Brainscape's Knowledge GenomeTM

Browse over 1 million classes created by top students, professors, publishers, and experts.


See full index

PSY 301 RMA > 2017-11-07 02 Exam > Flashcards

2017-11-07 02 Exam Flashcards

1
Q

Normal distribution and skewness

A

(Manual, 35)

- A skewness between -2 and +2 is normal

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

scales of measurement

A

(in-class 9/4) (A &; L, 79-83)

  • nominal
  • ratio
  • interval
  • ordinal (not needed)
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

nominal scale

A 
(in-class 9/4) (A & L, 80)
-used to measure categories
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

ratio scale

A

(in-class 9/4) (A & L, 80)

  • true zero (fixed-point)
  • used to measure quantities
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

interval scale

A

(in-class 9/4) (A & L, 80)

  • used to measure ratings
  • identity (each number has a specific meaning), order (numbers on a scale, in ordered sequence), equal intervals (distance between numbers on the scale is equal)
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

operational definition

A

(in-class 9/4) (A & L, 77)

  • specifics of how the variable is measured
  • so it can be exactly replicated
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

central tendency

A

(A & L, 147)

  • central score
  • summarizes center of distribution
    • mode, median, mean
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

mode

A

(A & L, 149)
measures central tendency
- most frequent score in a distribution

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

median

A

(A & L, 149)
measures central tendency
- halfway point of distribution

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

mean

A

(A & L, 149)
measures central tendency
- arithmetic average

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

variability

A

(A & L, 150)

  • how much scores are different from each other in a sample
    • observed minimum, observed maximum, range, standard deviation
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

observed minimum

A

(A & L, 150)
measures variability
- lowest score in the sample

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

observed maximum

A

(A & L, 150)
measures variability
- highest score in the sample

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

range

A

(A & L, 150)
measures variability
- distance between observed minimum and maximum

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

standard deviation

A

(A & L, 150)
measures variability
- how much in general the scores in a sample differ from the mean

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

descriptive statistics

A

(A & L, 142)

  • used to analyze quantitative and qualitative data
  • quantitative analysis used to summarize characteristics of a sample
  • CT: mode, median, mean
  • Variability observed minimum, observed maximum, range, standard deviation
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
17
Q

descriptive statistics for nominal data

A

(A & L, 170)

  • frequencies and/or percentages
  • CT: (sometimes mode)
  • variability: –
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
18
Q

descriptive statistics for interval or ratio (normal distribution)

A

(A & L, 170)

  • (sometimes: percentages for each score on an interval scale)
  • CT: mean
  • variability: standard deviation (sometimes: possible min/max for interval, observed min/max for interval and ratio)
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
19
Q

descriptive statistics for interval or ratio (skewed)

A

(A &; L, 170)

  • (sometimes: cumulative percentage)
  • CT: median
  • variability: observed min/max or range
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
20
Q

sampling

A

(A & L, 119)

- process of how the sample is selected

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
21
Q

probability sampling (random sampling)

A

(A & L, 121)

  • sampling procedure that uses random selection
  • ideal, (external validity/generalizable)
    • simple random, stratified random, cluster sampling
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
22
Q

non-probability sampling (non-random sampling)

A

(A & L, 123)

  • sampling procedure that doesn’t use random selection
    • less time (no need to identify all participants [members, clusters] in a population)
    • if researcher can’t identify all members/clusters, appropriate sample size, and/or minimize non-response data
    • convenience, quota, maximum stratification, snowball,
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
23
Q

convenience sampling

A

(A & L, 129)
non-probability sampling
- sample is volunteers who are readily available and willing to participate
- typically have an over-represented group
- easiest (feasable)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
24
Q

snowball sampling

A

(A & L, 132)
non-probability sampling
- participants recruit others into the sample

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
25
Q

independent variable (and levels)

A

(A & L, 21) (in-class 8/29)

  • variable that’s manipulated in an experiment
  • Levels: a control group and then 1 or more other assignments/groups
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
26
Q

dependent variables

A

(A & L, ) (in-class 8/29)

