Psyc 2020 #Exam2

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Psyc 2020 #Exam2
2013-11-25 22:41:24
Psyc 2020 Exam2

Psyc 2020 #Exam2
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  1. Social Desirability
    How to prevent this in the future.
    A socially desirable response which has nothing to do with the question.

    Prevention: Control privacy & confidentiality
  2. Simplicity wording
    Avoid jargon terms and technical terms; define complex terms before asking about them.
  3. Negative wording
    • Avoid negative wording.
    • Ex: Do u think that they should not.....
  4. yea saying & Nay saing
  5. Questionnaires & Interviews are part of _________ research.
    Pros & Cons
    • Surveys Pros: Easy, cheap, fast, Lg groups
    • Survey Cons: Hard 2 identify age/gender & poor response rate.
  6. Double barreled Question
    • Two questions with one response.
    • Ex: should you forgive a friends gay rights or life choices?
  7. Loaded Question
    A question that might have strong wording and forces people to answer in only one way.

    Ex: do you beat ur kids?
  8. Closed Ended Question
    • Fixed number of responses
    • Rating scales are often used 5-7 points
    • (strongly agree to strongly disagree)
  9. Open Ended Questions
    Free response
  10. Interviews can be done.......
    Pros & Cons
    face to face, on telephone, or focus groups.

    • Pros: Unexpected / Unthinkable answers
    • Cons: Lose anonymity & Interviewer Bias
  11. Questionnaires:
    Pros / Cons
    Pros: Can mail, use on internet, not very costly, respondents = anonymous.
  12. It is often impossible to include all of the population that's why a ________ is used to represent it.
  13. How to measure confidence interval.
  14. What is probability sampling?
    All population is included and it is rare & hard to conduct research this way.

    Ex; Simple random sampling: in this way every member of the population has an equal chance to be selected.

    Often times hard to include everyone so that's why non probability sampling is more commonly used.
  15. Even if random sampling is used, what can be a two ways for bias to occur?
    • The sampling frame used (ex white pages)
    • Poor response rates.
  16. Non probability sampling:
    Sampling in which one cannot specify the probability  that any member of the population will be included in the sample.
  17. Internal Validity:
    Certainty that IV manipulation caused the changes in the DV. AND NOT confounding variables
  18. External Validity:
    Ability 2 generalize results
  19. What is confidence interval?
    range that tells you how accurate you think you are.
  20. Confounding
    variable =
    a variable that is not controlled in a research   investigation.

    • e.g.,
    • If I had married couples complete a difficult puzzle in   either a hot room (experimental cond.) or a normal   temperature room (control cond.), but did not
    • control the   size of the room,
    • confounding may occur
  21. Basic Simple Experimental design
    2 Variables ( IV & DV)

    & IV only has two levels
  22. Post-test - Only Design =
    a true experimental design in which the DV (post-test) is measured only once, after manipulation of the IV.

    • e.g.,
    • an experimental group that receives the treatment and a  control group that does not

    •   e.g., use two different amounts of the IV
    • or
  23. Pretest-Posttest Design =
    a true experimental design in which the DV is   measured both before (pretest) and after (posttest) the manipulation of the IV.


    *Disadvantages:  time consuming & pretests can sensitize participants   to what you are studying
  24. Solomon four-group design
    combines both the posttest-only and the pretest-  posttest design.  (This lets you assess the impact of the pretest.)
  25. Three ways of assigning participants to experimental conditions:
    • Independent Groups Design (AKA between subjects deign)
    • Repeated Measures Design (AKA within subjects design)
    • Matched Pairs Design (match participants 1st then randomly assign)
  26. Independent Groups Design (AKA between subjects deign)
    Repeated Measures Design (AKA within subjects design)
    Matched Pairs Design (match participants 1st then randomly assign)
    • Independent Groups Design (AKA between subjects deign)
    •    -Researchers randomly assign participants to ONE level of IV
    • Repeated Measures Design (AKA within subjects design)
    •    -Researchers assign participants to ALL levels of the IV.
    • ADV: Fewer research participants needed because running twice.
    • DISadv: Practice effect, fatigue effect, or carryover effect

    • Matched Pairs Design (match participants 1st then randomly assign)
    •    -Ex: B4 experiment give IQ test - List in order - pair accordingly with one in exp group and one in control group.
  27. To deal with problems, researchers can employ ____________ ___________ or increase the time interval between treatments.
    Counterbalancing techniques
  28. Setting the stage involves:
    • Informed consent
    • Explain why experiment is being conducted.
    • Must introduce IV & DV within context of the experimental setting.
  29. Straightforward manipulation:

