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TEXAS A&M / Psychology / PSYC 607 / In what year did penicillin become the soc for treating syphilis, but

In what year did penicillin become the soc for treating syphilis, but

In what year did penicillin become the soc for treating syphilis, but


School: Texas A&M University
Department: Psychology
Course: Experimental Psychology
Professor: Winfred arthur
Term: Fall 2016
Tags: Psychology, 302, experimental, psych302, and tamu
Cost: 50
Name: Psychology 302 Final Exam - Mathur
Description: This review goes covers Ch. (4, 12, 13, 14)
Uploaded: 12/08/2017
20 Pages 245 Views 2 Unlocks


In what year did penicillin become the soc for treating syphilis but the men still were not given treatment, because another goal of the experiment was to see the long-term effects of the disease in post-mortem patients?

Psych 302 Study Final Exam Study Guide

Ch. 4 – Ethics:

A. Ethics in Science

a. Resources:

i. HHS:

1. Federal Policy

ii. APA  

1. Ethics Documents

iii. Minimum Ethics Training Compliance: 

1. CITI – training in human subjects and animal research

b. Objectivity:

i. Scientists have an obligation to minimize bias in research  

1. Biases Towards Hypotheses We also discuss several other topics like What is the meaning of message flow?
Don't forget about the age old question of How do you teach sequences of activities in operant conditioning?

a. Difficult given investment

2. Personal Biases:

What are the three kinds of ethics violations?

If you want to learn more check out What is the meaning of hearing in psychoacoustics?

a. Commitment to diversity

b. Need to consider taboo subjects and ideas formulated from  

diverse sources

c. People with biases make decisions about what questions are  

being studied, and shape what questions will be asked in  

the future (scientists and funding agencies).

B. Nuremberg Code (1947)

a. History:

i. Implemented after the Nuremberg Trials of Third Reich scientists for their  experimentation on human test subjects

ii. Included:

1. Mandate of voluntary consent of human test subjects

a. Excluded vulnerable populations  

b. A participant must be allowed to stop at any time.  

iii. Research Must:

What is the 1974 national research act?

1. Benefit humanity

2. Balance risks/benefits

3. Be well-designed and based off of previous research

C. Milgram Study (1960-1961)

a. Too what lengths will a person go if someone in authority tells them to do it? b. Human test subjects believed they were gibing harmful electrical shocks to other  participants who were actually confederates

D. Declaration of Helsinki (1964)

a. Ethical principles regarding medical research on human test subjects Don't forget about the age old question of What is the proposed solution?

b. Internationally Agreed Upon Code of Ethics

i. Developed by the World Medical Association


ii. Foundation and basis for future codes of ethics and laws across other  nations

c. Further Developed Nuremberg Code

i. Protection and inclusion of vulnerable groups

ii. Physicians moral obligation to look after his or her subjects is absolute iii. Research Ethics Committee must approve – foundation of IRBs 

E. Tuskegee Syphilis Study (1932-1972):  

a. History

i. Longitudinal Study conducted by the Public Health Services.  

ii. 200 Uninfected black men and 300 infected black men.

iii. In 1943 Penicillin became the SOC for treating Syphilis but the men still  were not given treatment, because as another goal of the experiment was  to see the long-term effects of the disease in post-mortem patients.  

b. Three Kinds of Ethics Violations:

i. The men were not treated respectfully

1. The researchers lied to them about what the experiment was  


2. Did not allow the participants to give their full consent.  

3. The low-income families may have felt that they were coerced.  

ii. The men in the study were harmed.  We also discuss several other topics like Why do we have states (national governments)?

1. The men were not given or told about the treatment that could have  cured their disease.  

iii. The researchers targeted a disadvantaged social group.  

c. Direct Consequences:

i. 1974 National Research Act:

1. Established the rule that all federally funded research involving  

human test subjects must be approved by an IRB.  

ii. 1979 Belmont Report:

1. Identified three core principles for research involving human test  


F. Belmont Report  Don't forget about the age old question of How do you discipline a child that won't listen?

a. Respect for Persons:

i. Informed Consent:  

1. Individuals participating in research should be able to make their  

own decisions and every participant is entitled to the precaution -

they should know all the costs and benefits.

