Test 2

Question 1

Quantitative data

Answer 1

numerical
ex. scales, physiological responses

Question 2

Qualitative data

Answer 2

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

Question 3

qualitative
1. strengths
2. weaknesses

Answer 3

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

Question 4

quantitative
1. strengths
2. weaknesses

Answer 4

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

Question 5

Qualitative data can be transformed into numbers

Answer 5

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

Question 6

Coding schemes

Answer 6

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

Question 7

qualitative analysis
*perserving detail

Answer 7

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)

Question 8

mixed-methods approach

Answer 8

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

Question 9

correlation studies

Answer 9

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

Question 10

1. positive correlation
2. negative correlation
3. Pearson correlation (r)

Answer 10

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

Question 11

1. positive values
2. negative values

Answer 11

1. values range from 0 to 1
2. values range from -1 to 0

Question 12

1. increases in 1 variable means…..
2. perfect correlation are…..
3. limitation:

Answer 12

1. highly predictable second variable
2. impossible in the real world - complexity of human behaviour, measurement error
3. only detects linear relationships

Question 13

1. correlations do not…..
2. correlation of 0
3. correlation of 1

Answer 13

1. represent percentages
2. no relationship
3. perfect relationship

Question 14

1. effect size
2. Cohen
3. Hemphill
4. very few stronger than…..

Answer 14

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

Question 15

square of correlation r2

Answer 15

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.

Question 16

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

Answer 16

variability

Question 17

_____ differences in correlation correspond to _____ differences in variance

Answer 17

small, large

Question 18

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

Answer 18

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

Question 19

1. Curvilinear relationships best seen through….
2. important to examine ___ before ____

Answer 19

1. scatter plots
2. data, analysis

Question 20

correlation studies strengths
1.
2.
3.

Answer 20

1. naturally occurring
2. useful when things cannot be manipulated or changed
3. can be applied to archival data

Question 21

1. Naturalistic observation
2. observations taken over a _____ period are more ____ than a single observation

Answer 21

1. observations for descriptive purposes, with interpretations
2. longer, accurate

Question 22

1. Observation in the lab =
2. Observation outside the lab =

Answer 22

1. naturalistic observation
2. systematic observation

Question 23

measure naturalistic observation by:
1.
2.
3.

Answer 23

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

Question 24

N.O
1. strengths
2. weaknesses

Answer 24

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

Question 25

Case studies

Answer 25

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

Question 26

1. nomothetic studies 2. reductionism

Answer 26

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

Question 27

case studies 1. strengths 2. weaknesses

Answer 27

1. lots of detail, rare populations, generate & test hypothesis 2. generalizibility

Question 27

1. Archival data 2. content analysis

Answer 27

1. existing data surrounds, usually publicly available 2. systematically observing and quantifying (coding schemes), counting instances of interest, analyzing texts (natural language processing)

Question 28

archival data 1. strengths 2. weaknesses

Answer 28

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

Question 29

survery & questionnaires

Answer 29

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

Question 30

response sets

Answer 30

ways of responding dont reflect the question content

Question 31

Likert scales need 1. 2. 3. 4.

Answer 31

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

Question 32

scales of measurement 1. 2. 3. 4.

Answer 32

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

Question 33

descriptive statistics

Answer 33

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

Question 34

central tendency

Answer 34

1. mean = average (sum / # of scores) 2. median = middle *helpful with extreme scores 3. mode = most frequent score

Question 35

1. variability 2. standard deviation 3. low standard deviation means..

Answer 35

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

Question 36

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

Answer 36

1. the bell shaped curve, mean and median identical 2. 68%, 1 3. 95%, 2

Question 37

1. deceptive statistics 2. ___ vs ____ differences 3. graphing data

Answer 37

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

Question 38

1. Inferential statistics 2. most common form = 3. H0 vs H1

Answer 38

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

Question 39

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

Answer 39

reject null hypothesis, statistically significant fail to reject null hypothesis

Question 40

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

Answer 40

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

Question 41

p value replication

Answer 41

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

Question 42

1. Trafimow said .... P value myths: 1. 2. 3. 4. 5.

Answer 42

1. said NHST was logically invalid 1. p < 0.5 = null hypothesis is unlikely 2. rejecting null hypothesis and chance of false positive is 5% 3. the results would replicate 95 out of 100 times 4. there is a large difference or effect 5. there is an important or significance effect

Question 43

1. Effect size 2. significant 3. nonsignificant 4. Cohens d

Answer 43

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

Question 44

Confidence intervals

Answer 44

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

Question 45

The larger the sample size

Answer 45

the narrower the CI and less uncertainty

Question 46

1. statistical power 2. more power?

Answer 46

1. ability to detect small effects using NHST 2. Bigger samples

Question 47

Bayesian statistics

Answer 47

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

Question 48

SD of sample vs. population

Answer 48

sample = N-1 population = N