    Staged Manipulations:
    • Straightforward manipulation:
    • Variables are manipulated with instructions & stimulus presentations
    • (Most manipulations of IVS in research are like this)

    • Staged Manipulations:
    • necessary to stage events in order to manipulate the IV successfully
    •   *They often employ a confederate
  30. Best way to get the strength of the manipulation?
    Past research while keeping ethics in mind.
  31. DV in most experiments is one of three general types:
    • Self Report Measures
    • Behavioral Measures
    • Physiological Measures
  32. 1)  Self-Report Measures

    Ppl provide info about their own attitudes, beliefs, and behaviors

    Advantages: you get info it straight from them.

    Disadvantages: They may lie, social desirability (make themselves appear in + light)
  33. Behavioral Measures
    Advantages / Disadvantages
    measures in which direct observation of behavior occur


    • Disadvantages: May not always catch
    • everything. Need to use IOA reliabilty.
  34. 3)  Physiological Measures
      … measures of people’s body responses (e.g.,heart rate, temperature, muscle tension)


  35.  *The DV should be __________
    enough to detect differences between

    • Ex:
    • Men's measurement of how attractive they view their wife. Watch 30 min pornography,
    • and control group does nothing, then ask the men after video if wife is
    • attractive.

    Is this sensitive enough? No!
  36. Ceiling Effect =
    • Ceiling effect = failure of a measure to detect a
    • difference because it was too easy
  37. Floor Effect =
    failure of a measure to detect a difference because it was too hard

    So in depth that there is no correlation
  38. Demand Characteristics =

    How can we eliminate demand characteristics?
    • cues
    • that inform the subject how he or she is expected to behave

    • Ex: asking them 50x after taking a
    • caffeine pill…… if they are nervous.

    • We want to eliminate demand
    • characteristics, you can use deception to control for this, use filler
    • questions, use a control group a group that doesn't get the treatment.
  39. Expectancy effects =
    any intentional or unintentional influence that the experimenter exerts on subjects to confirm the hypothesis under investigation
  40. Blind & Double Blind Study
    Blind Study: Participants do not know which group they are in

    Double Blind Study: The experimenter also do not know which group participants are in.
  41. To control for expectancy effects, researchers can:
    • -
    • train extensively & practice behaving consistently

    •   - run all conditions simultaneously or
    • make the procedures   automated

    •   - make the experiment double blind (hire
    • uninformed people)
  42. Pilot studies:
    • You
    • run your study doing a trial run with a smaller group.

    • Point
    • is to see if there is any flaws in the study and then can fix them before runing
    • actual experiment.

    • Ask
    • questions after trial run to see if they knew what your hypothesis was….. If
    • so…. U messed up somewhere.
  43. What are factorial designs?
    Designs with more than one IV
  44. 5x5 factorial design
    How many:
    Main Effects
    • Groups: 25 groups
    • Levels: 10 Levels
    • IVS: 2 IVS
    • Main Effects: 5 Main effects
  45. Factorial design yields two things:
    • Simple main effects: effects of 1 IV at a particular level of another IV.
    • Main effects = Effects of IV by itself.5IV = 5main efects
  46. Moderator variable
  47. Factorial designs yield two kinds of information
    • The main effect of an IV
    • Whether there is an interaction between variables
  48. You run a nova to if the information is ______ ________.

    This measures if it was just a fluke or is it significant.
    statistically significant
  49. What experimental design is run to see the effect on ONE participant.
    Also has no control group.
    Reversal Design: Treatment is introduced after a baseline (control) period. ABA

    • A = Baseline B= Treatment
    • Baseline acts as control group / Comparison

    Single case design is when there is no control group present.
  50. Multiple Baseline design
    Why might this be used?
    Observing behavior before & after a manipulation under multiple circumstances.

    Used in certain situations, it may be unethical to remove a treatment in order to test its   effect

    Ex: instead of using 1 person to test drug. Use 3 people to see if yield same results. Gets rid of extraneous variables and flukes.

    • P1 Drug Day: 10
    • P2 Drug Day: 20
    • P3 Drug Day: 30
  51. Are Quasi experiments true experiments?

    What are quasi Experiments & why are they not true experiments?