2. No Coercion or Undue Influence:

a. Coercion occurs when researchers explicitly suggest that  

the participant will suffer a negative consequence for not  

doing what they say

b. Undue Influence occurs when researchers offer an  

incentive to attractive to refuse

ii. Autonomy:

1. Everyone is entitled to autonomous actions and some groups  

require special protection to insure it.  

2. Ex: (Prisoners, children, people with disabilities)


3. Information must be easily understood


b. Beneficence:

i. Do no Harm

1. Researchers must take precautions to protect participants from  

harm and to insure their well being.  

2. Harm can be both psychological and physical.  

ii. Maximize Benefits and Minimize Risks

1. Consider who benefits from the researcher and who might be  


c. Justice

i. Balance

1. Fair match between who bears the burden and who could receive  

the benefits

2. Protection of vulnerable populations

3. Inclusion of Relevant Populations 

a. Researchers might first ensure that the participants  

involved in a study a re representative of the kinds of  

people who would also benefit from its results.  

G. Ethical Principles  

a. Five General Ethical Principles

i. Beneficence:

1. Do no harm

ii. Justice

1. Fair and balanced

iii. Respect for Persons

1. Informed consent  

iv. Integrity:

1. Having moral principles

a. Ex: Professors are obligated to teach you accurately and  

therapists are required to say up-to-date on empirical  

evidence of therapeutic techniques

v. Fidelity and Responsibility:

1. Fidelity:

a. Consistency and commitment to professional behavior.  

Take ownership of ethical behavior

b. A psychologist must remain dedicated to his or her patients  

and must execute to the best of their ability promises that  

this relationship entails. This relationship must be kept  

professional to the role it serves.  

c. Ex: A clinical psychologists who teaches in a university  

may not serve as a therapist to one of his or her classroom  


d. Ex: Psychologists must avoid sexual relationships with  

their students or client


b. Ten Specific Ethical Standards

i. General:

1. Psychologist members of the APA who violate any of these  

standards can lose their professional license or may be disciplined  

in some other way by the association.  

2. Ethical Standard 8: Research and Publication is by far the most  

important for the context of this class

H. Ethical Standard 8:

a. International Review Boards (8.01)

i. A committee responsible for the interpretation of ethical principles and  who ensure that research using human and animal participants is  

conducted ethically.  

ii. Before conducting a study, researchers must fill out a detailed application  describing their study.  

iii. If an institution conducts research using federal money (such as grants  from the government), then a designated IRB is required. 

iv. An IRB panel in the U.S. includes at least five people from specific  backgrounds

1. >1 Scientist

2. >1 Person with academic interests outside the sciences

3. >1 A community member who has no ties to institution

4. Research for prison populations must include a member who is a  

prisoner advocate

5. Research involving children will be heavily scrutinized.

b. Informed Consent (8.02)

i. Researchers are obligated to explain the study to potential participants in  everyday language. Usually must have written documentation of  

procedures, cost/benefit, what is being recorded, privacy etc.  

ii. Written Consent

1. Indicates informed and voluntary participation 

2. Two copies, one for the researcher and one for the participant

3. Written consent might not be needed when participants answer  

completely anonymous questionnaires, and forms may not be  

required for naturalistic observations

iii. When Consent is not Possible

1. Waiver documentation of consent

2. Assent documentation with children

c. Deception (8.07)

i. Requirements:

1. Should only be done when there are no other alternatives

2. There is no foreseeable harm to the participants

3. The research must be important

ii. Researchers withhold some details of the study from the participant.  1. Omission: Not informing participants of the full scope of the  



2. Commission: Actively lying to the participants

d. Debriefing (8.08)

i. When researchers use deception, they must spend time debriefing each  participant in a structured conversation.  

1. Must be done as soon as possible

2. Must remove harmful effects of deception

3. Cost/Benefit analysis must be done here. Debriefing should not be  done if it would cause more harm than good 

ii. Researchers describe the nature of the deception and explain why it is  necessary.

