A disadvantage of holding a variable constant is that it limits an experiment’s external validity.

Experimental research strategy

• is to establish the existence of a cause-and-effect relationship between two variables.

1. The first step in demonstrating a cause-and-effect relationship is to demonstrate that the “cause” happens before the “effect” occurs.
2. To establish that one specific variable is responsible for changes in another variable, an experiment must rule out the possibility that the changes are caused by an extraneous variable.

Rour basic elements of an experimental study

1. Manipulation. The researcher manipulates one variable by changing its value to create a set of two or more treatment conditions.
2. Measurement. A second variable is measured for a group of participants to obtain a set of scores in each treatment condition.
3. Comparison. The scores in one treatment condition are compared with the scores in another treatment condition. Consistent differences between treatments are evidence that the manipulation has caused changes in the scores (see Box 7.1).
4. Control. All other variables are controlled to be sure that they do not influence the two variables being examined.

Experiment/ Turn Experiment

• An experiment or a true experiment attempts to show that changes in one variable are directly responsible for changes in a second variable. 

• In an experiment, the independent variable is the variable manipulated by the researcher. In behavioral research, the independent variable usually consists of two or more treatment conditions to which participants are exposed.

• In an experiment, a treatment condition is a situation or environment characterized by one specific value of the manipulated variable. An experiment contains two or more treatment conditions that differ according to the values of the manipulated variable.
  

• Levels are the different values of the independent variable selected to create and define the treatment conditions.

• The dependent variable is the variable that is observed for changes to assess the effects of manipulating the independent variable. The dependent variable is typically a behavior or a response measured in each treatment condition.
  

• Extraneous variables are all variables in the study other than the independent and dependent variables.
  

Problem One: The Third-Variable Problem

• Although a study may establish that two variables are related, it does not necessarily mean that there is a direct (causal) relationship be¬ tween the two variables. It is always possible that a third (unidentified) variable is controlling the two variables and is responsible for producing the observed relation.

Problem Two: The Directionality Problem

• Although a research study may establish a relationship between two variables, the existence of a relationship does not always explain the direction of the relationship. The remaining problem is to determine which variable is the cause and which is the effect.

• To establish a cause-and-effect relationship, an experiment must control nature, essentially creating an unnatural situation wherein the two variables being examined are isolated from the influence of other variables and wherein the exact character of a relationship can be seen clearly.
• there is always a risk that the conditions of an experiment are so unnatural that the results are questionable.

• consists of identifying the specific values of the independent variable to be examined and then creating a set of treatment conditions corresponding to the set of identified values.
• The primary purpose of manipulation is to allow researchers to determine the direction of a relationship.
• A second purpose for manipulation is to help researchers control the influence of outside variables. In an experiment, researchers must actively manipulate the independent variable rather than simply waiting for the variable to change by itself. If you let variables change on their own, it is always possible that other variables are also changing, and these other variables may be responsible for the relationship you are observing.

• In the context of an experiment, the particular concern is to identify and control any third variable that changes systematically along with the independent variable and has the potential to influence the dependent variable.
• experiment must rule out any other possible explanation for the observed changes; that is, eliminate all confounding variables.
• an extraneous variable becomes a confounding variable only if it influences the dependent variable.
• a confounding variable must vary systematically with the independent variable.

Holding a Variable Constant

• An extraneous variable can be eliminated completely by holding it constant.
• By standardizing the environment and procedures, most environmental variables can be held constant.
• Ex: only using 6 year old
• Whenever a variable is prevented from reaching its natural range of variation, the external validity of the research is limited.

Matching Values Across Treatment Conditions

• matching the levels of the variable across treatment conditions.
• Another common form of matching is to ensure that the average value is the same (or nearly the same) for all treatments.
• Matching involves assigning individuals to groups so that a specific variable is balanced, or matched, across the groups. The intent is to create groups that are equivalent (or nearly equivalent) with respect to the variable matched.

• Randomization is the use of a random process to help avoid a systematic relationship between two variables.
• Random assignment is the use of a random process to assign participants to treatment conditions.
• Its primary advantage is that it offers a method for controlling a multitude of variables simultaneously and does not require specific attention to each extraneous variable.
• However, randomization does not guarantee that extraneous variables are really controlled; rather, it uses chance to control variables.
• In restricted random assignment, the group assignment process is limited to ensure predetermined characteristics (such as equal size) for the separate groups.

Comparing Methods of Control

• The goal of an experiment is to show that the scores obtained in one treatment condition are consistently different from the scores in another treatment, and that the differences are caused by the treatments.

Pros & Cons of Control Methods

Cons

• require some extra effort or extra measurement
• limiting generalization (external validity).
  
• randomization is not guaranteed to be successful;

Pros

• controlling a wide variety of variables simultaneously.

