Psych 344 Study Guide

  1. What are the necessary characteristics of a causal relationship?
    • covariance
    • temporal precedence
    • internal validity 
    • random assignment of subjects
    • manipulation of an IV
  2. Pseudo variables supports claims of causality
    True or False?
    False: age and gender do cannot support claims of causality
  3. How can regression beta values be used to investigate causality?
    • whichever beta value is largest compared to the rest of the beta values is most likely to have a casual relationship
    • beta values can have both positive and negative values (pos/neg relationships)
  4. Types of Longitudinal Studies:
    • cohort studies
    • panel studies
    • record linkage
    • prospective studies
    • retrospective studies
  5. Cohort Study
    chart the lives of people with shared characteristics who experience the same life events at the same time
  6. Panel Studies
    focuses on a household
  7. Record Linkage Studies
    link together administrative records for the same individual over time
  8. Prospective Studies
    follow an individual's life
  9. Retrospective Studies
    Individual is asked to recall their life
  10. Types of Correlations:
    • cross-sectional correlation
    • autocorrelations
    • cross-lag correlations
  11. Cross-Sectional Correlation
    test whether to see if two variables are correlated when measured at the same point in time
  12. Autocorrelations
    determines the correlation between one variable and itself when measured at two different points in time
  13. Cross-lag Correlations
    determine whether the earlier measure of one variable is associated with the later measure of the other variable (helps establish temporal precedence)
  14. Apparent correlations are a function of:
    • random chance
    • fabrication of data
    • an actual casual relationship due to the third variable
  15. What should authors do after finding significant effects in their pilot data?
    They should not keep the pilot data and conduct a new experiment with the same design but different sample and data
  16. What should authors do if they want to publish a paper with a known confound?
    • qualify what they found
    • be honest and say there was a confound in the publication
    • full disclosure and include in the limitations that there was a confound
  17. What should authors do if they conduct multiple analyses on their data?
    • conducting multiple analyses on data the risk of making a type 1 error increases 
    • lower alpha values so that a type 1 error is less likely to occur
  18. Why are reversal designs potentially unethical?
    • some might question the ethics of withdrawing a treatment that appears to be working
    • may be unethical to use a treatment that is not empirically demonstrated to be effective
  19. Interaction Effect
    whether the effect of the original independent variable depends on the level of another independent variable
  20. Crossover Interaction
    the original IV depends on the new IV and causes a graph of the interaction to intersect

    • ex: preference for hot food or cold food?
    • hot>cold for pancakes
    • cold>hot for ice cream
  21. Spreading Interaction
    DV (how often dog sits) is higher at one level of the original IV (saying sit) combined with the new IV (holding treat) and is lower or non-existent at the other levels (saying sit/nothing and not holding a treat)
  22. What happens to a main effect when an interaction is found?
    when a study shows both a main effect and an interaction, the interaction is always more important
  23. Main Effect
    the overall effect of one independent variable on the dependent variable, averaging over the levels of the other independent variable
  24. How do researchers discuss a two-way interaction?
    • in a three-way design, there are three possible two-way interactions
    • to inspect these interactions, one must construct 2x2 tables, compute the means of each cells and investigate the difference in differences 
    • the effect of 1 variable DEPENDS on the level of the other variable  
    • look for "it depends" or "only when"
  25. How do they discuss a three-way interaction?
    in a design with three independent variables, the three-way interaction tests whether two-way interactions are the same at the levels of the third independent variable
  26. Selection Effects
    bias introduced by the selection of individuals, groups or data for analysis in such a way that proper randomization is not achieved

    ex: participants choose to be in a certain group
  27. Solutions for Selection effects:
    random assignment and matched sets (participants sorted lowest to highest and grouped into sets of two)
  28. Carryover Effects
    any lingering effects of a previous/other experimental condition affecting a current experimental condition