1. Must emphasize the importance of their research and must attempt  to have honest relationship with the participant.  

2. Non-deceptive studies often include a debriefing session as well e. Animal Research (8.09)

i. Standards:

1. Must follow APA standards and AWA.  

2. Research must have IACUC (Institutional Animal Care and Use  Committee)

ii. Animal Care Guidelines:

1. Replacement:

a. Researchers should find alternatives to animals in  

researcher when necessary.

2. Refinement:

a. Researchers must modify experimental procedures and  

other aspects of animal care to minimize or eliminate  

animal distress.  

3. Reduction:

a. Researchers should adopt experimental designs and  

procedures that require the fewest animal subjects possible

iii. Outcomes:

1. Resulted in numerous benefits

2. Animal research has contributed to countless valuable lessons  about biology, psychology, neuroscience

3. Has made fundamental contributions to both basic and applied  science

f. Research Misconduct

i. Data Fabrication/Falsification (8.10):

1. Fabrication:  

a. Occurs when, instead of recording what really happened in  

a study, researchers invent data that fit their hypotheses.  

2. Falsification:  

a. Occurs when researchers influence the study’s results,  

perhaps by selecting deleting observations from a data set  

or by influencing their research subjects to act in the  

hypothesized way.  

3. Consequences:


a. Scientists use data to test their theories, and they can do so  

only if they know that previously reported data is true and  


4. Reasons:

a. In many universities, the reputation, income, and  

promotions of professors are based off their publication and  

their influence in the field.  

ii. Plagiarism (8.11):  

1. Representing the ideas or words of others as one’s own without  

giving proper credit.  

2. To avoid, a writer must cite the sources of all ideas that are not his  

or her own, to give appropriate credit to the original authors.  


Ch. 12 - Factorial Designs:

A. Overview:

a. Factorial Designs (Factor = IV/predictor variable):

i. Use two or more independent variables/predictors that are  

presented/manipulated at the same time.  

ii. In the most common factorial design, researchers cross the two  

independent variables to study each possible combination, creating a  

condition representing each possible combination of the two (overlaid two  independent variables)

1. This process creates all the number of condition types or cells. 

iii. Allow researchers to test for:

1. Interaction Effects:

a. Whether the effect of the original independent variables  

depends on the level of another independent variable  

b. Interactions can allow researchers to ask, “Does cell phone  

effects on driving depend on age?”

2. Multiple Hypotheses at once

a. All variables have two levels

b. Intuitive Interactions:  

Much of the most important research in psychology explores interactions among  multiple independent variables.  

i. Crossover Interactions: 

1. Lines measuring the effect of the interaction of two independent  

variables on a dependent variable in which both IVs cross over one  


2. Difference in Differences: Has two measurements one of negative  

value and one of positive value across the interception 

ii. Spreading Interaction:

1. Lines are not parallel and they do not cross over each other.

2. When there is an interaction, one can describe it accurately from  

either direction.


a. It is equally accurate to say: “When I am not holding a treat  

there is zero difference between the ‘say sit’ and ‘say  

nothing’ conditions; or “When I am holding a treat, there is  

a large difference between the ‘say sit’ and ‘say nothing’  


b. Often times one can see an interaction  


c. Factors/Predictors:

i. Can be manipulated (true IVs), measured (participant variables), or a  combination of the two (mixed)

1. Participant Variable:

a. A variable whose levels are selected (measured) not  

manipulated. Because the levels are not manipulated,  

variables such as age, gender, and ethnicity are not truly  

independent variables. However, they are used as such in  

factorial designs.

