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Research Psychology > Exam 2 > Flashcards

Exam 2 Flashcards

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1
Q

Scales of Measurement

A

Nominal, Ordinal, Interval, and Ratio Scales
- Importance: the different scales allow data to be analyzed and processed in different ways

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2
Q

Nominal Scale

A

Classification/categorization of objects/individuals
- Order doesn’t matter
- No numerical meaning
Examples
- male vs. female
- ethnicity
- favorite color

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3
Q

Ordinal Scale

A

Classification/categorization of objects/individuals where the order of categories has meaning
- Unequal differences between categories, intervals aren’t necessarily the same
Examples
- Placement in a race
- Ranking favorite ice cream flavors
- Grades

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4
Q

Interval Scale

A

Categorization of something where order matters and scales of measurement are equal
- Don’t have true zero value
- True zero value: when zero means the nonexistence of the measure (such as 0 people in a population would be an absence of people)
Examples
-Temperature

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5
Q

Ratio Scale

A

Categorization of something where order matters and scales of measurement are equal, and where there is a true zero
- The ratios are meaningful (eg. x is 3x as much as y)
Examples
- Height
- Age
- Weight

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6
Q

Measures of Central Tendency (Averages)

A

Mode (bimodal and multimodal), median, mean, and outliers

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7
Q

Mode

A

The score that occurs most often
- Can be used with any scale
- Only average that can be used for nominal scale data
Bimodal: two scores occur equally often + most frequently
Multimodal: three + scores occur equally often + most frequently

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8
Q

Median

A

The middle point in a set of scores
- 50% of scores fall above this point, 50% below this point
- Can be used for ordinal, interval, and ratio scales

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9
Q

Mean

A

The arithmetic average found by adding all scores and dividing by the number of scores
- Can be used for interval and ratio scales
- May be biased by outliers

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10
Q

Outlier

A

Extreme values much higher/lower than the majority of scores
- Greatest effect on mean
- Wont largely impact median and mode

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11
Q

Measures of Dispersion

A

How spread out data are
- Includes range (inclusive and exclusive), standard deviation, and variance

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12
Q

Range

A

Distance from highest to lowest value
Inclusive: Highest score - lowest score + 1
Exclusive: Highest score - lowest score - 1
- Outliers ruin

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13
Q

Standard Deviation

A

Expresses average distance of scores from the mean
- Only calculated on interval/ratio data

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14
Q

Variance

A

Standard deviation squared
- Only calculated on interval/ratio data (requires mean)

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15
Q

Correlation

A

The degree of a relationship between 2 variables
- Range between -1.00 - 1.00
- Correlation does NOT mean causation
Strength
- absolute value of correlation
- - .90 > .70, .90 > .70
Direction
- positive: when one variable increases, other also increases (and vice versa)
- negative: when one variable decreases, the other increases (and vice versa)

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16
Q

Bivariate Correlation Coefficient

A

Correlation coefficient is referred to as this when the relationship between two variables is being assessed

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17
Q

Types of Correlation

A

Pearson’s product-moment correlation (Pearson’s r)
- used with interval/ratio data
Spearman’s rho (p)
- if one or both variables are ordinal

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18
Q

Multiple Correlation

A

Correlation between multiple variables and one particular variable
- Single score
- 0.00 - 1.00
Multiple regression
- similar but gives info about each predictor variable (individual contributions)

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19
Q

Limits of Correlations

A

-There a different kinds of correlations for different kinds of data
- It is only evidence of a relationship not a cause

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20
Q

Error Variance

A

Natural fluctuations in group caused by something other than independent variable
- Differences within a group
- one measure = standard deviation
- Can be used to find significant difference by completing the equation: differences between groups/differences within groups = effect of independent variable + error variance/error variance

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21
Q

T-Test

A

Used to compare two groups to determine if there is a significant difference between them

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22
Q

Analysis of Variance (ANOVA)

A

Used to compare three+ groups to determine if there is any significant difference

