What is the difference between internal validity and external validity quizlet?

A. how to take statements, some have an assumption, a # have a qualification, they all have strengths and/or an importance statements, and they also typically have weakness statement
*understand how you apply each of these probability sample design
Simple random samples (most basic, list of people)
- find SF from population that you want to generalize
- find a table of random #'s
- label each element on the SF with #'s from 1 to N in order they appear on the list
-go to table and randomly select a # which represents a person in SF
- continue to write more #'s from the sample that your consistent from fashion from the table
- each # picked represents a person of the SF to be
Systematic samples (list of people)
- pick a # between 1 and K as the 1st SF members to be selected for inclusion in the sample
- then pick every Kth case/element after that until you go to the end of the list

K=1/sampling ratio
K=1/(sampling size/population size)
5% sample of 100 ---K=1/(5/100)=1(1/20)=20=K
10% sample of 100 ---K 1/(10/100)=1(1/10)=10=K
20% sample of 100 ---K 1/(20/100)=1(1/5)=5=K
*the larger the designed sample the smaller K will be (the smaller K is, the bigger the sample)
**Sample of 160 ---- SF=16000
K= 1/(sample size/population size)=1(160/16000)=100=K

Stratified samples (list of people, time & effort)
- you would take a simple (or systematic) sample within each of the strata. easiest way to think about this is to divide the SF into its relevant strata and then assign a sampling ration(%) that will be randomly taken from each category of the stratification variable
Cluster samples (list of grouping)
*randomly selet a # of grouping (called cluster) from a list with groups on it
*randomly select smaller sampling elements from the chosen group (clusters)
*they solve the problem of not having a list of elements for the population that you want to generalize to

LIST OF GROUPING
Probability Proportionate to Size (PPS) Samples
*probability cluster picked = size of the cluster/size of population
*randonly chose the same # of elements from each of the chosen clusters
**you can use a computer generated formula to do this
Disproportionate Sampling
*you could analyze the small group data separately and then analyze large group data separately and compare the results then it doesn't matter if the small group is disproportionate or you could use statistical weighting to weight down the disproportionate back to the correct % distribution found in the population.

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Terms in this set (50)

Internal Validity

- the degree to which the results are attributable to the independent variable and not some other rival explanation

External Validity

the extent to which the results of a study can be generalized

Population validity

refers to generalizations related to other groups of people
Do the results generalize for males as well as females?

Ecological validity

refers to generalizations related to other settings, times, contexts, etc.
Does the behavior modification technique work at school as well as at home?

Internal and external validity are related reciprocally which means

Controlling internal validity decreases external validity
Controlling external validity decreases internal validity

Often both types of validity are shown by:

Using one experiment to first demonstrate an effect in a highly controlled environment
(i.e., internal validity), then

Using a second experiment to replicate the study in a more realistic, natural setting
(i.e. external validity)

The Internal Validity

of a study hinges on control of the extraneous variables.
A study has good ______________ if the manipulation of the independent variable is the only reasonable explanation for differences between groups on the dependent variable.

A study with good _________________supports the conclusion that there is a cause-effect relationship

Threats are sometimes referred to as

"rival hypotheses" - Any alternative explanation(s) for results.
These threats weaken support for a cause-effect relationship.

Common Threats to Internal Validity

History
Maturation
Testing
Instrumentation
Statisitcal Regression
Differential Selection
Mortality
Attrition
Interaction

History

Refers to any external event or occurrence, not part of experimental treatment, that may have an impact on the dependent variable

The longer the duration of a study, the more likely it is that history can be a threat
For example: In the exercise study, if the participants were teenagers that were also getting ready for their high-school prom....

Maturation

Refers to the physical, intellectual, and emotional changes that naturally occur within individuals over a period of time.

This is likely to happen when the participants are children or adolescents who are undergoing physical & emotional developmental changes

This threat is especially of concern in longitudinal designs.

Testing

refers to the situation in which a pretest is given and scores on the posttest improve just because subjects have prior experience with the test

This effect exists even when no treatment is administered

This is especially true when the time interval is short, and items can be easily recalled

Instrumentation

Occurs when changes on the dependent variable between pretest & posttest are due to a lack of consistency in the instrument being used to measure the dependent variable.

This occurs when using unreliable measures
Using non-equivalent forms of a test
Form-B is more difficult than Form-A

Using untrained raters
Different raters interpreting the measure differently at each time

The threat of Statistical Regression

regression is most likely to be present when subjects are selected for a study based on extreme scores (very high / very low) on some measure.

When a test that is not perfectly reliable is administered two times, a test taker's score on the second administration is likely to be closer to the mean of the group than the score on the first administration

Regression to the Mean

Differential Selection of Subjects

Occurs when a researcher is not able to randomly assign subjects to groups
In this case, participants in the experimental group are somehow systematically different from the people in the control group (Sampling bias)

For Example: In the exercise study, if people who regularly go to the gym are selected as the experimental group....

Mortality (or attrition)

refers to the concern that subjects who drop out of a study may systematically differ from subjects who remain

This is also of concern in longitudinal studies where researchers can lose track of participants over time.

Interaction

Differential selection of participants may interact with another of the threats to internal validity (typically maturation, history, or testing) to cause another concern.

This occurs when using groups that are already formed. One group could have a systematic advantage (or disadvantage) over the other(s).
Maturing at different rates
Different histories

External validity

is concerned with how well results can be generalized to populations and settings beyond those used in the study.