B. Testing Limits

a. One purpose of factorial designs is to test whether an independent variable affects  different kinds of people, or people in different situations in the same way.  b. External Validity:

i. When researchers test an independent variable in more than one group at  once, they are testing whether the effect generalizes.  

c. Showing moderators

i. The process of using a factorial design to test limits is sometimes called  testing for moderators (a variable that changes the relationship between  two other variable)  

ii. Moderator:  

1. An independent variable that changes the relationship between  

another independent variable and a dependent variable

2. Results in an interaction the effect of one independent variable  

depends on the level of another independent variable

C. Testing Theories:

a. In a factorial design, researchers test each independent variable to look for a main  effect – the overall effect of one independent variable on the dependent variable,  averaging over the levels of the other independent variable.  

b. Main effect is a simple difference. In a factorial design with two independent  variables, there are two main effects.  

c. Marginal Means:

i. The arithmetic means for each level of an independent variable, averaging  over levels of the other independent variable. If the sample size in each  cell is exactly equal, marginal means are simple average. If the sample  means are unequal, the marginal means will be computed using the  

weighted average, counting the larger sample more.  

ii. Main effects may or may not be statistically significant.  

iii. Main effect = overall effect


1. When a study’s results show an interaction, the interaction itself is  the most important effect

2. The overall effect of one independent variable at a time

D. Detecting Interactions  

a. Interactions from a Table

i. A table can be used to determine if a study’s results show an interaction ii. Start by computing the two differences (an interaction), then go to the  second level of the first independent variable (always compute the  

differences in the same direction) 

iii. G

b. Interactions from a Graph

i. On a plotted line graph, see whether or not the lines are parallel. If not,  there is probably an interaction

ii. On a bar graph, imagine connecting the tops of each matching bar with  straight lines. If the lines are close, then you would assume that the graph  is parallel and there wouldn’t be an interaction

c. Describing Interactions in Words

i. One of the simpler verbal descriptions starts with one level of the first  independent variable, explaining what is happening with the second  

independent variable at the same level, then moves to the next level of the  first independent variable etc.  

1. Ex: “When people saw photos of alcohol, they were quicker to  

recognize aggression words than neutral words, but when people  

saw pictures of plants, they were slower to recognize aggression  

words over neutral words”

d. Interactions are almost always more important than main effects 

E. Factorial Variations

a. Independent-Groups Factorial Designs

i. In an independent-groups factorial design (between-subjects factorial)  both independent variables are studied as independent groups

1. For a 2X2 Factorial design there will be four different groups  

(cells) of participants in the experiment

2. Some light men drank the placebo alcohol, some light men drank  

the real alcohol, some heavy men drank the placebo alcohol, some  

heavy men drank the real alcohol

b. Within-Groups Factorial Designs

i. (Repeated-measures factorial) both independent variables are manipulated  within-groups

ii. There is only one group of participants but for a 2X2 design, they would  participate in all four cells

1. All participant saw both alcohol photos and plant photos which  

alternated over trials, all participants also responded to both  

aggression-related words and neutral words

c. Mixed Factorial Designs


i. One independent variable is manipulated as an independent groups and the  other ins manipulated as within groups

1. Ex: Age was an independent groups participant variable as  

participants are either young or old, but the cell phone condition  

was a manipulated independent variable as both young and old  

participants were exposed to it.  

F. Increasing the Number of Levels of an Independent Variables

a. General Guidelines

i. The quantity of numbers indicate the numbers of independent variables ii. The value of each of the numbers indicate the number of levels for each of  the independent variables

iii. The product of each of the numbers is the number of cells

iv. Marginal means can still be computed, easiest way to detect a interaction  is through computing the marginal means and plotting them on a graph G. Increasing the Number of Independent Variables

a. General Guidelines

i. Best way to depict a three-way design is to construct the original 2X2  Table, twice and create side by side graphs

ii. Three independent variables will result in three main effects to test, each  representing a simple, overall difference: the effect of one independent  variable, averaged across the other two independent variables

1. (The independent variable at the bottom of the chart is the one in 

which the main effect is being tested for) 

iii. When describing each main effect, you don’t mention the other two  independent variables because you averaged across them

1. A three-way interaction, if it is significant, means that the two-way

interaction between two of the IV depends on the level of the third  

independent variable.  

iv. Three-Way Interaction Present If:

1. You would find a three-way interaction whenever there is a two

way interaction for one level of a third independent variable but  

not for the other.