    Quasi-experimental designs allow us to study the effect of an IV on a DV, but lack the control features of true experimental designs
  52. One-Group Post test-Only Design
    One-Group Posttest-Only Design  (Partici -> IV ->  DV)

    • Ex:
    • park bench, ur accomplice sits next to stranger and you time how long until they get up &
    • leave.
    • ^Problem:

    Problems?  … no control group to aid in interpreting the results.
  53. What do you need for an experiment to be a true experiment?
    • Comparison group
    • &
    • Random assignment!
  54. One-Group Pretest-Posttest Design
    (participants -> DV -> IV (treatment) -> DV)

    • Problem:
    • no control group to rule out history effects, maturation effects, instrument decay,etc.

    • EX:
    • Program: Trying to help ppl quit smoking
    • Pretest measures how much they smoke (self report)
    • Treatment Given (program)
    • Post test measures again. (self report)
  55. History Effects:
    • any event that occurs between 1st
    • & 2nd measure & Is not part of the manipulation

    • If Obama diagnosed with stage 4 terminal lung cancer between measures it may
    • effect data.
  56. Maturation Effect:
    Maturation Effect: Change due to time and not the independent variable.

    • Ex:
    • Jealousy (longitudinal study) comparing year 1 to year 10.  IT can be naturally occurring.
  57. Instrumental Decay:
    Your measurement doesn't measure accurately over time.
  58. Nonequivalent control group design

    Like a true experiment ( has control group) BUT no random assignment is used.

    Problem: selection differences may affect the results.

    • Ex:
    • smoking ex: Get 100 volunteers à
    • creates bias -> two groups but not = groups

    • AKA
    • selection differences
  59. Nonequivalent Control Group Pretest-Posttest Design

    How is it similar to true experiment?
    *This is one of the most useful quasi-experimental designs.

    NO RANDOM ASSIGNMENT but... pretest scores act as a comparison group. Can compare with post test.

    • *No
    • control group but because of pretest you do have a comparison group.
  60. Cross-sectional design:
    cross-sectional designs, compare people at different  stages or ages in a developmental process)

    • Pros: yields immediate results
    • Problem: May cause cohort effects & confound with age differences.
  61. Longitudinal Designs

    With longitudinal designs, participants of the same age are followed with repeated measurements over a period of time.

    • Pros: yields valuable info
    • Cons: expensive, many years, & ppl dropping out of study (participant attriation)
  62. participant attrition
    Participants dropping out of the study
  63. Retrospective designs
    This design relies on participants recall of past events.

    Peoples memories can be inaccurate (bias, forgetting)
  64. Sequential Designs
    Cross sectional & longitudinal methods are combined.

    • Compare two sections of population & then when they are older they are measured again & again & again and see how they
    • change over time.
  65. When analyzing results when is it best to compare group percentages?

    If I’m studying whether males and females differ in their interest in sex, and ask males and females whether they like or dislike (yes or no) sex (a nominal
    variable), I can compare group   percentages
    When studying NOMINAL VARIABLES.
  66. When is it useful to correlate individual scores?

    when individuals are measured on 2 variables that have a range of numerical values


    • e.g.,
    • If  I’m studying location in classroom
    • and grades in the   class, I can determine
    • if there’s a relationship between the   two
    • variables.
    • or
    • Ex: height & income level
  67. When is it useful to compare group means?

    Useful when studying participants in 2 or more groups.


    • If I’m studying the effect of exposure to
    • violent video games, I can compare the average # of aggressive acts demonstrated in my experimental and control conditions after the manipulation
  68. What is frequency distribution?

    Frequency Distribution = an arrangement of a set of scores that indicates the number of times each score was obtained

    Ex: grades (ABCDF) It will show us how many of each was earned in the class can be charted using graphs, pie charts, and more.
  69. What are descriptive statistics?
    • Descriptive Statistics = statistical measures that describe the results of a study; descriptive
    • statistics include measures of central tendency(mean, median, mode), variability, and correlation.
  70. Central Tendency Statistic:

    Tells us?
    What are they?
    Central Tendency Statistic tells us what the sample as a whole is like.

    Mean, Median, Mode.
  71. Variability

    What is it?
    What are the 2 kinds of variability?
    Is a number that characterizes the amount of spread in a distribution of scores.

    • 2 kinds:
    • Standard Deviation (avg dev. from mean)  & Range(Highest - lowest)
  72. The SD is small when....
    most ppl have similar scores close to the mean.
  73. SD is large when most ppl......
    have scores that lie further from the mean.
  74. Pearson product-moment correlation

    Represented by?
    Tells us?
    Can range from?

