Introduction to Psychology Research Methods

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What this Is and What this Isn't

What this is: These notes were made by a student with a bad memory, in order to attempt to retain highlights of the lectures.

What this isn't: They are not a substitute for reading the text and taking a class.

Is Psychology a Science?

Psychology is the science of mind and behavior when it is studied using the scientific method.

When one studies something by using the scientific method of observation and test, then one is doing science.

Science does not provide "The Truth", which is a matter of faith not science.

However, scientific research allows us to say: "At this point, the best evidence shows that XXX is what is most likely to be happening."

The goals of Psychology scientific research are to discover:

  1. What we do - the description.
  2. Why we do it - the explanation, including testable theories.

Lesson 1: How do you know what you know?

  1. Psychology research is how psychologists learn

  2. How do you know what you know?

  3. If someone makes a claim about how the world operates, or what they can do, you can tell them "Show me!".

  4. 1624: Francis Bacon made his prescription for knowing:

  5. 1660s: Isaac Newton developed the scientific method:

    1. Observe events carefully using procedures that anyone else could copy and use.
    2. Record your observations.
    3. Explain your observations. i.e. Develop your theory.
    4. Test your observation by predicting what is going to happen next.
    5. Repeat the above sequence of steps, revising your theory as you acquire more data.

    Newton's Rules of Reasoning:

    1. Law of Parsimony: Admit only as many causes of natural things as are (a) true and (b) sufficient to explain their appearances.
    2. To the same natural effects assign the same causes.
    3. Extrapolation from experiments may be performed.
    4. "Propositions inferred by general induction from phenomena" (i.e., theories obtained from experiments) can be considered roughly true until other phenomena (note: not theories) occur.

Exercise: Pick any statement that you argue with someone about. Self-check on why you hold your opinion:

  1. You believe some expert.
  2. Common sense, based on recollections of personal experience.
  3. Common knowledge.
  4. The scientific method.

Lesson 2: Boundaries of science.

  1. Science is a set of beliefs about knowledge of the natural universe, including:

  2. The main goals of science:

  3. Science:

  4. Scientist study measurable events, directly or indirectly: "If you want to know about something, look at it." In the scientific or empirical method, your conclusions and beliefs are based on observation.

  5. Determinism: Everything is caused by something. Every scientific theory assumes that events are determined by identifiable factors.

  6. A theory is based on evidence (not speculation) collected in ways that safeguard against bias and error. It is a set of ideas: A good theory is:
    1. accurate (seems to be working; has predictive validity).
    2. parsimonious (explain the phenomenon with fewer possible concepts and assumptions than the others).
    3. fruitful (generative; stirs up new ideas).
    4. extensive (explains many different events).
    5. internally consistent.
    6. useful.
    7. unlikely to be wrong (even though it can never be proved true).

  7. Data are events that can be measured. The observed events are facts that do not change with time. Their interpretation and the theory, however, may change.

  8. Basic science emphasizes understanding the relationship among variables, develops ideas that help us see how these variables are related, and how the universe operates. Basic psychological research is designed to increase knowledge and understanding about behavior.

  9. Applied science emphasizes finding solutions to everyday problems. Applied psychological research is designed to increase our understanding of and solutions to specific and common problems:

  10. The two approaches interact. Kurt Lewin: "there is nothing as practical as a good theory."

  11. Induction: data leads to a theory; collect data by observing events; then create a theory to explain the data.
    Move from a part to a whole. Data from a particular study are used to develop a general theory.
    Advantage: you go where the data leads you and avoid having preconceptions.
    Disadvantage: the resulting theory is limited to the current set of observations; you don't get a general principle.

  12. Deduction: theory data leads to data; construct a theory to predict events; then observe the data.
    Advantage: you can develop general principles that apply to many sets of data.
    Advantage: having a theory and doing deductions helps us avoid fishing expeditions.
    Disadvantage: one can become too strongly attached to a theory.

  13. Experiments: some terms:
    Dependent variable
    Independent variable(s).

    Exercise: Pick any statement that you make or hear. Self-check on whether it is:

    1. A fact: testable. Often the statement includes numbers or greater-less-than comparisons. Can be confirmed or disconfirmed by careful observation.
    2. An opinion: value-laden; expresses how someone feels; right and wrong.

Lesson 3: Testable hypotheses.