2. If a graph shows two different two-way interactions

H. Statistical Test


i. Output F statistic and p value

ii. Effect Size

1. Eta squared (n)

2. Partial eta squared (np^2)


a. Values are smaller than for “d” and “r” for even strong  


b. .2 is a strong effect size

I. Identifying Factorial Designs in Readings

a. In Journal Articles


i. Look for:

1. Dual task

2. Factorial


b. In Popular Press

i. Look for:

1. It depends

2. Look for participant variables

Ch. 13 – Quasi-Experiments and Small–N Designs:

A. Quasi-Experiments

a. Overview:

i. Researchers do not have full experimental control.  

ii. Start by selecting an IV and DV, then participants are exposed to each  level of the IV

iii. Researchers might not be able to randomly assign participants to one level  or the other 

iv. Similar to correlational studies in threats to internal validity, however,  more manipulation of variables is done in Quasi 

b. Advantages/Disadvantages  

i. Ecologically valid/real life groups or changes

ii. Contains Internal validity threats

iii. The best design is the one that answers a research question the best 

c. Contrasting Designs:

i. True Experiment:

1. A study or research design in which the variables are manipulated.  

Experimenter has complete control over the assignment of subjects  

to conditions and presentation of conditions to participants

ii. Non-Experiment:  

1. Experimenter has no control over presentation of IVs; can only  

occur what is happening

iii. Quasi-Experiment:  

1. Researchers select participants for different conditions from pre

existing groups

a. “Ex post facto” experiments – “after the fact”

2. No random assignment but experimenters still have some  

manipulation and experimental control

3. Always a trade off between ecological validity and internal

4. Problems:

a. Preexisting groups differences

b. Confounds (subject variable) exist when participants are  

assigned to conditions based upon group membership

B. Group Designs

a. Repeated-Measures Design


i. Participants experience all levels of an independent variable. The  researcher takes advantage of an already-scheduled event, a new policy or  regulation, or a chance occurrence to manipulate the independent variable.  b. Interrupted Time-Series Design:

i. A quasi-experimental study that measures participants repeatedly on a  dependent variable before, during, and after the “interruption” caused by  some event

ii. Extension of the pretest/posttest design as it allows the same group to be  compared over time by considering the trend data before and after the  experimental manipulation

1. Ex: Judicial decision making: As the day went on, judges were  increasingly less likely to approve parole, unless the hearing came  directly after a snack break

2. A stronger argument can be made to eliminate maturation and  testing effects

c. Nonequivalent Control Group Design:

i. A quasi-experimental study that has at least one treatment group and one  comparison group, but participants have not been randomly assigned to  the two groups

ii. All members of group one experience the manipulation while all members  of group two do not 

iii. Nonequivalent Control Group Pretest/Posttest Design:

1. Version of this type of study as well that measures the subjects  before and after there self selected group choice

a. Ex: self-confidence levels of women before and after  

plastic surgery vs. a group of women who had no surgery

iv. Nonequivalent Control Group Interrupted Time-Series Design: 1. Independent variable was studied both as a repeated-measures  variable and as an independent-groups variable.  

a. Ex: Television access and crime rates

d. Types of Results:

i. Desired:

1. The experimental group has a statistical difference compared  to the control group between the pretest and posttest (ideally,  there should not be a change in the control group)

ii. Interpretable:

1. Statistical difference between the pretest and posttest for the  experimental group, but perhaps the control group started at a  level before or below the experimental group.

a. Ex: The types of women who get plastic surgery might  

have lower self-esteems in general while compared to  

women who do not.

iii. Uninterruptable:

1. Control group and experimental group have data from pretest  to posttest that are parallel.


2. Control group has scores that show no change pretest or  

posttest but are still greater than the posttest or pretest for the  

experimental group.