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23
Q

Between-Groups Variance

A

Measurements found by testing different groups on different variables
Pros: Prevents carryover effect
- Shorter duration
- Reduces impact of variables
- Results are easier to interpret
Cons: Requires more participants
- Individual differences can cause results

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24
Q

Within-Groups Variance

A

Measurements found by testing the same group/individual on different variables
Pros: increased statistical power
- Control for individual differences
- Potential reduced error-variance
Cons: Possibility for order effects
- Carryover effects
- Practice effects
- Time measurement effects

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25
Q

Parameter

A

Characteristic of a population

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26
Q

Parametric tests

A

Makes assumptions about population characteristics
- Only usable with interval/ratio data

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27
Q

Nonparametric tests

A

Makes no assumptions about population characteristics

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28
Q

Experiment

A

Researcher manipulates independent variable to see if there are any differences in the dependent variable
- Among equivalent groups
- Use random assignment
- Yields causal information about the effects of an independent variable (when done correctly)

29
Q

Quasi-Experiment

A

Similar to an experiment as an independent variable is manipulated BUT groups aren’t equivalent
- Could be due to lack of random assignment, etc.
- May yield causal information if confounds are eliminated

30
Q

Correlational Study

A

Explore existing effect of subject variable on dependent variable
- Cannot yield causal information
- Identify relationships between subject variable and dependent variable
- Sometimes only option

31
Q

Experimental Group

A

Group experiencing manipulation
- For example, administered real drug

32
Q

Control Group

A

Group that doesn’t experience manipulation OR experiences placebo
- For example, given sugar pill

33
Q

Placebo

A

Inert treatment with no effect on dependent variable
- Helps counteract demand characteristics
- Sugar pills or distractor task

34
Q

How can you ensure that independent variable manipulation is causal?

A
  • Groups must be equivalent before introducing independent variable
  • Must introduce IV before measuring dependent variable
  • Must be free of confounds
35
Q

Random Assignment

A

All participants have an equal chance of being assigned to any group in an experiment
- Can be achieved through: coin flip, pulling names out of a hat, random # table
- Not the same as random selection
- Not a guarantee but should lead to equivalency with enough participants

36
Q

Selection Bias

A

Occurs if researchers choose the participants groups, or if participants choose their own group.
- Can lead to inequivalent groups
- Any difference between groups should be due to random choice in random assignment

37
Q

Matching

A

Identifies pairs (or triplets, quadruplets, etc.) of participants who measure similarly on a characteristic related to dependent variables and then randomly assigns each of the participants to a different experimental condition
- Equivalent on 1+ important characteristic
- Good with limited subject pool, not good enough for good random assignment
Downsides
- Can be difficult to find good matches
- Only controls for matched characteristic

38
Q

Pretesting

A

Essentially the same as the actual experiment
- Establish a measure related to the dependent variable
- Not always practical as it can cause ceiling effect (could raise all scores too much)

39
Q

Subject Variable

A

Characteristic of participants that can’t be changed
- Makes random assignment impossible
Examples
- Gender
- Age

40
Q

Cross-Sectional Design

A

Examines differences across age groups
- Different individuals for different age groups

41
Q

Extraneous Variables

A

Variables other than the independent variable that can effect the dependent variable
- Must be equivalent across groups
- Causes confounded results when the extraneous variables change with the independent variable
- Can be controlled through balance and holding constant
- AKA confounds

42
Q

Confounds

A

Extraneous variables or any other flaw in research
- Fewer confounds = more internal validity
Examples
- Experimenter Bias
- Demand Characteristics
- Instrumentation Effects
- Subject Attrition

43
Q

Experimenter Bias

A

Any confound caused by research expectations
- Interpretation of ambiguous data
- Treating experimental/control groups differently

44
Q

Demand Characteristics

A

Any confound caused by participant expectations
- “What does experimenter hope to find”

45
Q

Instrumentation Effects

A

Occurs when instrument used to measure dependent variable changes in accuracy over time
- Machines/tools wear out
- People learn
- People get tired
- Leads to differences in how each participant is measured

46
Q

Subject Attrition

A

Participants may leave study partway through
- Nonsytematic: leaving for reasons unrelated to independent variable, may not threaten internal validity if it is more or less equal across conditions
- Systematic: participants leave study in unequal numbers from different groups, possibly related to independent variable, severe threat to internal validity

47
Q

Single Blind Procedure

A

Participant doesn’t know condition

48
Q

Double Blind Procedure

A

Experimenter + participant don’t know condition

49
Q

How can you ensure internal validity?