  1. Science is a self-correcting way of looking at the world.
  2. Karl Popper said scientists should emphasize falsifiability. To improve scientific knowledge:

  3. Pseudo science: A set of beliefs and evidence that masquerade as science, while violating at least one rule of the the scientific method.
    e.g., ESP; its "shyness" says ESP is always there even if you can't see it; that's a universal; so it can't be measured scientifically; can't be falsified.
    e.g., Creationism: claim that God created fossils and geological forms to make Earth appear old. This is not testable. Supernatural powers are not testable. They do not belong in biological science.

  4. A theory (set of beliefs) never becomes a fact (event).

  5. Testable hypothesis: a statement about the relationship of two (or more) measurable variables.
    IV (Independent Variable): Manipulated by the experimenter; causes things to happen.
    DV (Dependent Variable): Alleged effect; the response that the experimenter measures.
    e.g. Hypothesis: The consumption of alcohol reduced tension.
    You must create operational definitions of your concepts in terms of what you will measure.
    Here, IV could be vodka consumed (0 or 2 oz) or beer consumed (pints or 6-packs).
    DV could be measured blood pressure, galvanic skin response (G.S.R., a quick and easy way of measuring psychological arousal), etc.

  6. Exercise: How would you code emotions, e.g. of people in a parade? Posture? Gestures? Vocalization? Multidimensional.

Lesson 4: Thinking errors; validity.

  1. Validity ~ Truthfulness.

  2. Intervening variables:

  3. Three features of a good theory, after Diana Kuhn (2002):

  4. Why we make mistakes on seeing the evidence:

  5. "Necessary" does not equal "sufficient", to prove that a belief is correct:

Lesson 5: Observations.

Types of observations:

  1. Narrative Recording.

  2. Frequency Recording.

  3. Duration Recording.

  4. Time Sampling.

Observable Definitions of Behavior:

  1. We want to see what people will do, and to find connections. We need to avoid problems with observations, particularly:

  2. Therefore we establish an Objective Definition of Behavior that:

  3. Advantages of an Objective Definition include:

Lesson 6: Problems with observations; indirect observation.

  1. Observe an effect (only an estimate) of a behavior. Later could collect field data for physical traces that might supplement the observations, e.g. or archival data:

  2. Reactivity. Changes occur in the subjects behavior when the subjects know they are being observed. To avoid this:

  3. Researcher Bias.

  4. Naturalistic observations.

Lesson 7: Experiments.

  1. Experiment. "The manipulation of some environment in order to produce a specific comparison while other aspects of the situation are held constant." [DD]

  2. Experimental Method. "A method based on strict control in experimentation, for making valid inferences concerning the relationships between one variable and another." [Ray].

  3. Participants (preferred term) = subjects [old term and the generic term] = people who participate in experiments.

  4. Design.

  5. Design. You must have control:

    1. Manipulate one variable at a time.
    2. Hold the testing situation constant.


    1. one thing (the IV) is varied.
    2. All other variable that might influence behavior are held constant. These are the control variables.

  6. How not to do experiments - examples:

  7. Possible control variables are the individual pre-existing differences of subjects.

    1. Experimental setting should be consistent and controlled. Same light, noise, types of people present.

    2. Experimental procedure should be consistent and controlled, with the same number of steps taking the same amount of time to both groups. If some experiment can't be run with a required fixed variable (e.g., at same time of day), and you suspect that variable may affect the responses, add that variable to you design and check for it.

    3. Individual pre-existing differences of subjects.
      • Rats can be bred true by selective breeding. Can't do this with humans.
      • Compensate by random assignment: each subject has an equal chance of being assigned to any group. e.g. flip a coin or use a random number table to assign to any group. Behind this lurk the statistics to analyze the result. How big is random enough? In practice, 20-plus. If they are as small as 15 or 16, statisticians become skeptical that there is enough randomness. OK with 17 or 18 in each cell.

        Note that a population may be heterogeneous (as at Cabrillo) or bimodal (as at Harvard where they may be either smart or rich).

      • Random assignment increases the internal validity. If we have equivalent groups then we are more likely to have a valid procedure. You must have internal validity first. Then you consider external validity.

      • Random sampling (by contrast) can increase external validity. Taking a random sample from the general population will generalize.

      • We do not usually do random sampling. Instead we get an accumulation of evidence or else you discover that cross-cultural effects are substantial.

      • Subject matching can be used if you can't get enough subjects (e.g. people with Tourett's). Important subject characteristics are matched across the different groups. It is not entirely random.

