NR 449 Comprehensive Resource To Help You Ace 2026-2027 Exams Includes
Frequently Tested Questions With Elaborated 100% Correct Complete Solutions
Guaranteed Pass First Attempt!! Current Update!!
Confidence that a treatment or condition made a ditter-
Internal Validity
ence
Ability to rule out rival explanations; generalizability of
External Validity
results
1. Historical ettects
2. Maturation ettects
3. Testing ettects
4. Instrumentation ettects
what are 9 ditterent threats to internal validty? 5. Consent ettects
6. Hawthorne ettects
7. Selection ettects
8. Attrition
9. Multiple Treatment ettects
Historical ettects (Threat to Internal Validity) Events occurring during data collection
Changes in subjects over time (problematic in longitudinal
Maturation ettects (Threat to Internal Validity)
studies)
Testing ettects (Threat to Internal Validity) Improvments related to retaking a test
Instrumentation ettects (Threat to Internal Validity) Changes in instrument or data collections devices
Consent ettects (Threat to Internal Validity) Consenting subjects ditter from nonconsenting subjects
Individuals modify behavior in response to being ob-
Hawthrone ettects (Threat to Internal Validity)
served
Selection ettects (Threat to Internal Validity) Selection/assignment to groups is not objective
Attrition (Threat to Internal Validity) Loss of sujects during study
Multiple Treament ettects (Threat to Internal Validity) Diflculty determining eflciency of multiple treatments.
1. population validity
What are two types of external validity?
2. ecological validity
Population Validity
, NR 449 Comprehensive Resource To Help You Ace 2026-2027 Exams Includes
Frequently Tested Questions With Elaborated 100% Correct Complete Solutions
Guaranteed Pass First Attempt!! Current Update!!
Generalizability from sample to population
How closely subjects represent the population
Generalizability from environmental conditions to other
Ecological Validity
environmental conditions
False positive
Type 1 error
rejecting the null hypothesis when it is actually true
False negative
Type 2 error
Failing to reject the null hypothesis when it is actually false
Because of the possibility of making either error, we can
What is a key point for type 1 and type 2 errors not prove the null hypothesis is absolutely true. We can
only demonstrate a lack of evidence to reject it.
1. use the appropriate design (design matches research
problem)
2. Control of bias
3. Proper measurement/observation (threat doesn't occur
How do you minimize threats to Internal Validity?
or occurs infrequently)
4. Selection and participation control
5. Data collection and measurement standards
6. Appropriate statistical analysis
1. Select tests a priori (before study begins) to reduce
researcher bias
What are 3 rules of Quanitative analysis? 2. Run all tests identified to prevent bias
3. Report all test that were run (selecting specific results is
unethical)
1. evaluate ditterences between groups
2. Assess nature and the direction of relations between
quantitative analysis: goals variables
3. Predict outcomes
4. Reduce data b grouping variables
quantitative analysis: Data Assumptions
Frequently Tested Questions With Elaborated 100% Correct Complete Solutions
Guaranteed Pass First Attempt!! Current Update!!
Confidence that a treatment or condition made a ditter-
Internal Validity
ence
Ability to rule out rival explanations; generalizability of
External Validity
results
1. Historical ettects
2. Maturation ettects
3. Testing ettects
4. Instrumentation ettects
what are 9 ditterent threats to internal validty? 5. Consent ettects
6. Hawthorne ettects
7. Selection ettects
8. Attrition
9. Multiple Treatment ettects
Historical ettects (Threat to Internal Validity) Events occurring during data collection
Changes in subjects over time (problematic in longitudinal
Maturation ettects (Threat to Internal Validity)
studies)
Testing ettects (Threat to Internal Validity) Improvments related to retaking a test
Instrumentation ettects (Threat to Internal Validity) Changes in instrument or data collections devices
Consent ettects (Threat to Internal Validity) Consenting subjects ditter from nonconsenting subjects
Individuals modify behavior in response to being ob-
Hawthrone ettects (Threat to Internal Validity)
served
Selection ettects (Threat to Internal Validity) Selection/assignment to groups is not objective
Attrition (Threat to Internal Validity) Loss of sujects during study
Multiple Treament ettects (Threat to Internal Validity) Diflculty determining eflciency of multiple treatments.
1. population validity
What are two types of external validity?
2. ecological validity
Population Validity
, NR 449 Comprehensive Resource To Help You Ace 2026-2027 Exams Includes
Frequently Tested Questions With Elaborated 100% Correct Complete Solutions
Guaranteed Pass First Attempt!! Current Update!!
Generalizability from sample to population
How closely subjects represent the population
Generalizability from environmental conditions to other
Ecological Validity
environmental conditions
False positive
Type 1 error
rejecting the null hypothesis when it is actually true
False negative
Type 2 error
Failing to reject the null hypothesis when it is actually false
Because of the possibility of making either error, we can
What is a key point for type 1 and type 2 errors not prove the null hypothesis is absolutely true. We can
only demonstrate a lack of evidence to reject it.
1. use the appropriate design (design matches research
problem)
2. Control of bias
3. Proper measurement/observation (threat doesn't occur
How do you minimize threats to Internal Validity?
or occurs infrequently)
4. Selection and participation control
5. Data collection and measurement standards
6. Appropriate statistical analysis
1. Select tests a priori (before study begins) to reduce
researcher bias
What are 3 rules of Quanitative analysis? 2. Run all tests identified to prevent bias
3. Report all test that were run (selecting specific results is
unethical)
1. evaluate ditterences between groups
2. Assess nature and the direction of relations between
quantitative analysis: goals variables
3. Predict outcomes
4. Reduce data b grouping variables
quantitative analysis: Data Assumptions
