the dependent variable are due to the independent variable or the confound.

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Between Groups Design

A research design where different participants are assigned to different groups (or conditions) of an experiment to compare the effects of the independent variable.

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Randomization

The random assignment of participants to experimental groups, ensuring that each participant has an equal chance of being assigned to any group.

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Random Sampling vs. Randomization

Random sampling involves selecting participants from a population randomly to participate in the study. Randomization is the random assignment of selected participants into different experimental groups.

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Matched Groups

In a between-groups design, creating groups that are similar on important characteristics (e.g., age, intelligence) before the experiment starts.

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Free Random Assignment

A method of randomization where participants are assigned to groups based solely on chance (e.g., coin toss).

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Matching Steps

  1. Identify important variables to match, 2. Measure these variables for each participant, 3. Create pairs of participants with similar values, 4. Randomly assign one member of each pair to each group.
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Limitations of Small Samples

With small samples, random assignment might not completely equalize differences between groups, making it harder to draw reliable conclusions about the effect of the independent variable.

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Nuisance Effects

Factors that can influence the outcome of an experiment but are not of primary interest to the researcher. Randomization helps minimize these effects.

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Diffusion of Treatment Effects

When the treatment effect from one group spreads to another group, potentially masking the true treatment impact.

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Resentful Demoralization

When a group feels disadvantaged or unfair treatment compared to another group, affecting their behavior and potentially skewing results.

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Carryover Effects

When the effects of a previous treatment condition carry over and influence the results of subsequent treatment conditions.

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First-Order Carryover

The effect of the first treatment condition influences the second treatment condition.

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Second-Order Carryover

The effect of the first treatment condition influences the third treatment condition, even without directly affecting the second condition.

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Nth-Order Carryover

The effect of a treatment condition can influence any subsequent treatment condition, regardless of how many steps are in between.

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Advantages of Between-Subjects Designs

Between-subjects designs offer several advantages, including simplicity, clean results without carryover effects, faster participant completion time, and the ability to establish causality.

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Why Between-Subjects Designs are Popular

Between-subjects designs are popular because they minimize carryover effects, allowing for a clean interpretation of results.

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Between-group variance

The variability between the average scores of different treatment groups. Reflects the potential effect of the independent variable.

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Treatment index

A measure of how effective the treatment (Independent Variable) is in influencing the dependent variable. It is calculated as the F-ratio.

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Significant F-ratio

An F-ratio that is large enough to indicate that the differences between group means are unlikely due to chance and probably caused by the treatment.

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Non-significant F-ratio

An F-ratio that is small and suggests that the differences between group means could be due to random chance, not the treatment.

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Large between-group variance is good

Indicates that the different treatment groups have significantly different means, suggesting that the treatment caused the effect.

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Large within-group variance is bad

Indicates a lot of variability within each group, making it harder to identify a clear effect of the treatment.

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Minimize within-group variance

Reduce the variability within each group to increase the chance of finding a significant treatment effect by using techniques like standardization and controlling individual differences.

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Assignment bias

When the participants are not evenly distributed across groups due to the selection process, leading to systematic differences between groups.

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Individual differences

Variability between participants that can influence the outcome of an experiment, even if random assignment is used.

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Environmental variables

Factors in the environment that can affect participants' behavior differently, potentially introducing bias into the results of an experiment.

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Single-factor multiple-group design

A research design where multiple groups are compared based on one independent variable with more than two levels.

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Maximize between-group differences

Maximize the effect of the IV by ensuring the different treatment groups are truly distinct and different. Good experimental design strategy.

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Minimize within-group differences

Reduce the variability within each group to make the effect of the IV more apparent by using techniques like standardization of procedures.

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Hold extraneous variables constant

Controlling factors that are not the independent variable but could influence the dependent variable to make sure the IV is the only factor that changes the DV.

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Study Notes

Between-Subjects Design

  • A different group of participants is assigned to each condition
  • Each group receives a different experimental treatment (value of the IV) and the groups are compared
  • Separate groups of participants are used for the different conditions
  • Data (on the DV) is compared across groups to look for differences
  • Participants experience only one level of the IV, so there is one score on the DV per participant
  • These are called independent measures or independent-measures experimental designs
  • Some independent variables can only be measured in a between-subjects design
    • Examples: age, gender
  • Other independent variables can be measured in a between- or within-subject design
    • Examples: teaching method, video condition

Outline of Between-Subjects Design

  • The between-subjects design (also called between-groups or independent groups design)
  • Systematic and non-systematic variance
  • The F-ratio
  • Confounding variables, including:
    • Individual differences
    • Environmental variables
  • Randomization
  • Additional threats to internal validity
  • Advantages/disadvantages