        If I think there might be a characteristic that could contaminate my results, I will match. e.g. 2 people with IQ of 100; randomly assign one of each matching pair to each of the two conditions. If someone can't be matched, they can't be used.

        e.g. excluding from a taste test people with objection to cola has no effect on internal validity but it does affect external validity.

  8. Effect of increasing amount of control: internal validity rises, giving you a greater chance to see cause and effect.

    But the more artificial the situation is, the less the external validity.

    Therefore you need to run more than one experiment. Replication in different situations can fill in the gaps.

    You must have a control group or control condition.

  9. Portacaval Shunt Studies made in 1940s were reviewed 20 years later. Although more studies were "enthusiastic" about the Shunt, those tended to be the uncontrolled studies:

    Uncontrolled. 247132
    Poorly controlled. 103215
    Well controlled. 0134

    Neglecting to include a control condition is a basic flaw in the design. e.g. 1980 study claimed that having kids play with dolls that are anatomically correct is a reliable way of diagnosing sexual abuse. In fact, they neglected to test a control group. If they had, they would have found that the behavior of non-abused kids (a control group) was the same as the test group.

  10. Assume the null hypothesis: "Hypothesis that the differences between two or more population parameters are zero. Used non-technically to refer to the condition that no difference exists between groups in an experiment." [Ray.]

    In statistics, stick with the null hypothesis until the results show it is extremely unlikely that nothing happened.

    Probability of getting results by chance alone should be <5% (i.e., p<0.05). If it's extremely unlikely there is no relationship, then reject the null hypothesis. With inferential statistics you are always working backwards.

    Similarly, assume that a person before a jury had the bad luck to be accused. Be extremely skeptical of claims of guilt: "We are all supposed to be here because the person is here because of an innocent mistake." [DD].

  11. Statistics allow you to test the significance of a difference, to test how likely it is to occur by chance. When it's not 100%, you need to calculate probability. e.g.:

    This is what we measure. 70%20%50%
    These are the perfect data known only to God 66%25%41%

    Anything is possible but few things are likely. Test the null hypothesis: that the independent variable had no effect on the dependent variable. How likely is it that we could get this effect if the IV has no effect on the DV. If there is less than 5% chance (by convention) that this could happen by random, we are certain enough.

    e.g., Statistician Fisher (inventor of analysis of variance) gives example of testing if a lady can tell if milk or tea is poured first into cup; make 3 cups of each; arrange randomly. As there are 20 different combinations in which the cups could be presented (6+5+4+3+2), there is a 1-in-20 chance that random guesses match the presented cups. If she is right, simply repeat again and if she is also correct, your results are very significant.

  12. Chi-square test.

Lesson 8: Writing reports.

  1. Use APA format.

Lesson 9: Threats to validity.

  1. Treatment variance is the amount of change caused by our treatment. Error variance is the noise. Try to get Treatment variance >> Error variance.

  2. Reminder: Internal validity shows the relationship of IV and DV. Construct validity means that we are observing what we intend to observe.

  3. Obscuring factors prevent you from seeing a relationship between independent variable and dependent variable. With too much noise in the data, you err by missing a relationship (type II error). Contrast with confounding factors.
    1. Variability through individual differences. N too small. Solution: use a larger sample and/or counterbalance; or change to use matched-group design; or change to use within-subjects design.
    2. DV is not sensitive enough; task is too hard or too easy; results crowd in a floor or ceiling effect.
    3. Ineffective manipulation: range of values of IV is weak or too small.
    4. Variability through the situation, such as when there are distractions or when the behavior requires a large effort.
    5. Measurement error, such as by self expression. Includes unreliability of self reports.

  4. Confounding factors [also confound (or confounding) variables]: an irrelevant variable that varies with the IV. It was not introduced by the experimenter but it can bias the research.
    when a change occurs in the DV but there is also a change in some other factor besides the IV, which might be interfering with the DV. If any confounding factor is not taken into account, you can err by claiming a relationship that does not exist (type I error). Contrast with obscuring factors.

    Confound or cause of problem Solution
    Lack of group equivalence. Random assignment of subjects to groups.
    Changes within procedures, such as wear and tear on equipment or tiring of experimenters. Standardize procedures. Use computers as much as possible.
    Experimenter effects or bias. Double blind.
    Demand characteristics: features of the procedure give the subjects a hint of the expected behavior. Remove such cues.
    Attrition: loss of participants during the course of the study can bias the samples. Make efforts to avoid attrition. Document what attrition occurs.
    Maturation: effects of growing up (for children) and aging are more significant in longer-term studies. Control group.

Lesson 10: External validity.