  • variable that’s measured in an experiment
  • expected to change based on IV
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
27
Q

pilot study definition

A

(in-prac 9/18)

  • still with target population
  • test before spending money
  • work on any possible changes
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
28
Q

Pilot studies can find problems with

A

(in-class 10/24)

  • recruitment
  • retention (who will stay?)
  • implementation (measures good?)
  • assessment (is it accurate?)
  • new methods (money)
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
29
Q

Experimental design

A 
(A & L, 19)
1 - random assignment
2 - IV manipulated (at least 2 levels)
3 - DV measured
Main benefit: can determine causality
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
30
Q

Random assignment definition

A

(A & L, 280, 184)

  • essential for an experiment
  • participants (already selected) chosen at random to IV conditions/levels
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
31
Q

Random assignment and purpose

A

(A & L, 280, 284)
- increases internal validity
- IV groups to be as similar as possible before IV exposure
- evens out individual differences across IV conditions
(in-class 10/26)
- any group differences between groups isn’t the confounds (confounds affect both groups)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
32
Q

Independent variable (and levels)

A

(A & L, 21) (in-class 8/29)

  • variable that’s manipulated in an experiment
  • Levels: a control group and then 1 or more other assignments/groups
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
33
Q

IV manipulation: reliable and valid

A

(A & L, 308)

  • need equivalent IV levels/conditions
  • manipulation check
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
34
Q

Manipulation checks

A

(A & L, 292)
- Checking if what you manipulated what you wanted to manipulate
EX: if part of the study was to read a book, quiz participants on their comprehension of the book

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
35
Q

Pilot studies can find problems with

A

(in-class 10/24)

  • recruitment
  • retention (who will stay?)
  • implementation (measures good?)
  • assessment (is it accurate?)
  • new methods (money)
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
36
Q

Confounding variables definition

A 
(In-class 10/24)
- History effect
- Maturation effect
- Testing effect
- Instrumentation effect
- Regression to the mean (statistical regression)
(( usually more than one at once ))
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
37
Q

Confounding variables: ways to limit

A

(In-class 10/24)

  • random assignment
  • manipulate ONE variable
  • need equivalent IV levels/conditions
  • large sample size
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
38
Q

History effect

A

(In-class 10/24)

  • (due to experiences or environmental factors)
  • changes may be due to outside events
  • anything external to the study
  • if only affecting one IV group (as average), then probably history confound
39
Q

Maturation effect

A

(In-class 10/24)
-(due to experiences or environmental factors)
- changes due to participants’ internal changes over time
- more likely across long time periods or with young children
(kind of opposite history confound)

40
Q

Testing effect

A

(In-class 10/24)
-(due to experiences or environmental factors)
- repeated testing can impact results
EX: students used to professor’s tests, not knowledge growth

41
Q

Instrumentation effect

A

(In-class 10/24)
-(due to experiences or environmental factors)
- changes in measurement instrument can cause changes in DV
EX: measuring children < 3y/o on a table, and > 3 y/o standing

42
Q

Regression to the mean (statistical regression)

A

(In-class 10/24)

  • (due to participant characteristics)
  • scores that are selected because they’re extreme are likely to be less extreme when retested
43
Q

Threats to internal validity

A

(In-class 10/24)

  • Confounds:
  • History effect
  • Maturation effect
  • Testing effect
  • Instrumentation effect
  • Regression to the mean (statistical regression)
44
Q

Criteria for causality

A

(A & L, 271)

  • Correlation: (relationship between A and B)
  • Sequence: (change in A comes before change in B)
  • Ruling out confounds: (controlled for possible confounds, so A must be the only factor to cause change in B)
45
Q

Inferential statistics definition

A

(A & L, 185)

- statistical analysis of data from one sample to draw conclusions about population sample is from

46
Q

Descriptive statistics vs inferential statistics

A

Descriptive statistics: depend on skewness and scale of measurement (Central tendency and variability)
Inferential statistics: depend on scale of measurement and levels of IV