Systematic Variance

  • Between-subjects (systematic) variance: differences among the means of different treatment groups
  • Differences in means could be due to treatment effects or chance factors
  • Compare differences between means
  • Hypothetical: If we treated the groups the exact same way, would you expect to see the exact same scores on aggression?
  • No, there will always be error and some differences between means

Non-Systematic Variance

  • Within-group (non-systematic) variance: any differences (variation) between subjects who are treated alike
  • Within any given treatment group, all subjects have been treated identically
  • Any variability can only be a result of chance factors
  • Non-systematic (within-group) variance is experimental error

Testing Group Differences

  • Use between-group and within-group variance to form the condition or treatment index, on which the F-ratio is based
  • F-ratio responsive to absence or presence of treatment effects (the effect of the IV)
  • Treatment index = between-groups variance/within-group variance
  • The treatment index is called the F-ratio

Between-Subjects F-Ratio

  • To determine statistical significance, compare between-group variance to the within-group variance
  • F = between-group variance/within-group variance
  • When there is large within-group variance, it is difficult to see an effect
  • Large between group variance is good
  • Large within group variance is bad

Comparing > Two Groups

  • Single-factor multiple-group design: compares driving performance under three telephone conditions (cell phone, hands-free phone, no phone)
  • Analyzed with a single-factor analysis of variance (ANOVA)—between-subjects = independent measures
  • Provides stronger evidence for real cause-and-effect relationships compared to two-group designs

Between-Groups Design

  • Researchers try to maximize between-group differences (e.g., 1 hour vs. 1 hour 15 mins of violent cartoons)
  • Researchers try to minimize within-group differences by ensuring all participants within groups are treated the same (standardised experimental procedures)
  • Minimize individual differences

Individual Differences

  • Any personal characteristic that differs from one participant to another (e.g., age, sex, IQ, SES, personality, relationship status, health)
  • Want to make sure the different groups are as similar as possible, except for the IV
  • Assignment bias: when the process of assigning participants produces groups with different characteristics, threatening internal validity
  • Example: study of drivers' memory for traffic patterns with/without cell phones. If scores in one group are different from those in the other, the difference may be due to the treatment (diff. phone use) or the age difference

Environmental Variables

  • Any characteristic in the environment that may differ (e.g., room, lighting, time of day, noises, presence of researcher)
  • If those differ between groups, we may have an extraneous variable that becomes a confound
  • Can no longer say treatment caused outcome; could be an environmental factor

Between Groups Design

  • To establish equivalent groups of participants, they must be created equally, treated equally, and composed of equivalent individuals

Limiting Individual Differences + Environmental Variables

  • Randomization: participants are randomly assigned to groups to ensure groups are as equal as possible before treatment (most powerful technique to control pre-existing differences)
  • Randomization is NOT the same thing as random sampling

Limiting Individual Differences + Environmental Variables (cont.)

  • Randomly assign to different conditions to ensure even distribution of individual differences across conditions

Free Random Assignment

  • Coin toss to ensure participants are assigned to groups solely on the basis of chance; each person has an equal chance of being placed in any treatment condition

Limiting Individual Differences + Environmental Variables (cont.)

  • Matching: participants are matched on critical variables (e.g., intelligence, gender, age, severity of illness)
  • Match subjects on pre-existing differences related to the DV to guarantee equivalent groups on critical variables

Matching across Blocks

  • Matching can be extended to larger units than pairs (e.g., IQ blocks: high, medium and low groups)

Threats to Internal Validity

  • Attrition: refers to participants leaving the study before completion; not a problem if members of all groups leave at the same rate, but problematic if it's differential attrition (different groups leave at different rates)
  • Communication between groups: diffusion may occur—treatment effects spread from one condition to another condition; one group benefits from information from another group
  • Resentful demoralization: one group might receive course credit while another group receives payment; any perceived inequity can influence behavior

Advantages of Between-Subjects Design

  • Simple design
  • Each score is independent from other scores, so it is clean and uncontaminated
  • No carryover, practice, or fatigue effects
  • Experiment takes less time for each participant
  • Causality can be established
  • Carryover effects are of unknown duration; one can estimate the bias introduced by carryover effects. (Carryover effects create more bias as the number of conditions increase)

Disadvantages of Between-Subjects Designs

  • Requires many participants

Other Disadvantages

  • Assignment bias, experimenter-expectancy, and subject-expectancy biases
  • Solutions: participants and experimenters should be blind to the conditions; data analysts should also be blind to the conditions
  • When to use between-subjects designs
  • When to avoid between-subjects designs
  • Example of practical study design with between-subjects design

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Description

This quiz covers the fundamental concepts of between-subjects design in experimental research. It explains how different participant groups are assigned to various conditions and compares the results across these groups. Key terms like independent measures, systematic variance, and confounding variables are also discussed.

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