  1. Threats to external validity.

    1. Population. If the group is homogeneous (e.g. middle-class, white, 19-20), it may not generalize. Volunteers may behave differently from people that are paid.
    2. Experimental setting: mundane realism (to what extent do the events in the lab look like events in everyday life) and experiential realism (do the participants have a real experience, take it seriously, and get involved).
    3. Operational definition. Stay within the limits of your definitions when drawing conclusions.

  2. The most common mistakes when interpreting results:
    1. Misuse of statistics. e.g. the wrong test for the design or for the data.
    2. Ignoring lack of group equivalence.
    3. Overlooking confounding variables, where things were not controlled for.
    4. Believing correlation shows causation.
    5. Generalizing beyond the bounds of the experiment.
    6. Ignoring alternative explanations. Parsimony many help here. Or be aware if there are two or more equally simple and good explanations.
    7. Assuming that statistical significance is the same as practical significance.

Lesson 11: Within-subjects designs.

  1. Within-subjects design: Experimental design such that each participant serves in every group and receives every level (two or more levels) of the independent variable. For each different treatment, each individual's performance is measured through the dependent variable.
  2. Advantages: each participant serves every group so the groups are all the same at the start of the experiment; fewer participants are needed than for between-subjects design; greater sensitivity to changes in treatment effects.
  3. Problems can arise due to order effects, fatigue effects (see as a confounding decrease in performance), and practice effects (see as a confounding increase in performance). A poor choice if any experimental condition has a long-term effect on a subject.
    Use reversal design to check for carry over: Condition A -> Condition B -> Condition A. Does DV reverse so that its value is the same from each Condition A?
  4. To minimize the confounding effects of such problems, use intragroup counterbalancing:

Lesson 12: Factorial Designs

  1. Factorial design: Two or more IVs are used in such a way that all possible combinations of the IVs are included in the experiment.

  2. A 2*2 experiment has 2*2 = 4 combinations.
    A 4*4 experiment (which has 2 factors each of 4 levels) has 4*4 = 16 combinations or conditions.

  3. Benefits:
    1. Better approximation to the real world.
    2. Can resolve conflicts.
    3. Most theories today predict interactions, which a factorial design can investigate.
    4. Testing two variables in a factorial experiment can be better than testing each variable in separate experiments because you have more control over setting and experimenter variables.

  4. Between groups: assign randomly to each combination.
    E.g., Godden and Baddeley (1975) tested learning and remembering in distinctive situations (wearing frog suits).

  5. Within subjects either:
    1. Counter balance: half of the subjects chosen randomly experience condition A then condition B; the other half experience B then A.
    2. Or randomize: the trials are quick and participants can flip quickly among conditions, e.g. mental rotation.

  6. Mixed factorial design: Reduces variability caused by individual differences. Includes within-subjects and between-subjects components.

  7. Main effect:. The influence of one independent variable on the dependent variable. In a graph, the distance between lines indicates the strength of the main effect of one IV. The slope of the lines indicates the main effect of the other IV.

  8. Interaction effect:. The effect of IV1 on the DV depends on the effect of IV2. In a graph, the difference of slopes indicates the interaction.

Lesson 13: Surveys and Sampling Procedures

  1. Difference between survey researcher and experimental researcher:
    1. Survey researcher bases general conclusions on one study.
    2. Experimental researcher bases general conclusions on many different studies.

  2. Surveys:
    1. Are best where:
      (a) direct observation is difficult;
      (b) you actually do want opinion rather than behavior;
      (c) for exploratory research to see if variables are related;
      (d) intervention would be unethical.
      (e) you need correlation rather than causality data.
    2. Need care on how the subjects are selected, how they are asked, and what they are asked. It's most important to get a representative sample of the population.
      Use Random selection to get a representative sample from a complete population and avoid bias.
      Cluster sampling. e.g. from a list of all counties in the USA, randomly select a sample of 60 counties; this is the first cluster. For those counties, get a list of all the high schools that have home rooms for seniors; from that list randomly select 400; this is the second cluster. Then give the survey to all students in the randomly selected homerooms.
    3. Need caution on interpretation because what people say they do can be different from what they actually do.
    4. Need further caution because if the participants think too much they have been found to make worse estimates than if they respond quickly.

  3. Adjust the survey sample size depending on the homogeneity of the population. Nationwide, for a representative sample with confidence interval +/-3%, 1500 responses can be sufficient.