If the results of the exercise experiment showed that exercising was an effective means to weight loss, can these results be generalized to a larger population?

Population validity

refers to generalizations related to other groups of people
Do the results generalize for males as well as females?

Ecological validity

refers to generalizations related to other settings, times, contexts, etc.
Does the behavior modification technique work at school as well as at home?

Common Threats to External Validity

Pretest Sensitization

Sequencing Effects

Selection-Treatment Interaction
Specificity of Variables & Treatment Diffusion

Participant Effects

Experimenter Effects

Pretest Sensitization

In some situations, the pretest may "sensitize" participants to the treatment.

In these cases it could make participants respond more strongly than they would if they had not been pre-tested.
It is most likely to occur when the dependent variable is related to emotion or attitude.
It makes the effect of treatment appear more different than it actually is.
It affects generalizability to populations that are not pre-tested

Carryover Effect

occurs when the effects from a prior treatment make it difficult to assess the effectiveness of the new treatment.
E.g. many reform initiatives have been implemented in the last 10 years in the public schools. Because these initiatives remain in place until another one takes its place, it is difficult to determine which, if any, program is improving student achievement.

Order Effect

occurs as a result of the order in which the treatment(s) is given.
If you want to examine the effects of caffeine on learning, you might give the participants caffeine on the first day and observe the learning. The second day you give the placebo and observe the learning. The second day the participants are more familiar with the study which could effect the outcome.

Selection-Treatment interaction

occurs when participants are not randomly assigned to treatment groups
Non-representative groups are used and so treatment effects apply only to that group and not to the population in general

This threat can also occur when participants are randomly selected

For instance, suppose you have a list of ten randomly selected schools for a new science initiative. Nine schools on the list turn you down, but the tenth school accepts. It is possible that this tenth school is fundamentally different in some way from the other nine on your list.

Specificity of Variables

Occurs when variables (independent and/or dependent) are not operationalized sufficiently.

Treatment Diffusion

Participants from different treatment groups communicate and share aspects of the treatment with each other

Participant Effects

A researcher often creates an artificial environment to control for extraneous variables, this can affect the way participants react to the treatment.

Participants know they are involved in an experiment and may react differentially

Hawthorne effect

- participants react positively to the 'special' attention given to them and perform better than would be expected of the general population.

John Henry effect

members of the control group feel threatened or challenged by treatment group and try to outperform them.

Novelty effect

participants react with heightened interest or motivation because the treatment is something new and different. This effect tends to wane as the experiment continues.

Placebo effect

Using a 'sugar pill' so it appears that all groups are being treated the same way

Experimenter Bias Effects

When the researcher knows which participants are assigned to which group

Previous knowledge of the participants might also affect experimenter behavior or attitude
For example, if a teacher knows that Mary has a particularly difficult home environment, this may unconsciously affect the way the researcher evaluates or rates her on the dependent variable

Researcher Expectations
For example, the researcher developed a particular instructional strategy and has a strongly held belief that it to be superior to other strategies before experiment begins.

Experimenter personal-attributes

The characteristics of the experimenter affect participant reaction to treatment
Age, gender, race, anxiety level
For example, studies have shown that participants react differently to the same questions depending on the gender and/or race of the interviewer.

Confounding

refers to the intertwining of the effects of the independent variable and the extraneous variables

There are several ways to control extraneous variables in the context of experimental designs

Randomization
Matching
Homogeneous sub grouping
Using participants as their own controls

Some of these methods are used to control for extraneous variables in non-experimental designs as well.

Randomization

if used, there is no reason to believe that the groups differ systematically on any extraneous variable.

Random Selection (external validity)-

selecting participants at random from the population - random sampling

Random Assignment (internal validity)

assigning participants to the treatment groups randomly

Matching

Each participant is matched or paired with another participant who is the same (or similar) on the variable to be controlled

Once a pair is identified, one is randomly assigned to one treatment group and the other to the other treatment group

Alternately, you can rank all members based on control variable and then use two highest as a pair regardless of match similarity and so on.

Limitations of Matching

Participants with no match have to be excluded from the study

Very difficult to match participants on more than two extraneous variables

May not be possible to find a match for participants with "extreme scores"

Homogeneous Subgroups

Does not involve one-to-one matching

The goal is to make groups the same with respect to representation of categories or ranges of the extraneous variable

After forming the subgroups, randomly assign half of the participants to one treatment group and the other half to the other treatment group

Limitations of homogeneous sub grouping

It restricts generalizability

It is not really different from stratified random sampling

You can simply design the study to account for the variable.

Using the Subjects as Controls

For this technique, use a single group of participants and expose them to different treatments one at a time

The same participants get all of the treatments.

However, you must guard against carryover effects and order effects.

To do this, divide the group into two smaller groups and give the treatments, but in different order.

This is called counterbalancing.

Threats to internal validity

Internal validity is always a concern with single-subject research

Two major threats

Instrumentation
Specificity of variables

Controlling threats

Baselines are multiple measures of pretest performance so that stability of the behavior of interest is established.
By repeating baseline measures over a period of time specific threats to internal validity (history and maturation) can be controlled.

Threats to external validity

Lack of external validity is the major concern with single-subject designs
Generalizability is addressed through multiple replications of the same treatment and design that produce similar results for a number of different participants.

Replication

is, therefore, an important aspect of single-subject research
The more one's results are replicated the more confidence one has in the procedures that produced the results

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