Exam 2 Study Guide 290
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Apr 3, 2024
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Study guide for exam 2
This is a rough guide, highlighting the major points that will be tested. It is not intended as an outline of the exam, nor is it fully comprehensive. Material that has been covered more extensively, whether it be in lecture, the text, or on slides, will likely be tested in more depth. However, all material from the chapters is fair game for the exam unless I have specifically said it will not be tested. It is worth reviewing the featured studies in the chapters, as they provide good examples of the material in that chapter. NOTE: You will NOT be tested on this exam as to how to analyze the data from these designs.
Chapter 5: Experimental Research
- Woodworth's "Columbia Bible" covered a very large variety of topics. You do not need to know them in detail, but should know the importance of this document and a general description of what was included
- know the definition of experiment that Woodworth put forth, as we still use it today. Know what is expected/required for a study to be considered an experiment
- know what an independent variable is, as well as the difference between a manipulated IV and a non-manipulated IV in terms of the conclusions that can be drawn
- know what a dependent variable is
- you should definitely know Mills' approach to experiments, including what is meant by Agreement and Difference, and how they are used together to determine causation. The example of watching violent TV and aggression is a good one. Understanding how Mills' approach is somewhat idealized, and why real-world studies can't easily accomplish everything he states, would also be good
- Know the types of manipulated IVs (Situational, Task, Instructional) and be able to identify an example of each. - know what is meant by extraneous variables as well as confounding variables, including the specific characteristic that distinguishes confounds from all other extraneous variables
- know what ceiling and floor effects are, and why they can be a problem in studies
- know what is meant by subject variables, and how they differ from manipulated IVs, especially in terms of conclusions that can be drawn
- Review Box 5.2 (Bandura's study with Bobo Dolls and Aggression). You should know its purpose, the variables being studied, and the results. NOTE: although this is in chapter 5, as can
be seen in Figure 5.3, this also would meet the characteristics of a factorial design study.
- Know the 4 types of validity that are discussed here: Statistical Conclusion, Construct, External, Internal. - Know the various types of external validity, including other populations, other environments, and other times. Know what ecological validity is.
- Know the threats to internal validity (History, Maturation, Regression to the Mean, Testing, Instrumentation), and be able to identify examples of them. Also know what factors may increase
these threats, and why
- Know what is meant by Participant problems, including subject selection effects and attrition
Chapter 6: Methodological Control in Experimental Research
- Know the difference between a Between-subjects design and a Within-subjects design in terms of:
- how many levels of the IV each participant experiences
- the total number of participants needed
- kinds of variables which require one type of design over the other
- know why it is important to create equivalent groups, and the procedures to do so
- Random assignment. What is it, how may it be accomplished?
- Blocked Random Assignment. What does it guarantee that simple random assignment does not?
- Matching. I discussed at least 2 ways this can be done, including their strengths and weaknesses - know them. You should also know how we decide on the matching variables to use, and that we need to be able to measure them in order to use them
- know what the Muller-Lyer Illusions are, and why they are tested using a within-subjects design (as are other examples related to perception)
- Know what order effects are, as well as the specific types (Progressive, Carry-over)
- Know what counterbalancing is, what its purpose is, and the difference between Complete and Partial
- Know what a Latin Square is and its requirements
- There are multiple techniques that may be used when testing more than once per condition
- reverse counterbalancing
- block randomization
- know the difference between a cross-sectional and a longitudinal study, including strengths/weaknesses, time requirements, etc.
- know what a cohort effect is, and what a cohort sequential design is
- Definitely review the material in Box 6.1, including why that study is so unusual
- Know what is meant by bias, and the difference between experimenter and participant bias
- What are the procedures used to reduce experimenter bias?
- what is meant by manipulation check (remember, there are multiple ways this term is used)
- what is the Hawthorne Effect? Review Box 6.2
- what is the good subject effect? Evaluation apprehension?
- what are demand characteristics, how can they affect a study, and how can we determine if they
are present?
- What are the procedures used to reduce participant bias?
- have a general understanding of the ethical obligations of participants
Chapter 7: Single-factor designs
- know what this term means, why these studies formed the basis of much of the early research in
psychology, and why they are not used as often today
- Review Figure 7.1 (Decision tree) as it gives a good overview of the various types of designs
- Review Table 7.1, as it gives a good summary of the attributes of the 4 types of single-factor designs, including how equivalent groups are created
- You will be asked to identify the type of design from descriptions of research studies
- know why groups are considered to be inherently unequal in an Ex-post facto design
- know why the IV is manipulated by definition in a within-subjects design
- review the Mueller and Oppenheimer (2014) study on laptop note-taking
- Why would a study involving brain damage (or similar issues) have
to be an ex-post facto
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design?
- Review the studies by Stroop, including the methods and design, as well as the results (these studies are also covered in chapter 7)
- Review the Boothby, Clark, and Bargh (2014) study on chocolate-tasting and shared experiences
- What is a multilevel design? What are its primary advantages over simpler designs?
- know the basics of the Yerkes-Dodson Law/curve as an example of multilevel design. Also review the Plotner et al. (2015) study on Bystander Effect, the Steele et al. (1997) study on the Mozart Effect, and Ebbinghaus' forgetting curve studies as further examples
- What is the general purpose of a control group?
- What is a placebo, and why is it used?
- What is meant by a wait-list control group, and why is it used?
- the 1992 Merikle and Skanes study on subliminal messages is a good example of how placebo and wait-list controls groups can be used in the same study
- Review Box 7.3 (and the slides and lecture) on the ethics of the use of control groups
- What is a yoked control group, and why is it used?
Chapter 8: Factorial Designs
- know the two important characteristics of a factorial design
- if given a description of a factorial design, be able to identify the number of
- IVs
- levels of each IV
- total number of conditions
- know what is meant by main effect and interaction effect
- be able to identify the number of main effects and interaction effects being tested for if given a description of a study. - the 'shortcut' to determining the total number of effects (main and interaction) being tested for is 2
n
- 1, where n is the number of IVs. Example, in a 3-way factorial design, n = 3, therefore the total number of effects being tested for is 2
3
- 1, which is 8-1 = 7
- if given a table of means (or a Figure with the same information), be able to determine if there are significant main and/or interaction effects (we will be going over this on Monday)
- Main effects are found by comparing the marginal means (compare the row means to each other to determine the main effect of that IV, compare the column means to each other to determine the main effect of that IV0
- interaction effects are found by comparing the patterns in the cell means
- Box 8.1 is an example of a 2x4 repeated-measures factorial design
- Factorial designs may be all-between, all-within, or mixed (at least 1 of each type)
- know what is meant by a P x E design, and why the P is usually (not always) a between-
subjects variable
- Figure 8.6 gives a summary of the various factorial designs in a decision-tree format. It is worth
reviewing, and is an extension of the one from chapter 7
- know what an ATI design is, and what field often uses them
- know how to determine the number of participants needed for a particular kind of factorial design
- for example, if a study is a 2x3 all-between study, and there are 10 participants in the first condition, how many total participants are needed?
- how many would be needed if it were a 2x3 all-within with 10 in the first condition?
- how many would be needed if it were a 2x3 mixed, where the between-subjects variable
is the one with 2 levels? What if the between-subjects variable is the one with 3 levels?
- Review Box 8.2 for information about being a competent and ethical researcher