    Ray (p.331) says that the size depends on how many people are available to be in the survey and how homogenous are these people. He gives this formula:

    Sample size =
    SQUARE OF (Confidence level * Variation in population / Desired precision )

  4. Make stratified random samples where appropriate. Define your strata by subgroups, e.g. gender or race. Within each group get a random sample.

  5. Social desirability bias: tendency of someone being surveyed to say whatever makes them look good.

  6. Non-response bias: distortion of the results that occur because many randomly selected people did not do the survey. They are replaced by others and yet the sample is now no longer random.

  7. Face-to-face bias: response can depend on reactions to the characteristics of the person collecting the data or the perceived social stigma of the truthful response.

  8. Typical non-response rates:
    85% for phone surveys (cheap, fast, unreliable).
    70% for survey by mail.
    20% face-to-face.

  9. Steps in questionnaire constructions:
    1. Decide what information you need.
    2. Choose the type of questionnaire to use (phone, pencil and paper, etc).
    3. Draft the questionnaire with easy, non-threatening questions first. If there are sensitive questions save them for the end.
    4. Decide which questions are closed (true/false and multiple-choice are like this) and which are open-ended (fill-in-the-blank and essays are like this).
    5. Quantify where possible. e.g. ask "How often do you do X?" rather than "Do you do X?"
    6. For an valid survey, avoid loaded questions.
    7. Pilot test the first draft.
    8. Revise it.
    9. Give it to a few people in the population, to discover ambiguities, omissions, etc.

  10. Sampling procedures:

    Non-probability Convenience People available, e.g. on street.
    Quota Sample a number of individuals of each desired type.
    Probability Cluster Select randomly population units; enlist inhabitants.
    Multi-stage Randomly sample in different levels, e.g. sample to get a set of colleges, then at each of those, sample students.
    Simple random Use a random number table. Every member has an equal chance of selection.
    Stratified random Divide by feature (e.g. gender) and then select randomly.
    Systematic Choose every Nth person from a randomized list.

Lesson 14: Bad Statistics

  1. Two common mistakes:
    1. Ignoring regression toward the mean. But extreme values of any variable tend to be followed by values that are less extreme.
    2. Ignoring base rates. e.g. more people are killed at traffic lights if they walk when the light is green. Also more fluctuations in a smaller than a larger hospital, over 60% of births are boys

  2. Bad statistics are reported by the press:
    1. Research suggests that 200K people exhibit stalker's traits. This mutated to 200K celebrities are being stalked.
    2. Research suggests that 150K women have anorexia and this can result in death. This mutated to 150K deaths per year. But CDC shows 70 deaths/year and only 8.5K/year for ALL causes in females 15-24 years.

  3. Always ask about statistics in the press:
    1. Who created the statistic? Is the source neutral or biased?
    2. Why was the statistic created? To motivate people to action? To persuade the citizens that the problem is under control?
    3. Was the phenomenon measured properly? Do you get a clear definition of what was measured? How loose are the definitions? e.g. prostrate cancer 5-year survival doubled, but there is a 10% increase in deaths. Discrepancy due to earlier detection.
    4. Compared to what? What is the average? the trend?
    5. Beware of large numbers.

  4. How bad statistics are defended:
    1. Attack the motive of the person.
    2. Talk about the Dark Figure, which is the unknown number of unreported cases. Avoid speculation on this unmeasureable and (by definition) unknown quantity.

Lesson 15: Ethics

  1. Risk. You want a strong enough manipulation to find something but not strong enough to do harm.

    Minimal risks: the risks encountered in this procedure are no more than are encountered in everyday life.

  2. Informed consent. Must be given information before the research begins. Participants have the right to refuse. They must always have an alternative. Aftercare: make sure they are O.K. before they leave the lab.

  3. Debriefing. Subjects must be told the purpose of the research after it's concluded and deceptive procedures should be explained. Deception must be revealed gently.

  4. Control groups: if treatment is clearly beneficial, it may not be ethical to consign participants to a control group.

  5. Animals: Person in charge must have experience working with animals. PI is responsible.

  6. Fraud: Misleading colleagues.

Lesson 16: Small-n Designs


  1. Small-n often called "single subject" design. Each subject is a separate experiment.
  2. Reliability is established by replication rather than a significance test.
  3. Used by practitioners: to see if treatment makes a difference; when subjects are difficult to get; and to avoid ethical problem of withholding.
  4. Usually features very careful controlled procedures and many measurements of DV.
  5. Always begin with a baseline measure of behavior, which should be reliable and stable.
  6. Advantage: Results are very useful for seeing the effects on one person studied. Also: very flexible.
  7. Best predictor of future behavior is past behavior. Can predict what they can do.
  8. Disadvantage: Does not generalize; cannot be added to other study.
  9. Do not do "AB" design (baseline plus intervention only), but reversal "ABA" design, where the intervention is applied and later withdrawn.
  10. Or do multiple baseline design: good for ethical concerns when you do not want to remove Tx.


