Threats to Validity (Lecture and Chap. 9) [Question]: Explain and illustrate the differences between the following 3 types of validity: construct validity, internal validity, and external validity.

­ Construct validity [87] is assessed by seeing whether a particular measure relates as it should to other measures. Researchers typically examine construct validity by calculating correlations between the measure they wish to validate and other measures.

­ Internal Validity [196] is the degree to which a researcher draws accurate conclusions about the effects of the independent variable. An experiment is internally valid when it eliminates all potential sources of confound variance. When an experiment has internal validity, a researcher can confidently conclude that observed differences were due to the independent variable.

­ External Validity [206] refers to the degree to which the results obtained in one study can be replicated or generalized to other samples, research settings, and procedures. External validity refers to the generalizability of the research results to other settings.

[Answer]: Construct validity applies to the correlations between different measures in a study and internal validity applies to when accurate conclusions are drawn about the independent variable and confounding variances are at a minimum. External validity applies to the replicability of the study and whether or not the results can be found repeatedly. [Question]: Why are non­experimental methods considered low in internal validity? Illustrate with examples. [Answer]: Nonexperimental methods are considered low in internal validity because the crux of internal validity is that most confounds are accounted for. The logic of experimentation requires that nothing can differ systematically between the experimental conditions other than the independent variable ­ which is something one can’t accomplish in non­experimental designs.

­ An example of this would be the pepsi experiment. They had bottles labeled with M for Pepsi and Q for Coke but since the letters never changed the researchers can never be sure whether people prefer Coke or the letter Q or vice versa. The problem with studies like these is that they introduce other possible alternative explanations for results, and thus threaten the internal validity.

[Question]: Threats to internal validity: explain, provide, and recognize examples of:

­ confounds, experimenter expectancy effects, demand characteristics, differential attrition, regression to the mean [already know this one]

­ How can we guard against experimenter expectancy and demand characteristics? [Answer]: Threats to internal validity:

­ Confounds: When confounding occurs there’s no way to know whether the results were due to the independent variable or to the confound. [Ex. The Q and M in the Pepsi vs. Coke experiment were confounds. There was no way of knowing if the results were due to the letter or the soda].

­ Experimenter Expectancy Effects: When the experimenter’s expectations distort the results of an experiment by affecting how they interpret participants’ behavior. [An example of this is the experiment where people were primed with old people words and the experimenters expected them to move faster/slower and the results correlated.]

­ Demand Characteristics: Participants’ assumptions about the nature of a study affecting the outcome of the research. These are aspects of a study that indicate to participants how they should behave. [Ex. Orne and Schiebe (1964): Release forms and panic buttons were presented to the participants which raised alarm that something potentially harmful was going to happen].

­ Differential Attrition: Attrition is the loss of participants during a study. When the rate of attrition differs across the experimental conditions, it is a bias known as differential attrition. [For example, participants assigned to a condition they don’t like having a higher chance of dropping out of experiments].

To eliminate demand characteristics, experimenters often conceal the purpose of the experiment from participants. In addition, they try to eliminate any cues in their own behavior or in the experimental setting that would lead participants to draw inferences about the hypotheses or about how they should act. Perhaps the most effective way to eliminate both experimenter expectancy effects and demand characteristics is to use a double­blind procedure. With a double­blind procedure, neither the participants nor the experimenters who interact with them know which experimental condition a participant is in at the time the study is conducted. To prevent experimenter expectancy effects like with the clever Hans experiment you can isolate the subject from anyone with an expectancy or anyone that knows the answer to what is going on. As with demand characteristics, having a double blind study could help prevent the expectancy effects. [Question]: External Validity: What is it, when and why is it a problem, what are potential solutions? How does it relate to internal validity? Why would an experimental psychologist not be concerned about it. [Answer]: External Validity [206] refers to the degree to which the results obtained in one study can be replicated or generalized to other samples, research settings, and procedures. External validity refers to the generalizability of the research results to other settings.

­ To some extent internal validity and external validity are inversely related; high internal validity tends to produce lower external validity, and vice versa.

­ The more tightly the experimenter controls the experimental setting, the more internally valid the results, but the lower the external validity.