C. Types of Quasi Experiments:

a. Single Factor Design Without Manipulation:

i. One quasi-experimental IV (group membership)

ii. Levels of IV are preexisting

b. Single Factor Design with Manipulation:

i. All members of one group selected to be in one condition (ex:  

treatment condition)

ii. All members of another group selected to be in another condition (ex:  control group)

iii. IV confounded with group membership

c. Mixed Factorial Design with Manipulation:

i. One between subjects quasi-experimental IV that is not manipulated ii. One within subjects experimental IV that is manipulated

d. Natural Experiments:

i. A form of Quasi-Experiments where the intervention (or assignment  to conditions) happens due to external circumstances that are thought  to be or close to random

ii. Used to evaluate causal hypotheses when true random assignment is  impossible or unethical

iii. Make sure the groups are similar in all respects except for the factor  that the researcher thinks is causal 

1. Odds Ratio: 

a. Measure of Association 

b. >1 indicates an association/greater odds for the 

dependent variable 

Four Validities in Quasi-Experiments

A. External:

a. Generalizability:

i. Higher ecological validity, real world problems or context

ii. Can we generalize to other outcomes (DVs), conditions (IVs),  

populations, settings

B. Internal:

a. Selection Effects

i. General:

1. Only relevant for independent-group designs, not for repeated  

measures designs

2. Applies when the groups at the various levels of the  

independent variable leads to differences in the dependent  

variables between the two groups

ii. Counter:

1. Within-subjects, or repeated measures design

2. Matched-Group Design (or analysis)


a. Using information on age, sex, BMI, income, and several  

other variables to compare groups of people

3. Wait-List Design

a. All the participants plan to receive treatment, but are  

assigned to do so at different times

iii. Confounds:

1. Examine potential third variables to help rule out systematic  effects

b. Maturation Effects

i. General:  

1. Occurs when, in an experimental or quasi-experimental design  with a pretest and posttest, a treatment group shows an  

improvement over time, but it is not clear whether the  

improvement was caused by the treatment or whether the  

group would have improved spontaneously.

ii. Counter:

1. Repeated Interrupted Time-Series Designs help rule out  

spontaneous changes (two groups serve as a mutual control)

2. Fdas

c. History Threat

i. General:

1. Occurs when an external, historical event happens for  

everyone in a study at the same time as the treatment variable. 2. It is unclear whether the outcome is caused by the treatment  or by the common external event or factor

ii. Counter:

1. Control/Comparison Group

2. Beware of selection-history threats which will affect one group  systemically

d. Regression to the Mean

i. General:

1. Occurs when an extremely likely finding is caused by a  

combination of random factors that are unlikely to happen again.

2. A threat primarily when a group is selected because of its  

extremely high or low scores.  

ii. Counter:

1. Longitudinal designs in which the researcher tests the participants  repeatedly

e. Attrition Threat

i. General:

1. Occurs when people drop out of a study overtime  

2. Becomes an internal validity issue when people drop out of a study  for systematic reasons.  

ii. Counter:

1. Analyze dropouts vs. completers

2. Missing Value Analysis:


a. As long as the dropouts are not systematically different  

from the completers, there is no attrition theat.  

f. Testing/Instrumentation Effects

i. General:

1. A kind of order effect in which participants tend to change as a  

result of having been tested beforehand. Testing might cause  

people to improve or get worse.

2. An instrument that changes how it measures over repeated use

ii. Counter:  

1. Control/Comparison groups

g. Observer Effects:

i. Observer Bias: create a double-blind, or masked study

ii. Placebo: Include a control/comparison group  

iii. Demand Characteristics: Were the participants able to detect the goals of  the study?

C. Statistical:

a. Measure the effect size

i. (Cohen’s D)  

ii. Looks at the how large the difference between the two groups were

b. Measure Statistical Significance  

i. (>p)

ii. Effect size and sample size required

D. Construct Validity

a. How well were the variables measured/manipulated?

Small-N Designs:

A. Differences Between Small Samples and Large Samples

a. Large:

i. Participants are grouped

ii. Data from individual participants are not of interest in themselves; data  from all participants in each group are studied together

iii. Data represented as group averages 


b. Small:

i. Each participant is treated as a separate experiment

ii. Small-N designs are almost repeated-measure designs in which  

researchers observe how the person or animal responds to several 

systematically designed conditions 

iii. Individual’s data is presented

iv. Instead of gathering a little information from a larger sample, they obtain a  lot of information from just a few cases.  