• The term experimental group refers to the treatment condition in an experiment.
• The term control group refers to the no-treatment condition in an experiment.

Control group; Two Categories

1. no-treatment control groups
2. placebo control groups.
   

No-treatment Control Group

• In an experiment, a no-treatment control group is a condition in which the participants do not receive the treatment being evaluated.
• The purpose of the no-treatment control is to provide a standard of normal behavior, or baseline, against which the treatment condition can be compared.

• The placebo effect refers to a response by a participant to an inert medication that has no real effect on the body. The placebo effect occurs simply because the individual thinks the medication is effective.
  
• A placebo control group is a condition in which participants receive a placebo instead of the actual treatment.

• A manipulation check is an additional measure to assess how the participants perceived and interpreted the manipulation and/or to assess the direct effect of the manipulation.
  
• A manipulation check directly measures whether the independent variable had the intended effect on the participant.

Two ways to check the manipulation.

1. an explicit measure of the independent variable.
2. embed specific questions about the manipulation in a questionnaire that participants complete after their participation in the experiment.

manipulation check impotent when: 

1. Participant Manipulations.
2. Subtle Manipulations.
3. Simulations.
4. Placebo Controls.

• A simulation is the creation of conditions within an experiment that simulate or closely duplicate the natural environment in which the behaviors being examined would normally occur.
  
• Mundane realism refers to the superficial, usually physical, characteristics of the simulation, which probably have little positive effect on external validity.
• Experimental realism, on the other hand, concerns the psychological aspects of the simulation; that is, the extent to which the participants become immersed in the simulation and behave normally, unmindful of the fact that they are involved in an experiment.
• E.x.: The Stanford prison study

• Field study is research conducted in a place that the participant or subject perceives as a natural environment.
• “bystander apathy” in emergency situations experiments

• A between-subjects experimental design, also known as an independent measures experimental design, requires a separate, independent group of individuals for each treatment condition. As a result, the data for a between subjects design contain only one score for each participant.
  it compares different groups of individuals.
• The general goal of a between-subjects experiment is to determine whether differences exist between two or more treatment conditions.
• A between-subjects design allows only one score for each participant. Every individual score represents a separate, unique participant.
• The primary disadvantage of a between-subjects design stems from the fact that each score is obtained from a unique individual who has personal characteristics that are different from all of the other participants.

• Individual differences are personal characteristics that can differ from one participant to another.
• two problems:

1. Individual differences can become confounding variables.
2. Individual differences can produce high variability in the scores, making it difficult to determine whether the treatment has any effect.

• the researcher gets two sets of scores, both obtained from the same sample. This strategy is called a within-subjects design
• The defining characteristic of a within-subjects design is that it uses a single group of participants and tests or observes each individual in all of the different treatments being compared.
• each participant experiences all of the different levels of the independent variable.
• a within-subjects design looks for differences between treatment conditions within the same group of participants.
• In the context of statistical analysis, a within-subjects design is often called a repeated-measures design because the research study repeats measurements of the same individuals under different conditions

Other Confounding Variables

• Confounding from individual differences, which is called assignment bias.
• Confounding from environmental variables.

1. Created equally. The process used to obtain participants should be as similar as possible for all of the groups.
2. Treated equally. Except for the treatment conditions that are deliberately varied between groups, the groups of participants should receive exactly the same experiences.
3. Composed of equivalent individuals. The characteristics of the participants in any one group should be as similar as possible to the characteristics of the participants in every other group.

Limiting Confounding By Individual Differences

• researchers typically use one of the following three procedures to set up groups for a between-subjects experimental study.
• The three procedures are the same methods that were identified for controlling potentially confounding variables in an experiment

1. Random Assignment (Randomization)
2. Matching Groups (Matched Assignment)
   
3. Holding Variables Constant or Restricting Range of Variability

• Variance is a statistical value that measures the size of the differences from one score to another
• If the scores all have similar values, then the variance is small; if there are big differences from one score to the next, then variance is large.

Differences Between Treatments and Variance Within Treatments

• Between treatments: to establish the existence of a treatment effect by demonstrating that the scores obtained in one treatment condition are significantly different (higher or lower) than the scores in another treatment condition. thus, big differences between treatments are good because they provide evidence of differential treatment effects.
• Variance within treatment: big differences within treatments are bad because the differences that exist inside the treatment conditions determine the variance of the scores,

Minimizing Variance Within Treatments

• Standardize Procedures and Treatment Setting
• Limit Individual Differences
• Random Assignment and Matching
• Sample Size
• The best techniques for minimizing the negative consequences of high variance are to standardize treatments and to minimize individual differences between the participants in the study.

• Differential attrition refers to differences in attrition rates from one group to another and can threaten the internal validity of a between-subjects experiment.