47
Q

Null hypothesis

A

(A & L, 190) (in-class 10/26)
- prediction of no difference between groups
- don’t assume reader already knows IV, DV, or levels
EX: “no difference” “similarly”

48
Q

Alternative hypothesis (directional)

A

(A & L, 197)
-(one-tailed)
- prediction of the direction the results from a sample will differ from the population
EX: “better than” “highest”

49
Q

Alternative hypothesis (non-directional)

A

(A & L, 197)
-(two-tailed)
- prediction that results from a sample will differ from the population without saying how
EX: “there will be a difference”

50
Q

Alternative directional hypothesis for multi-level

A

(in prac 10/30)
- mention DV
- mention IV levels and how they’re related to each other
EX: “A will be more than B” “followed by”

51
Q

Type I error

A
  • When rejecting NULL

- less than (

52
Q

Type II error

A
  • When retaining NULL
  • more than (>) .05
  • can never know chance
53
Q

Reject null

A
  • Type I error

- chance is p value ( less than (

54
Q

Retain null

A

Type II error
when p is more than (>) .05
- can never know chance

55
Q

Power definition and impacts

A

(A & L, 205)

  • Ability to correctly reject the null hypothesis
  • Factors:
    • sample size
    • amount of error
    • strength of effect
56
Q

Power: how to increase

A
  • increase sample size
  • increase effect size
  • increase within-group homogeneity
  • increase between-group heterogeneity
57
Q

Between groups variance (treatment variance)

A

(A & L, 330)

  • variability between groups/levels/conditions
    • want to MINimize this
58
Q

Within groups variance (error variance)

A

(A & L, 330)

  • variability among participants scores (in same group/level/condition)
    • want to MAXimize this
59
Q

Pearson’s r definition

A
  • correlation coefficient that tells magnitude of relationship between 2 variables (r^2)
  • (interval/ratio) and (interval/ratio)
60
Q

Pearson’s r assumptions

A

interval/ratio and interval/ratio

61
Q

Pearson’s r analysis

A
  • scatter plot

- SPSS: “correlation coefficient”, p value: “Sig. (2-tailed)”

62
Q

Pearson’s r effect size

A

Pearson’s r is the effect size (tells magnitude of relationship) between 2 variables (r^2)

  • small: r ~ .1, r^2 ~ .01 (1% variance accounted for) (( absolute value ))
  • medium: r ~ .3, r^2 ~ .09 (9% variance accounted for) (( absolute value ))
  • large: r ~ .5, r^2 ~ .25 (25% variance accounted for) (( absolute value ))
63
Q

Pearson’s r formula for results section

A

-SPSS: “correlation coefficient”
- p value: “Sig. (2-tailed)
“ (r = ._ _, p = . _ _ _) “

64
Q

Chi-square test of independence definition

A
  • (nominal) and (nominal)

- examines distribution frequencies

65
Q

Chi-square test of independence assumptions

A
  • (nominal) and (nominal)
  • independent groups ( no matching/repeated measures)
  • expected frequency of at least 5 in each cell
  • variables not related to each other
66
Q

Chi-square test of independence analysis

A
  • SPSS effect size: “Phi” in “Symmetric Measures” ( phi-squared ( ϕ^2 ) )
67
Q

Chi-square test of independence effect size

A
  • phi-squared ( ϕ^2 )
  • small: ϕ^2 ~ .1, r^2 ~ .01 (1% variance accounted for)
  • medium: ϕ^2 ~ .3, r^2 ~ .09 (9% variance accounted for)
  • large: ϕ^2 ~ .5, r^2 ~ .25 (25% variance accounted for)
68
Q

Chi-square test of independence formula for results section

A
  • “ X^2(df, N = #) = “Value” under “Pearson Chi-Sq.”, p = ._ _ , ϕ^2 = . _. ”
69
Q

Independent-samples t test definition

A
  • (nominal grouping/dichotomous) and (interval/ratio)
70
Q

Independent-samples t test assumptions

A
  • groups are independent
  • IV (or grouping) nominal grouping/dichotomous
  • DV (or outcome) is interval/ratio
71
Q