1. Single-N Designs restricts study to just one person/animal

B. Four Validities in Small-N Designs

a. Internal:  

i. Can be strong if properly designed


b. External:

i. Problematic, depends on the “target” of the generalization

c. Construct:

i. Strong when measures and observations are precise

d. Statistical:  

i. Can be difficult, as for they’re to be statistical power, effect sizes need to  be large.  

C. Three Types of Small-N Designs

a. Stable-Baseline Design:

i. A study in which a researcher observes behavior for an extended baseline  period before beginning a treatment or other intervention

ii. If the behavior is more stable during the baseline, the researcher is more  certain of the treatments effectiveness.  

iii. Longer pretest allows researchers to effectively say that treatment has a  discernable effect and is not spurious

b. Multiple-Baseline Design

i. Researchers stagger their introduction of an intervention across a variety  of contexts, times or situations

ii. Ex: Girl who repeatedly touches her hair, face, and objects is given a  baseline for each. They target the first action, touching her hair, and  

overcorrect the behavior for a few days

iii. Three behaviors act as control for each other to improve internal validity iv. Provide comparison conditions to which a treatment or intervention can be  compared.  

c. Reversal Design

i. Researcher observes problem behaviors both with and without treatment,  but takes the treatment away for a while (the reversal period) to see  

whether the problem behavior returns.  

ii. If the treatment was really working, the behaviors should worsen again  when the treatment is discontinued.  

Ch. 14 – Replicability, Generalization, and the Real World

A. General

a. Reproducibility:

i. Can researchers get the same results, using the same data

ii. Needs:

1. Exact record of statistical procedure

2. Access to the same data

b. Is it Replicable?

i. Have the results been repeated, not do they have they potential to be  

repeated. Gives the study credibility and it is crucial in the scientific  


ii. Direct Replication:


1. Researchers repeat an original study as closely as they can to see if  the original effects to see if the findings are replicable

2. Needs:

a. Adequate descriptions of Experimental procedures

b. Same measures

c. Record of statistical procedures

d. New data

iii. Conceptual Replication

1. Researchers study the same research question but use different  


2. The variables in the study are the same, but the procedures for  

oprerationalizating the variables are different

3. Needs:

a. New ways to define constructs

b. New data

iv. Replication-Plus-Extension

1. Researchers replicate their original study but add variables to test  

additional questions.  

2. Might introduce a new situational variable

a. Ex: If degree of practice affected one’s ability to drive and  

talk on the phone, IF a prior experiment had been  

conducted to see how talking on the phone affected one’s  

driving ability

v. Replication by Independent Researchers

1. Many psychological scientists give extra weight to replication  

studies that are conducted by independent researchers working  

outside the lab

B. Cohort-Designs

a. General:

i. Often prospective studies (looks for disease onset and risk factors) 

ii. Quasi-Experimental

iii. Steps:

1. Researchers first raise a research question, forming a hypothesis  

about the potential causes of a disease

2. The researchers then observe a group of people, the cohort, over a  period of time (often several years), collecting data that may be  

relevant to the disease.  

a. This allows the researchers to detect any changes in health  

in relation to the potential risk factors they have identified.

b. Used by epidemiologists looking into the factors that affect  

the health and illness of populations.

iv. Led to the identification of risk factors for CVD3 


C. Nested/Hierarchical Design

a. General


i. A class of experimental design in which every level of a given factor  appears with only a single level of any other factor 

ii. Real world interactions 

b. Challenge to Analyze

i. Observations are not independent

ii. Special analysis techniques

iii. Ex:  

1. Individuals within a classroom within public vs. private schools in  

each of the fifty states

2. Family can be broken into income, housing condition, number of  

family members etc.