­ An experimental psychologist wouldn’t be concerned about it because in experimental research the goal is seldom to obtain results that generalize to the real world. The goal of experimentation is not to make generalizations but rather to test them.

­ When faced with the dilemma, virtually all experimental psychologists opt in favor of internal validity.

Experimental Design (Lecture and Chap. 10) [Question]: Factorial designs:

­ Terminology: One way, two way, n­way, factors (IVs), levels, and conditions. ­ How to use factorial nomenclature [Ex. How many factors does a 3 x 2 x 2 design

have? Levels?] ­ Be able to identify kind of design: between ­ subjects (randomized factorial), within ­

subjects, split ­ plot (aka mixed factorial or between ­ within, I don’t care which of these you use), and expericorr (use this term rather than “mixed” for clarity).

[Answer]: All You Need To Know:

­ A 3 x 2 x 2 has three independent variables, one variable has three levels and the other two have two levels.

­ A one way design has one independent variable. A two way design has two independent variables. A three way design has three independent variables and so on.

­ Within Subjects: A repeated measures [or within subjects] factorial design requires participants to participate in every experimental condition.

­ Between Subjects: In a randomized group factorial design participants are assigned to one of the possible combinations of the independent variable.

­ Split Plot: A design that combines one or more between subject variables with one or more within subject variables.

[Question]: Main effects and interactions:

­ Define what a main effect is and what an interaction is. ­ Identify what the main effects and interactions are in a given design. ­ For a particular study, you should be able to succinctly state what question can be

addressed by a test of each main effect and interaction. [Answer]: The Good­Good:

­ A main effect is the effect of a single independent variable in a factorial design. An interaction is present when the effect of one independent variable differs across the levels of other independent variables.

­

[Question]: Subject assignment to condition:

­ Know how subjects are assigned to conditions in randomized group designs, matched­subject design, and repeated measures designs.

­ Understand when/why you should use these, understand their advantages and concerns you need to be aware of in using one or the other.

[Answer]: (8

­ A randomized groups design is a between­subjects design in which participants are randomly assigned to one of two or more conditions.

­ A matched­subjects design is one where participants are matched into blocks on the basis of a variable the researcher believes relevant to the experiment. The participants in each matched block are randomly assigned to one of the experimental or control conditions. This is used to increase the similarity of the experimental groups prior to the manipulation of the independent variable.

­ In a repeated measures or within subjects design, each participant serves in all experimental conditions.

Quasi­experiments (Lecture and chap. 13) [Question]: Quasi experimental design:

­ Define and recognise a quasi­experimental design and an expericorr design. ­ What is a quasi­independent variable? Be able to identify them in a study and provide

examples of different kinds of quasi­independent variables. ­ Explain when/why you would design a quasi­experiment. Be able to explain the

advantages/disadvantages. [Answer]:

­ A quasi­experimental design is one where the the researchers are unable or unwilling to manipulate the independent variable. If the researcher lacks control over the assignment of participants to conditions and/or does not manipulate the causal variable of interest, the design is quasi­experimental.

­ A quasi­independent variable is used to indicate that the variable is not a true independent variable that is manipulated by the researcher but rather is an event that participants experienced for other reasons.

­ Expericorr designs: Researchers sometimes design experiments to investigate the combined effects of situational factors and participant variables. These designs involve one or more independent variables that are manipulated by the experimenter, and one or more preexisting participant variables that are measured rather than manipulated.

­ Quasi­experimental designs generally do not possess the same degree of internal validity as experimental designs. Participants are not randomly assigned to conditions and the researcher may have no control over the independent variable.

­ However, a well­designed quasi­experimental design can provide strong circumstantial evidence about cause­and­effect relationships.

[Question]: Pretest­Posttest designs. Nonequivalent control group designs [what makes those groups nonequivalent]. Problems with nonequivalent group designs. [Answer]: Stuffs.

­ Pretest­Posttest Designs: an experiment in which participants’ responses are measured twice ­ once before and once after the introduction of the independent variable.

­ Nonequivalent control group designs: a quasi­experimental design in which the group of participants that receives the quasi­independent variable is compared to one or more groups of participants who do not receive the treatment.