  1. Subjects are grouped. Data are presented in group averages. "This is how most people behave."
  2. Reliability among conditions is established by determining if the means are significantly different, testing statistical significance.
  3. Used by practitioners to see if treatment makes a difference, when subjects are difficult to get, and to avoid ethical problem of withholding.

Other forms of replication:

  1. Systematic replication does show generalizability and reliability. e.g. Pavlov's conditioning and Darley and Latane's helping behavior experiments.
  2. Conceptual replication uses different procedures to test the same hypothesis. e.g. (1) Men on a scary bridge follow-up by being more likely to phone an attractive confederate; (2) Men on exercise bikes might rate erotic films as more exciting.

Lesson 16 (part 2): Types of Faulty Argument

  1. Appeal to ignorance: e.g., Anything that has not been proved cannot be proved; or anything that has not been proved false much be true.
  2. Begging the question. Making a statement that has not been proved, & using that as if it were established.
  3. Ad hominem attack.
  4. Argument from authority. "The argument is good because the person making the claim is good."
  5. Justify with aversive consequences. "This claim must be true; if it isn't true then all kinds of evils will happen." e.g., "This person must be convicted because otherwise many more crimes of this sort will take place."
  6. Non sequitur: present two unrelated ideas as if they were related. "Our country will win the war because God is great."
  7. Post hoc ergo propter hoc: After that, therefore because of that.
  8. False dichotomy. Argument includes only two extremes and leaves out the many in-between shades.
  9. Straw man. Present an absurd argument, attributed to the opposition and knock it down. "Psychologists claim that if we banned most TV violence there would be no more violence." [In fact, they claim there would be a small but measurable reduction.]

Lesson 17: Quasi-experiments

Quasi research:

  1. Don't control variables as much as true experiments. Therefore don't know cause and effect.
  2. Useful where manipulation of the IV would be unethical or impossible.
  3. Usually avoids random assignment of participants to different situations.
  4. If there is a control group is might not be equivalent to the treatment group.

  5. One-group pretest-posttest, O-X-0. O=observation. X=treatment. Fancy name for "before and after".

    Note that this is NOT a quasi experiment. Fatally flawed because of:

  6. Non-equivalent control group design:
    O-X-O and

  7. Simple interrupted time-series design: O-O-O-X-O-O-O.

  8. Time-series with non-equivalent control group design: O-O-O-X-O-O-O and O-O-O---O-O-O.
    "X" might indicate a law (like a speed limit) going into effect in one state. The other group (control group) could be people of an adjacent state without the law change but with similar economics, weather, etc.

  9. Subject variables: = pre-existing characteristics of the participants (sex, age, introversion, ...):

Glossary: Terms and Jargon

actor-observer effect (n.)
The tendency of a person to attribute her own behavior to external reasons but that of others to internal causes.

alpha level (n.)
The probability of making a Type I error. Contrast with beta level and Type II error.

aggression (n.)
Behavior intended to harm or injure a person or object.

ANOVA (n.)
Analysis of variance, a technique of inferential statistics. Use ANOVA to compare two (or more) groups in order to make a decision on whether the independent variable is influenced by the dependent variable(s).

archival research (n.)
Research using previously collected records that were gathered for another purpose than the purpose of the present study. However:

basic research (n.)
Improves our understanding of behavior.

beta level (n.)
The probability of making a Type II error. Contrast with alpha level and Type I error.

between-subjects design (n.)
An experimental design to establish comparisons between one group and another. Participants of one group receive a different level of the independent variable or treatment than do participants in another group.

bias (n.)
Prejudice in the design, performance, analysis, or presentation of a research project.
Systematic errors can result from researchers' expectations. See also "confirmation bias"
Minimize by (a) automatic recording equipment and (b) more than one observer.
See also Researcher Bias.

central limits theorem (n.)
If a number of samples are drawn from a population at random, then the means of the samples tend to be normally distributed.

classical conditioning (n.)
Learning through association: a neutral stimulus (conditioned stimulus) is paired with a stimulus (unconditioned stimulus) that produces an emotional response.

confederate (n.)
An accomplice of an experimenter; a participant in a research experiment assumes that each confederate is another participant or a bystander.

conformity (n.)
Acquiescence to perceived group pressure.

confound (n.)
A factor that systematically biases the research but was not purposely introduced by the experimenter. Techniques to minimize their influence can be built in, and include:
  1. Counterbalancing.
  2. Elimination of the confound, e.g. study a single gender.
  3. Equate the numbers of participants with different values of the confound, e.g. an equal number of men and women in each group controls for possible gender differences.
  4. Match.
  5. Randomize the selection through use of a random number table.