3. Neighborhood can be broken down into safety, resources, grocery,  park etc.

D. Meta-Analysis

a. Definition:

i. A way of averaging the results of all the studies that have tested the same  variables to see what conclusions the whole body of work supports

1. Scientific Literature:

a. Consists of series of related studies by various researchers

ii. A set of statistical methods for quantitatively aggregating the results of several primary studies to arrive at an overall conclusion about either the  relationship between two variables or the effectiveness of an intervention  or treatment

1. Looks at overall effect size of multiple experiments

b. Myth of the Perfect Study:

i. There are no perfect studies

1. All studies contain measurement error

2. No study has measured the perfect construct validity

3. Sampling Error

ii. No single study or small selected subgroup of studies can provide an  optimal basis for scientific conclusions

c. Scientific Need:

i. Studies often report conflicting information

ii. Individual study results interpreted based on statistical significance 1. Type II Error – (False negative) underpowered studies will not find  statistical significance  

2. Smaller sample sizes will result in smaller effects which will result  in lower statistical validity

3. Strength of Relationship:

a. Cohen’s D

i. Weak = .20

ii. Medium = .50

iii. Strong = .80

b. R

i. Weak = .10

ii. Medium = .30


iii. Strong = .50

d. Strengths and Limitations

i. Strengths:

1. Peer reviewed more scrupulously as each article it contains was  

first published in an academic journal

2. Aggregate large volumes of literature

3. Control for sampling error and measurement error

4. Focuses on magnitude of the effects

5. Resolves conflicts in literature

6. Test moderators

7. More standardized and objective method

8. Generate population parameters

9. When one combines multiple studies, it increase the sample size  

and allows researchers to identify small effects/trends with 

statistical significance 

ii. Weaknesses:

1. File Drawer Problem: 

a. There is a publication bias as significant relationships are  

more likely to be published than null effects

b. Meta-Analyses might be overestimating the true size of an  

effect because null effects or even opposite effects have not  

been included in the collection processes

c. Can address partially by open publication of null  

results/failure to replicate the studies

d. Can request file-drawer data from colleagues  

2. Too many judgment calls/subjective decisions

3. Might be comparing apples and oranges

a. Can address this by including differences in moderators

4. Garbage In-Garbage Out

a. Can address by weighting studies based on quality and  


iii. Good Judgment is Essential

1. Judgment is needed at all steps of the research process:

a. Conception

b. Planning:

i. Think about expected effect sizes and design studies 

with enough power that they can reasonably find 

such effects 

ii. Minimize measurement error and maximize sample 


c. Execution

d. Analysis  

e. Interpretation

i. Interpretation should be based on strength (validity,  

reliability, scientific rigor)

E. Combining Studies


a. Assigning Value

i. Not all studies have equal value

1. Some have stronger methods, some have larger samples

2. Quantify the quality of each study to determine the weight given to  each study

ii. Weight studies based on quality

iii. Compare multiple studies based on effect size 

iv. Code other relevant moderators or design factors

b. Conducting Meta-Analyses

i. Identify Research Area/Question

1. Ex: “The ordinal effects of ostracism”

ii. Figure Out Inclusion/Exclusion Criteria

1. Ex: “We only considered cyberball experiments”

iii. Locate Studies

1. Ex: “We used seven search strategies from 2012 to 2013”

iv. For each study, compute focal effect size. Record any other variables  (potential moderators) 

1. Ex: “We used hedges “g” version”

v. Compile Database

vi. Complete Analysis

1. Results

F. External Validity

a. Generalizing to other Participants

i. Must have a probabilistic sample of the population

ii. Understand that the population of interest isn’t generally the entire  population

iii. “How” matters more than “how many”

b. Generalizing to other Settings

i. Ecological Validity:

1. “Mundane Realism” a study’s similarity to real world contexts

ii. The importance of external validity depends on a researchers priorities iii. Cultural Psychology:

1. Sub-discipline of psychology that focuses on how cultural contexts  shape the way a person thinks, feels, and behaves

2. Muller-Leyer Illusion

c. Real World

i. Field Setting

1. When a study takes place in the real world

ii. Experimental Realism

1. Many laboratory experiments create settings in which people  

experience authentic emotions, motivations, and behaviors

G. Scientific Mode:

a. Theory Testing Mode

i. Researchers usually are testing the association or causal claims to  

investigate support for a theory


ii. Theory-Data Cycle is the process of designing studies to test a theory and  using the data from studies to reject, refine, or support the theory. In this  mode, external validity matters less than internal

b. Generalization Mode

i. Researchers want to generalize the findings from the sample in their study  to a larger population

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