A

Ensure different groups experience almost the same circumstances
- Time
- Location
- Researchers
- What they’re told
- Weather
- How researcher is dressed
- Etc.

50
Q

Types of Within-Subject Designs

A

Pretest-posttest design, repeated measures design, longitudinal design

51
Q

Pretest-posttest Design

A

Two measures of dependent variable for each participant
- One before independent variable manipulation
- One after

52
Q

Repeated-measures Design

A

Multiple dependent measurements for each participant

53
Q

Longitudinal Design

A

Repeated-measures design that occurs over an extended time (days, weeks, months, years)
- Often paired with cross-sectional studies

54
Q

Benefits of Within-Subjects Design

A

Require fewer participants
- Each participant provides a score or scores for each level of the independent variable
Lower error variance
- No difference between groups
- Same variability “should” hold for different levels of independent variable

55
Q

Disadvantages of Within-Subjects Design

A

Particularly susceptible to demand characteristics
- More opportunities to guess study purpose
- Expectation to do “differently” in different conditions
- May require use of “ethical” deception (eg. placebo)
Carryover Effect
- Practice Effects
- Fatigue Effect
- Counterbalance
- History Effect
- Maturation Effect
- Testing Effect
- Regression toward the mean
Cannot test subject/selected variables
- Despite flaws, proper control can compensate for many disadvantages just not the last one

56
Q

Practice Effect

A

Get better at task each time

57
Q

Fatigue Effect

A

Get worse at task each time

58
Q

History Effect

A

Something happens during study that effects dependent variable measures

59
Q

Maturation Effect

A

People and their scores may naturally change over time

60
Q

Testing Effect

A

People improve on tests with multiple takings
- Independent of anything else

61
Q

Regression toward the mean

A

Tendency for extreme scores to normalize when retested
- Independent of manipulation
- Common when participants are selected based on pretest

62
Q

Counterbalancing

A

Present different experimental conditions to participants in different orders
- Controls for many carryover effects
- May be complete/incomplete counterbalancing

63
Q

Complete Counterbalancing

A

All subjects experience each condition several times until they have experienced every possible order
- Any carryover effects should influence all conditions equally
- ABBA Counterbalancing
- Block Randomization

64
Q

ABBA Counterbalancing

A
  • Used w/ two experimental conditions
  • Ensures first condition is also the last
  • May be repeated as often as necessary
  • Potential Issues: linear improvement vs. nonlinear practice effects, may lead to issues if participants notice patterns
65
Q

Block Randomization

A

Each block consists of a single presentation of each experimental condition in a unique order
- Presented in random order
- Useful for 3+ conditions
- Need enough blocks for randomization to work
- Each condition should be first/last a roughly equal number of times

66
Q

Incomplete Counterbalancing

A

Participant receives unique order of all conditions at least once
- Does not receive all possible orders
- Complete design isn’t always practical, leading to incomplete counterbalancing
- Must have enough participants to counterbalance potential fatigue + practice effects
- Random Order with Rotation
- Balanced Latin Square (Latin Square)

67
Q

Random Order with Rotation

A

Establishes random order and use that order with first participant, move first condition to last condition for each participant
- eg. DBCAE to BCAED to CAEDB
- Make sure # of participants is a multiple of possible orderings

68
Q

Balanced Latin Square (Latin Square)

A

Each condition is presented in each possible position, and presented before/after each other condition
- Even # of conditions: same # of orderings as conditions
- Odd # of conditions: twice as many blocks (5 conditions = 10 orderings)
- Choose # of participants that is a multiple of the # of orderings

69
Q

What are the limitations of counterbalancing?

A
  • Amount time
  • # of participants
  • Permanent changes

Research Psychology (4 decks)

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