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Research Methods > Test 2 > Flashcards

Test 2 Flashcards

1
Q

Quantitative data

A

numerical
ex. scales, physiological responses

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2
Q

Qualitative data

A

any form other than numbers
ex. words, sounds, images

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3
Q

qualitative
1. strengths
2. weaknesses

A
  1. rich in detail, captures human experiences, why people do things
  2. resource intensive, smaller samples (*more generalizability), less agreement on appropriate methods of analyzation
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4
Q

quantitative
1. strengths
2. weaknesses

A
  1. can use statistics, how often, how much, more agreement on methods
  2. numbers provide limited information
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5
Q

Qualitative data can be transformed into numbers

A

ex. watch a movie, but count number of times things happen
gain use of statistics, but lose potentially important information

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6
Q

Coding schemes

A

a set of rules for turning qualitative to quantitive (rules on how you measure.
*the specificity reduces the measurement error and improves reliability **you need to be specific

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7
Q

qualitative analysis
*perserving detail

A
  1. inductive (be objective)
  2. deductive (go into it having a theory)
  3. thematic analysis (read data and identify common themes)
  4. reflexivity (acknowledging potential bias)
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8
Q

mixed-methods approach

A

combine qualitative and quantitative
do qualitative + quantitative analysis of qualitative data
ex. get them to rate something, then ask an open-ended question

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9
Q

correlation studies

A

measure 2 or more variables and how they relate
*naturally occurring, no manipulation

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10
Q
  1. positive correlation
  2. negative correlation
  3. Pearson correlation (r)
A
  1. as 1 variable gets larger, so does the other
  2. 1 variable goes up, the other goes down
  3. measures the linear relationship between 2 variables
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11
Q
  1. positive values
  2. negative values
A
  1. values range from 0 to 1
  2. values range from -1 to 0
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12
Q
  1. increases in 1 variable means…..
  2. perfect correlation are…..
  3. limitation:
A
  1. highly predictable second variable
  2. impossible in the real world - complexity of human behaviour, measurement error
  3. only detects linear relationships
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13
Q
  1. correlations do not…..
  2. correlation of 0
  3. correlation of 1
A
  1. represent percentages
  2. no relationship
  3. perfect relationship
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14
Q
  1. effect size
  2. Cohen
  3. Hemphill
  4. very few stronger than…..
A
  1. measuring the strength of the relationship/ association
  2. small 0.1, medium 0.3, and large 0.5
  3. bottom third, middle third, top third
  4. 0.3
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15
Q

square of correlation r2

A

percentage of variance accounted for. Tells you how much the variance in the dependent variable is accounted for by the independent variable.
For example, a model with an R-squared value of 0.9 means that approximately 90% of the variance in the dependent variable is explained by the independent variables. This suggests a strong relationship between the variables and indicates that the model provides a good fit to the data.

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16
Q

You need _____ in both things you are looking at in order to examine correlation

A

variability

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17
Q

_____ differences in correlation correspond to _____ differences in variance

A

small, large

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18
Q

correlation studies limitations
1.
2.
3.
4.
5.
6.

A
  1. correlation is not causation
  2. control for third variables (partial correlation & multiple regression)
  3. temporal information (something needs to cause A before you can measure B)
  4. linear assumption
  5. need variability to see correlation
  6. outliers
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19
Q
  1. Curvilinear relationships best seen through….
  2. important to examine ___ before ____
A
  1. scatter plots
  2. data, analysis
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20
Q

correlation studies strengths
1.
2.
3.

A
  1. naturally occurring
  2. useful when things cannot be manipulated or changed
  3. can be applied to archival data
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21
Q
  1. Naturalistic observation
  2. observations taken over a _____ period are more ____ than a single observation
A
  1. observations for descriptive purposes, with interpretations
  2. longer, accurate
22
Q
  1. Observation in the lab =
  2. Observation outside the lab =
A
  1. naturalistic observation
  2. systematic observation
23
Q

measure naturalistic observation by:
1.
2.
3.