Independent-samples t test effect size

A

Cohen’s d (formula given)
- small: d ~ .20
- medium: d ~ .50
- large: d ~ .80
Squared point biserial correlation rpb^2
- gives percentage of variance of outcome (DV) accounted for by predictor (IV)

72
Q

Independent-samples t test formula for results section

A

“ t(df) = t#, p = ._ _ _, d = . _ ”

73
Q

P-value

A
  • less than () .05 : not stat. sig., retain null, chance of Type II error
74
Q

Statistically significant

A
  • when p is less than (>) .05, reject null, chance of Type I error
75
Q

NOT statistically significant

A
  • when p is more than (>) .05 : not stat. sig., retain null, chance of Type II error
76
Q

Homogeneity of variance

A

(A & L, 316)

  • assumption that variance of populations is the same
  • group SDs are estimates of the population variances
77
Q

Levene’s test

A

In independent-samples t test

  • Not Stat. Sig. (p ≥ .05), SECOND line
  • Stat. Sig.(p ≤ .05), FIRST line
78
Q

Effect size definition

A

describes strength/magnitude of IV effect

for simple experiment with independent groups

79
Q

Effect size: r

A

Pearson’s r is the effect size (tells magnitude of relationship) between 2 variables (r^2)

  • small: r ~ .1, r^2 ~ .01 (1% variance accounted for) (( absolute value ))
  • medium: r ~ .3, r^2 ~ .09 (9% variance accounted for) (( absolute value ))
  • large: r ~ .5, r^2 ~ .25 (25% variance accounted for) (( absolute value ))
80
Q

Effect size: phi-squared ( ϕ^2 )

A

Chi-square test of independence

  • small: ϕ^2 ~ .1, r^2 ~ .01 (1% variance accounted for)
  • medium: ϕ^2 ~ .3, r^2 ~ .09 (9% variance accounted for)
  • large: ϕ^2 ~ .5, r^2 ~ .25 (25% variance accounted for)
81
Q

Effect size: Cohen’s d

A

Independent-samples t test
(formula given)
- small: d ~ .20
- medium: d ~ .50
- large: d ~ .80
Squared point biserial correlation rpb^2
- gives percentage of variance of outcome (DV) accounted for by predictor (IV)

82
Q

Quasi-experiment definition

A

1 - NO random assignment
2 - IV manipulated (at least 2 levels)
3 - DV measured

83
Q

Quasi-experiment advantages

A

When unethical to have random assignment (participant age, gender)

84
Q

Multi group (multi level) experiment definition

A
  • IV with 3 or more levels

- DV as usual (measured)

85
Q

Multi group (multi level) experiment advantages compared to many simple experiments

A
  • decreases probability of a Type I error
  • decreases confounding
  • increases efficiency (decreases # of studies and participants)
  • increases internal validity (examines functional relationships, and non-linear/linear)
86
Q 
Multi group (multi level) experiment limitations
weigh advantages and disadvantages with topic
A
  • what are the research questions/hypothesis?
  • what would be the advantages for this study of adding a 3rd condition?
  • what would be the disadvantages?
87
Q

Results section APA

A
  • heading centered, double spaced, indented paragraph
  • M, SD of participants
  • inferential statistic test
  • formula for inferential statistic
  • statistically significant?
88
Q

Discussion section APA

A
  • restate hypothesis
  • statement about meaning/implications of results
  • limitations
  • new directions
89
Q

Multi group (multi level) experiment assumptions

A
  • IV with 3 or more levels

- DV as usual (measured)

90
Q

Simple experiment definition

A
  • IV: manipulated, 2 conditions, nominal

- DV: interval/ratio

91
Q

Simple experiment advantages

A
  • simple (relative to multiple group)

- maybe smaller sample size

92
Q

Simple experiment limitations

A
  • only 2 groups

- non-linear

93
Q

Practical significance

A

(A & L, 209)

- usefulness and everyday impact

PSY 301 RMA (2 decks)

Brainscape helps you reach your goals faster, through stronger study habits.
© 2024 Bold Learning Solutions. Terms and Conditions