Communication Between Groups

• Diffusion refers to the spread of the treatment from the experimental group to the control group, which tends to reduce the difference between the two conditions.
• an untreated group learns about the treatment being received by the other group, and demands the same or equal treatment. This is referred to as compensatory equalization.
• compensatory rivalry. when participants in an untreated group change their normal behavior when they learn about a special treatment that is given to another group.
• It is also possible that the participants in an untreated group simply give up when they learn that another group is receiving special treatment. This is referred to as resentful demoralization.
• In each case, internal validity is threatened because the observed difference between groups can be explained by factors other than the effects of the treatment.

Two-Group Mean Difference

• the researcher manipulates one independent variable with only two levels. This design is often referred to as the single-factor two-group design or simply the two-group design.
• The primary advantage of a two-group design is its simplicity.
• The primary disadvantage of a two-group design is that it provides relatively little information.
• several groups (more than two) are necessary to obtain a good indication of the functional relationship between an independent and a dependent variable.
• advantage of a simple, two-group design is that it allows the researcher to maximize the difference between treatments by selecting opposite extremes for the independent variable.

Threats to Internal Validity to Within-Subjects Experiments

1. Confounding from environmental variables.
2. Confounding from time-related variables. (5 Subsection)

5 Time-related variables.

1. History: When a group of individuals is being tested in a series of treatment conditions, any outside event(s) that influences the participants’ scores in one treatment differently than in another treatment is called a history effect. History is a threat to internal validity because any differences that are observed between treatment conditions may be caused by history instead of by the treatments.
2. Maturation: Any systematic changes in participants’ physiology or psychology that occur during a research study and affect the participants’ scores are
3. Instrumentation: refers to changes in a measuring instrument that occur over time.referred to as maturation.
4. Regression toward the mean: Statistical regression, or regression toward the mean, refers to the tendency for extreme scores on any measurement to move toward the mean (regress) when the measurement procedure is repeated.
5. Order effects (practice, fatigue, and carry-over effects): Whenever individuals are tested in a series of treatment conditions, participation in one treatment may have an influence on the participants’ scores in the following treatments.

• Order effects occur when the experience of being tested in one treatment condition (participating and being measured) has an influence on the participants’ scores in a later treatment condition(s). Order effects threaten internal validity because any observed differences between treatment conditions may be caused by order effects rather than the treatments.
  

• Carry-over effects occur when one treatment condition produces a change in the participants that affects their scores in subsequent treatment conditions.
  

• Progressive error refers to changes in a participant’s behavior or performance that are related to general experience in a research study but not related to a specific treatment or treatments. Common examples of progressive error are practice effects and fatigue.
  

Dealing With Internal Validity Threats 

• Within-subjects designs can control environmental threats to internal validity using the same techniques that are used in between-subjects designs.
  

1. randomization,
2. holding them constant
3. matching across treatment conditions.

DEALING WITH TIME-RELATED THREATS AND ORDER EFFECTS

• By controlling the time from one treatment condition to the next, a researcher has some control over time-related threats to internal validity.
  
• counterbalancing. In counterbalancing, different participants undergo the treatment conditions in different orders so that every treatment has some participants who experience the treatment first, some for whom it is second, some third, and so on.
• Limitations of Counterbalancing
• this process does not eliminate the order effects. In particular, the order effects are still part of the data, and they can still create problems.
• can distort the treatment means.
• counterbalancing adds the order effects to some of the individuals within each treatment but not to all of the individuals.

Counterbalancing and the Number of Treatments

• The idea behind complete counter¬ balancing is that a particular series of treatment conditions may create its own unique order effect.
• partial counterbalancing simply uses enough different orderings to ensure that each treatment condition occurs first in the sequence for one group of participants, occurs second for another group, third for another group, and so on.
• A simple and unbiased procedure for selecting sequences is to construct a Latin square.
• 
  

Advantages of Within-Subjects Designs

• requires relatively few participants
• eliminates all of the problems based on individual differences that are the primary concern of a between-subjects design.
• In statistical terms, a within-subjects design is generally more powerful than a between-subjects design;

Disadvantages of Within-Subjects Designs

• The primary disadvantage comes from the fact that each participant usually goes through a series of treatment conditions, often with each treatment administered at a different time.
• participant attrition. In simple terms, some of the individuals who start the research study may be gone before the study is completed.

• In a matched-subjects design, each individual in one group is matched with a participant in each of the other groups. The matching is done so that the matched individuals are equivalent with respect to a variable that the researcher considers to be relevant to the study.
• The goal of a matched-subjects design is to duplicate all the advantages of within- and between-subjects designs without the disadvantages of either one.
• that matching can become extremely difficult as the number of matched variables increases and the number of different groups increases.

What are the two major threats to internal validity in within subjects experiments?

Which of the following is a potential problem with holding a participant variable constant?

What strategy do researchers use to hold participant characteristic constant?

Are extraneous variables a threat to external validity?