When all else fails, repeat the experiment under different circumstances that eliminate or alter suspected confounds.

See more at confounding factor and confound variable.

construct (n.)

control group (n.)
Experimental participants that so not receive the experimental treatment (manipulation of the independent variable).

covariation principle (n.)
For something to be the cause of a particular behavior, it must be present when the behavior occurs and absent when it does not. [A principle of attribution theory.]

convenience sample (n.)
Researchers often generalize from a sample that is convenient rather than truly random. Especially true of behavioral research.

correlation coefficient (n.)
From -1.00 to +1.00; a statistical indicator of the direction and strength of the relationship between two variables.

correlation studies (n.)
Research designed to examine the nature of the relationship between two or more naturally occurring variables.

counter balancing (n.)
Every possible sequence occurs in each presentation of the treatment. All participants receive the tasks in the same counter-balanced order. Each condition appears equally often, and it precedes and follows the other conditions an equal number of times.

debriefing (n.)
A procedure at the conclusion of a research session, to give participants complete information about the purpose of the study.

deception (n.)
A research technique where participants in a study are given false information.

deduction (n.)
Use a well-established theory (or general principles) to predict observations.

dependent variable (n.)
The variable whose value depends upon the value of the independent variable, which is manipulated by the experimenter. An experimental variable (a variable that is measured in an experiment) because it is believed to depend on the manipulated changes in the independent variable(s).

demand characteristics (n.)
The overall effects of the situation on the behavior of a participant. This can bias the result such as by preventing adequate influence of the independent variable on the dependent variable.

experimental methods (n.)
Research that is designed to test cause-and-effect relationships between variables.

experimental realism (n.)
The extent to which an experiment involves the participants, feels to them like 'real life', and lets them forget the observers inherent in the experiment.

experimenter effects (n.)
Unintended effects caused by an experimenter on the behavior of a participant.

external attribution (n.)
Explaining the cause of an event as due to factors (such as luck, other people, or the situation) external to the subject.

external validity (n.)
The degree to which a study's findings can be generalized to subjects other than those in the study.

factorial design (n.)
A design with more than one independent variable, where each IV is presented at every level of the other IV.

false consensus bias (n.)
The tendency to exaggerate how commonly one's own characteristics and opinions appear in the general population.

falsification (n.)
The philosophical position that the goal of science is the falsify proposed hypotheses.

foot-in-the-door technique (n.)
A two-step compliance technique where the influencer makes a small request and secures compliance, and then later follows this with a larger, less desirable request.

fundamental attribution error (n.)
The tendency to explain the behavior of others through internal attributions rather than external attributions.

Heuristic (n.)
A mental shortcut that reduces time taken to reach a decision by replacing complex judgment sequences by approximations and other simple rules of thumb.

Hypothesis (n.)
A specific proposition or expectation derived from a theory about the nature of things.

Independent Variable (n.)
An experimental variable that is manipulated by an investigator. The variable whose value is defined by the experimenter, and thus is outside of the control of the participants in the experiment. Compare with dependent variable.

induction (n.)
Observe events, then create a theory ('a statement of general principles') that explains them and helps us understand the specific events.

inferential statistics (n.)
Statistics that describe a population. Measures how often the result could occur by chance alone if the null hypothesis is true. From a given sample of scores, the statistician infers parameters related to the set of all possible scores from which that sample was drawn.
E.g., t-test.
E.g., inferential statistics variance = sum-of-squares divided by (N-1). Compare with descriptive statistics, where variance = sum-of-squares divided by (N).

internal validity (n.)
The procedure shows a clear effect of the independent variable on the dependent variable.

intervening variable (n.)
Used by researchers to relate independent variable and dependent variable.

laboratory experiment (n.)
An experiment in a controlled environment.

low-ball technique (n.)
A two-step compliance strategy: the influencer secures agreement with a request by understating its true cost.