A
  1. coding schemes (quantitative)
  2. interviews (quantitative)
  3. no interfering, no influences
24
Q

N.O
1. strengths
2. weaknesses

A
  1. realism/ecological validity, often rich qualitative detail, exempt from ethical review
  2. difficult, time-consuming, can be lots of data thats hard to interpret
25
Q

Case studies

A

extremely detailed description of a person of a few people. Necessary for unique/rare populations. Can be qualitative or quantitative

26
Q
  1. nomothetic studies
  2. reductionism
A
  1. studies that are from groups of people, but they are averages of a lot of people - which loses detail
  2. finding averages - but losing detail
27
Q

case studies
1. strengths
2. weaknesses

A
  1. lots of detail, rare populations, generate & test hypothesis
  2. generalizibility
27
Q
  1. Archival data
  2. content analysis
A
  1. existing data surrounds, usually publicly available
  2. systematically observing and quantifying (coding schemes), counting instances of interest, analyzing texts (natural language processing)
28
Q

archival data
1. strengths
2. weaknesses

A
  1. ecological validity (real world), lots of data
  2. difficult to organize and obtain, only have what is available, cannot make casual inferences
29
Q

survery & questionnaires

A

most simple way to gather info about a person. *but they must know this info and be willing to share it.

30
Q

response sets

A

ways of responding dont reflect the question content

31
Q

Likert scales need
1.
2.
3.
4.

A
  1. neutral mid-point
  2. number of points depends on question
  3. label all points
  4. is behaviour low-frequency or high-frequency
32
Q

scales of measurement
1.
2.
3.
4.

A
  1. nominal = named categories
  2. ordinal = ranked order
  3. interval = equally spaced (continuous)
  4. ratio = interval with a meaningful zero (continuous)
33
Q

descriptive statistics

A

summarizing your data - an average across items or across items and participants
includes correlation studies

34
Q

central tendency

A
  1. mean = average (sum / # of scores)
  2. median = middle *helpful with extreme scores
  3. mode = most frequent score
35
Q
  1. variability
  2. standard deviation
  3. low standard deviation means..
A
  1. how scores differ from one another, spread of scores, difference in responses
  2. average amount by which scores differ from the mean
  3. more agreement
36
Q
  1. the normal distribution
  2. ___ of all scores are ___ SD above or below the mean
  3. ____ of all scores are __ SD above or below the mean
A
  1. the bell shaped curve, mean and median identical
  2. 68%, 1
  3. 95%, 2
37
Q
  1. deceptive statistics
  2. ___ vs ____ differences
  3. graphing data
A
  1. when not reported accurately, stats can be misleading
  2. relative vs absolute (double sounds like more than going from 1 to 2)
  3. need to check numbers on the axis
38
Q
  1. Inferential statistics
  2. most common form =
  3. H0 vs H1
A
  1. making inferences about the population from sample
  2. null hypothesis significance testing *any procedure that produces a p value
  3. H0 = null hypothesis, no difference, effect being studied doesnt exist
    H1 = alternative hypothesis, there is a difference
39
Q

p value below alpha 0.5 =
p value above alpha 0.5 =

A

reject null hypothesis, statistically significant
fail to reject null hypothesis

40
Q

*** P value
__ p values mean data is ____
Cannot conclude that H0 is ___ or ___ based on data

A

probability that these data could arise, if the null hypothesis were true
small, unlikely
likely or unlikely

41
Q

p value replication

A

the p value you get the first time tells you nothing about what will happen the next time you measure p

42
Q
  1. Trafimow said ….
    P value myths:
  3. 3.
    4.
    5.
A
  1. said NHST was logically invalid
  2. p < 0.5 = null hypothesis is unlikely
  3. rejecting null hypothesis and chance of false positive is 5%
  4. the results would replicate 95 out of 100 times
  5. there is a large difference or effect
  6. there is an important or significance effect
43
Q
  1. Effect size
  2. significant
  3. nonsignificant
  4. Cohens d
A
  1. Measuring the strength of relationship between two variables
  2. p value lower than .05
  3. p > .05
  4. difference in means, in units of SD
44
Q

Confidence intervals

A

our sample sizes can be estimates of population, but theres uncertainty about these estimates
CI are a quantification of this uncertainty

45
Q

The larger the sample size

A

the narrower the CI and less uncertainty

46
Q
  1. statistical power
  2. more power?
A
  1. ability to detect small effects using NHST
  2. Bigger samples
47
Q

Bayesian statistics

A

provide estimates of whether null hypothesis is more or less likely based on data

48
Q

SD of sample vs. population

A

sample = N-1
population = N

49
Q
A

Research Methods (3 decks)

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