    ex: in a study that tests if caffeine use has an effect on test scores, an individual in the "no caffeine" group regularly used caffeine prior to study
  29. Solution for Carryover Effects
    counterbalancing: presenting IV to participants in different sequences, A to B vs B to A
  30. Observer Effects/Bias
    results of an experiment biased due to experimenters' expectations on a particular task, creating an implicit demand for the participants to perform as expected
  31. Solution for Observer Effects/Bias
    blind studies (masked design)
  32. Hypothesis
    a proposed explanation based on something that you observe as a starting point for further investigation (a prediction, something you test)
  33. Theory
    an in-depth statement describing general principles about how variables relate to one another within a scientific phenomenon
  34. Law
    a universal single statement that is accepted to be true usually involving math
  35. Characteristics which define a true experiment
    • random assignment of subjects
    • manipulation of an IV
    • correlation
    • temporal precedence
    • internal validity
  36. Systematic Variability
    • makes internal validity a problem by not controlling for a variable so it affects all participants at all levels of the study in the same way
    •     nonrandom: flaw in design
    •     ex: showing up for a study where some people got 9 hours of sleep vs. 5 hours of sleep and pairing all those with 9 hours of sleep together in one group
  37. Unsystematic Variability
    • has a random or haphazard variable which can obscure differences in the independent variable
    • individual differences: some people do great with 5 hours of sleep and other don't
    • random
    • noise
    • variable spread evenly
  38. Mediator
    • definition: reason for effect
    • X affects Y because X leads to M which leads to Y (X/Y relationship is implied because of M)
    • mediator must be a casual result of IV and a casual antecedent of DV
    • ex: exercise -> calorie intake -> weight loss
    • (exercising more increases your calorie intake which influences weight loss)
  39. Moderator
    • definition: contextualizing the effect
    • cannot be a casual result of IV
    • influences to what extent IV affects DV
    • ex: exercise -> weight loss (gender, age and previous weight are moderators because exercise does not cause them but they influence how much exercise affects weight loss)
  40. Multivariate Design
    study involves more than two variables
  41. Posttest Only
    participants are randomly assigned to the independent variable level and measured once on dependent variable after exposure to the independent variable
  42. Pretest/Posttest
    participants are assigned to the IV measured twice on DV (once before being exposed to the independent variable and once after being exposed to the independent variable)
  43. Repeated Measures Design
    participants are measured on the dependent variable more than once; after exposure to each IV condition
  44. Concurrent Measures Design
    participants are exposed to all IV levels at the same time and single attitudinal or behavioral precedence is dependent variable
  45. Pilot Study
    study using a separate group of participants completed before conducting the study: checks how well independent variables are operationalized
  46. Matched Groups Design
    • one in which the experimenters will measure all variables of the participants that could have an effect on the measured variable
    • will match like participants and split them evenly between all different groups so that variation between the groups decreased 
    • increases internal validity
  47. Factorial Design
    • crossing two IVs and studying the effects of each possible combination of IV
    • must have participant variable
  48. Stable-baseline
    • when a researcher observes behavior for an extended baseline period before beginning an intervention
    • proves that the behavior was not changing before the intervention
  49. Multiple-Baseline
    researchers stagger their introduction of an intervention across a variety of contexts, time or situations
  50. Reversal Design
    researchers observe a behavior before and during a treatment and then stop the treatment to see if the behavior issue returns/reverses
  51. Quasi-experiment
    a study similar to an experiment except that the researchers do not have full experimental control (they may not be able to randomly assign participants to independent variable conditions)
  52. Nonequivalent Control Group Design
    an independent-groups quasi-experiment that has at least one treatment group and one comparison group, but participants have not been randomly assigned to the two groups
  53. Nonequivalent Control Group Pretest/Posttest Design
    • an independent groups quasi-experiment that has at least one treatment group and one comparison group. 
    • participants have not been randomly assigned to the two groups and at least one pretest and one posttest are administered
  54. Interrupted Time-Series Design
    • a quasi-experiment in which participants are measured repeatedly on a dependent variable before, during and after the interruption caused by some event
    • ex: A factory wants to measure worker productivity. They measure productivity every week, halfway through the experiment they shorten shifts from 10 hrs to 8 hrs. Following the intervention productivity improves. Pretest was for the 10hrs, week of reduction was during “interruption”, measurements after that are post-interruption.
  55. Nonequivalent Control Group Interrupted Time-Series Design
    a quasi-experiment with two or more groups in which participants have not been randomly assigned to groups; participants are measured repeatedly on a dependent variable before, during, and after the “interruption” caused by some event, and the presence or timing of the interrupting event differs among the groups

    ex: same thing as above but this time, do it before christmas for the first floor of the factory , and after christmas for the second floor.
  56. Wait-list Design
    an experimental design for studying a therapeutic treatment, in which researchers randomly assign some participants to receive the therapy under investigation immediately, and others to receive it after a time delay
  57. Small-N Design
    a study in which researchers gather information from just a few cases
  58. Single-N Design
    a study in which researchers gather information from only one animal or one person
  59. Sources of Error
    • statistical fluke
    • situation noise
    • individual differences
    • errors in initial study design
    • lack of/improper execution of replication
    • conscious fraud
    • unconscious bias
    • mistake in data analysis
    • multiple DVs
    • underpowered
    • measurement
    • no effect in study
  60. Parametric Study
    looks at the impact of changing certain parameters within the study
  61. What defines a quasi-experiment?
    • researchers do not have full control
    • participants are not randomly assigned 
    • used when researcher is interested in IV that cannot be randomly assigned 
    • ex: personality traits to assess intelligence: participants assigned based on personality
  62. What defines a small-N study?
    • researchers use a small sample size bc they expect a large effect size
    • obtain a lot of info from a few cases rather than a little info a lot of cases 
    • each participant is treated separately 
    • data represents each individual rather than groups
  63. Internal Validity (N-study)
    • manipulation check
    • within-subject that repeated measured behavior before and after intervention
  64. External Validity (N-study)
    • triangulated by combining the results of the N studies with other studies
    • researchers can specify which population they want to generalize to
    • researchers may not be concerned with generalization at all
  65. Construct Validity (N-study)
    • multiple observers
    • interrater reliability
  66. Statistical Validity
    do not use traditional statistics but still treat data appropriately to provide evidence
  67. Disadvantages of N-study
    • internal validity issues in narrowing down what is specifically responsible for observed effects
    • external validity in generalization to population
Card Set
Psych 344 Study Guide
Study Guide for Final Exam