meta-analysis (n.)
A statistical technique that combines data from several empirical studies, in order to estimate more reliably the overall size of the effect of interest. If the studies use different independent variables, the effect size for each study is required.

null hypothesis (n.)
Assumption that the population mean is equal to the sample mean.

operational definition (n.)
A definition that presents a construct in terms of observable operations that can be measured and used.

objective definition of behavior (n.)
A definition that is limited to what you see and hear; it's specific; and includes examples and non-examples.

subject variable (n.)
Pre-existing characteristics of the participants (sex, age, introversion, ...):

quasi-experiment (n.)
When the experimenters cannot control variables as much as in a true experiments, we do not know the cause and effect. This is used where manipulation of the IV would be unethical or impossible, and/or lack of control prevents definitive statements about cause and effect, and/or it is difficult to control for potential confound variables. More information is at Lesson 17: Quasi-experiments.
The experimenter attempts to isolate a causal influence by selection of situations instead of manipulation of variables. e.g., we select cases in which 'X' actually does vary, such as a person's choice of crossing a ravine on a high bridge rather than a low one (Dutton and Aron, 1974).

random assignment (n.)
Placement of research participants into experimental conditions in a manner which guarantees that all have an equal chance of being exposed to each level of the independent variable.

random selection (n.)
Each member of the population has an equal chance of being chosen for the sample.

reliability (n.)
The degree to which an experiment is consistent and reproducible. Contrast with validity.

representative sample (n.)
A subset of a population that closely matches the overall characteristics of the population with respect to the distribution of males and females, racial and ethnic groups, and so on.

self-fulfilling prophecy (n.)
An expectation that something will happen, and as a result the searching by a researcher for confirmation of expectations.

significant difference (n.)
A difference between the results for experimental groups or conditions that would have occurred by chance less than a specified criterion. In psychology, the usual criterion is a probability, p, of less than 5% or:
p < .05

statistics (n.)
Meanμ M
Standard deviationσ S

t-test (n.)
Statistical test.
t = association / (lack-of-association)
If r2 = variance-accounted-for
t = (r / squareroot(1 - r2) * squareroot(df)
df = degrees of freedom

testable hypothesis (n.)
Refers to measurable events.

that's-not-all strategy (n.)
A two-step compliance technique: the influencer makes a large request then immediately offers an attraction (particularly a discount or a free bousy) before the initial request can be refused.

theory (n.)
A statement of general principles that helps us understand specific events.

treatment group (n.)
Experimental participants exposed to nonzero levels of the independent variable.

Type I error (n.)
The error of rejecting the null hypothesis when it is TRUE. Contrast with Type II error.

Type II error (n.)
The error of rejecting the null hypothesis when it is FALSE. Contrast with Type I error.

validity (n.)
The degree to which our ideas and research are accurate, true, and capable of support. See also external validity and internal validity. Contrast with reliability.


Buy 'Psychological Research' Psychological Research: The Ideas Behind the Methods (2001)
by Douglas Mook.
Text for the college course in Psychological research methods for which these notes are made.
See also our BLOG (web log) entry.

 1. Testing Our Ideas
    Making Friends With Statistics (MFWS): A Look Ahead 
 2. Theory and Data in Psychology
    MFWS: Statistical Significance 
 3. Data
    MFWS: Frequency Distributions 
 4. Observation and Description I
    MFWS: Descriptive Statistics 
 5. Observation and Description II: Some Technical Problems
    MFWS: Scatterplots and Correlations 
 6. Experiments with One Independent Variable
    MFWS: Analysis of Variance and the t Statistic 
 7. Experimental Control I: Obscuring Factors
    MFWS: More about Significance Testing 
 8. Experimental Control II: Confound Variables
    MFWS: Statistical Control 
 9. Experiments with More than One Independent Variable
    MFWS: Factorial Analysis of Variance and More about Interactions 
10. Single-Subject and "Small-N" Experiments
    MFWS: Signal-Detection Theory 
11. Quasi Experiment.
    MFWS: The Chi-Square Test 
12. The Reliability and Generality of Findings
    MFWS: Meta-analysis 
13. Ethical Considerations in Research 
14. Research Psychology, Pop Psychology, and Intuitive Psychology
    MFWS: When is a Problem a Statistical Problem? 
Appendix A: Random Numbers and How to Use Them 
Appendix B: Statistical Tables 
Appendix C: How to Report Research 

Methods: Towards a Science of Behavior and Experience (1997)
by William J. Ray.

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