    How to read the scale.
    • Represented by: r
    • Tells us: how strongly 2 variables r related & direction of relationship.

    Can range from: 0  -- +1  &   0 -- -1

    - A correlation of 0.00 means that the two variables are not related

      -When the correlation coefficient is positive, that means that there is a   positive linear relationship

    •  - When the correlation coefficient is
    • negative, that means that there is a negative linear relationship
  75. What does    r= -.88 mean?
  76. How to determine (from number) the strength of Pearson correlation coefficient.
    —r = +.70      or      Very strong positive relationship

    —r = +.40  - +.69  Strong Positive Relationship

    —r = +.30 - +.39      Moderate positive Relationship

    —r = +.20 - +.29  Weak Positive Relationship

    —r = +.01 - +.19  No / Little Relationship
  77. What are inferential statistics?

    Ex of inferential statistics
    • statistics designed to determine whether results based on sample data are generalizable
    • to a population.

    Ex: t-test & F tests
  78. Statistical inference begins with.......
    Two goals of an experiment?
    • statement of the null hypothesis and a research hypothesis.
    • -
    • The Null Hypothesis states that the observed difference in group means is caused   by chance, and not the IV (that the IV had no effect)

      - The Research Hypothesis states that the observed difference in group means is   due to the IV (that the IV did have an effect)

    The goal of an experiment is to be able to reject the null and accept the research hypothesis!

  79. When is a result considered statistically significant?
    What is it for psych?
    What is alpha level?
    If there is a low probability that the difference between the obtained sample means was due to random error.

    • Psych .05
    • Alpha level: probability required for significance.
  80. What are t tests and F tests
    When are they used?
    What do they determine?
    t test = a statistical significance test used to compare differences between 2 group means

    •   … the t value is a ratio of two aspects of the
    • data, the difference between the group
    • means & the variability within groups
    • Will tell u if difference is statistically difference or not. 

    • F test (AKA analysis of variance or ANOVA) =
    • a statistical significance test for determining whether two or more means
    • are
    • significantly different

      … F is the ratio of systematic variance to error variance
  81. What are Type I and Type II errors?
    Type I error = an incorrect decision to reject the null hypothesis when it is true

    • The wrong decision of rejecting the null.
    • (ex: u are saying drug works when in reality it doesn't

    Determined by Alpha Level (.05)

    •   *The probability of making a Type I error is
    • determined by the alpha level chosen

    •   e.g., If the alpha level chosen is .05 and the
    • null hypothesis is rejected,

    •   there are 5 chances out of 100 that the
    • decision is wrong

      Type II error = an incorrect decision to accept the null hypothesis when it is false

    • (ex: that the drug did not work but the
    • drug actually did work)

    •   *Making a Type II error is related to 3
    • factors:

    •   -
    • alpha level ( if alpha level is set too low you
    • become more likely to make type II eror)

    •   - sample size (if
    • the sample size is too low )

    •   - effect size (strength
    • of relation ship between variables – Ex: half pill – future research full  pill)

    • Type II error is worse because if a drug does actually work and we indicate it does
    • not work it wont get published.
  82. Non significant results can occur for a number of reasons:

    Non significant results do not necessarily indicate that the null hypothesis is correct.
    • 1)  A true relationship between the variables
    • does not exist

      2)  A true relationship exists, but wasn’t detected because:

    • - the study was poorly designed
    • (incomprehensible instructions, a weak   manipulation of the IV, a dependent measure that is unreliable)

    - the alpha level was set too low

    - the sample size was too small

    • - the relationship is weak so the effect size
    • (difference between groups) is too small
  83. Convenience Sampling:
    Representative samples:

    An incorrect representation of the population affects the external validity.
  84. Volunteer & Gender & Locale Bias
    • *Volunteers:
    • are more highly educated, wealthier, more in need of   approval, and more social than nonvolunteers

    •  *Males and females sometimes respond
    • differently too; therefore, it’s a good idea to include a proportionate ratio of males to females

      *Locale bias can affect external validity too

    •   There should be cultural considerations as
    • well
  85. Two types of Replication
    • 1)  Exact Replication – replication of research
    • using the precise procedures of a study to see whether the same results are obtained

    •   2) Conceptual Replication
    • replication of research using different procedures for manipulating or measuring the
    • variables
  86. Possible reasons for repeated failures:
    -the original results reflect a Type I error

    -the original researcher misrepresented the results

    • -the original results could only be obtained
    • under certain limited circumstances

    •  -the characteristics of the original
    • experimenter biased the results