­ Problems with nonequivalent control group designs: The weakness in the posttest only design is that we have no way of knowing whether the two groups were actually similar before the quasi­experimental group received the treatment so it is very low in internal validity. The pretest­posttest design still doesn’t eliminate all potential threat to internal validity, a local history effect may occur. Something may happen to one group that doesn’t happen to the other [pg. 274].

Sampling (Chap. 5, pp. 99 ­ 102, 109 ­ 111 textbook only, no lecture material!) [Question]: Basic differences between probability and nonprobability samples?

­ advantages and disadvantages of probability samples ­ what is the error of estimation? ­ goal of using probability sample? ­ why are probability samples not necessary (or even desired) for most psychological

research? [Answer]: Grr.

­ A probability sample is a sample that is selected in such a way that the likelihood that any particular individual in the population will be selected for the sample can be specified.

­ A nonprobability sample is a sample selected in such a way that the likelihood of any member of the population being chosen for the sample cannot be determined.

­ Probability samples are very time consuming, expensive, and difficult. In most research contexts it is impossible, impractical, or unnecessary.

­ Without probability sampling we cannot be sure of the degree to which the data provided by the sample approximate with the behavior of the larger population.

­ The Error of Estimation is the degree to which data obtained from a sample are expected to deviate from the population as a whole; also called margin of error.

­ Goal of using probability samples: When a researcher is interested in accurately describing the behavior of a particular population from a sample, probability samples are essential.

[Question]: Method for sampling:

­ Know what a simple random sample is and what a convenience sample is.

[Answer]: Just definitions. ­ A convenience sample is a nonprobability sample that includes whatever participants

are readily available. ­ A simple random sample is a sample selected in such a way that every possible

sample of the desired size has the same chance of being selected from the population. Developmental Research Methods (Chap 13, pp. 282 ­ 288) [Question]:

­ Basic question being addressed, change over time [the lifespan] ­ Why are developmental questions often addressed by quasi­experimental designs? ­ Understand what cross­sectional, longitudinal, and cross­sequential designs are? ­ Be able to identify and provide examples of each. ­ Illustration of pros and cons for each: cohort/generational effects, attrition rates, history

confounded with time, participant variable confounds. [Answer]:

­ In longitudinal designs the quasi­independent variable is time itself. Nothing has occurred between one observation and another other than the passage of time itself.

­ Longitudinal designs are used most frequently by developmental psychologists to study age related changes in how people think, feel, and behave.

­ Cross­Sectional Design: a research design in which a group of respondents is studied once. [Pros: Describes similarities and differences between people in different age groups. Easy to implement. Cons: Ignores individual differences. Generational effects or cohort effects].

­ Cross Sequential Cohort Design: a quasi­experimental design in which two or more age cohorts are measured at two or more times; in essence, it is a longitudinal design with multiple age groups that allows researchers to separate the effects of age and cohort. [Pros: Determine whether cohort effects influence your findings. Make both longitudinal and cross­sectional comparisons. More efficient than standard longitudinal design. Cons: Costly and time consuming].

­ Longitudinal Design: a study in which a single group of participants is studied over time. [Pros: Participant variable is constant. Variability is low. Provides description of change over time for each individual. Cons: Costly and Time­consuming. Threats to internal validity: High attrition, maturation/age is confounded with history. Demand characteristics. Practice effects/testeffects. Regression to the mean].

Physiological Research Methods (readings) [Question]:

­ Explain what it means that psychology and physiology have bidirectional, reciprocal interactions. Provide examples of using physiology as an IV and as a DV to study physiological states and processes.

­ Why is it difficult to use physiological indices as direct indicators of psychological constructs.

­ What are some of the strengths of psychophysiology? ­ What are some of the weaknesses of psychophysiology? ­ Describe the kind of physiological measures that can be used to study the mind.

[Answer]:

­ Bidirectional, reciprocal interactions basically mean that A affects B just as much as B affects A. The arrow, or interaction, goes both ways.

­ It’s hard to use physiological indices because it’s hard to interpret exactly what they mean or what they are correlating to. One physiological indicator doesn’t always necessarily signify a specific psychological construct.

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