Brainscape's Knowledge GenomeTM


Top Flashcards (Certified)
All Flashcards

Psychology > Quantitative Research Methods Sem 1 Yr 2 > Flashcards

Quantitative Research Methods Sem 1 Yr 2 Flashcards

1
Q

What are the 4 key principles of ethics?

A

Respect
Scientific Integrity
Social Responsibility
Maximise benefit and minimise harm

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

What is Primary Research?

A

Requires participant permission
Self conducted

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

What is Secondary Research?

A

Participant permission is not required
New knowledge/theories through the use of existing knowledge (literature review)
Conducted by others

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

What are some important ethical considerations?

A

Privacy and confidentiality
Respect
Power dynamics
Informed consent
Appropriate use of knowledge and skills
Don’t cause upset, harm or distress in any way
No conflicts of interest
No deception
Boundaries are maintained
Needs of vulnerable groups

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

What do we include when writing an ethics application or writing up a report?

A

DESIGN - Clear aim, questions you will be asking for ethical appraisal, unbiased
MATERIALS - How long will participation take? Where will you conduct research? What materials will be used? How will you interpret the scores of materials?
PARTICIPANTS - Number, age range(or mean), gender. Inclusions and exclusion criteria, recruitment and selection details. Incentives? PIS and consent form. Is any deception involved? Withdrawal procedures
DATA - Participants’ confidentiality, how you will store data, Length of storage, follow GDPR regulations
RISK ASSESSMENTS - Identify potential risks and how you would address them, details of relevant support services in PIS, complete risk assessment documents

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

What should be included in Participant Information Sheet?

A
  • Purpose of project
  • Why they are being invited to take part
  • What will happen
  • Benefits, disadvantages, and risks
  • Data treatment and storage
  • Withdrawal information
  • Who has reviewed the project
  • Contact details of the researcher
  • Contact details of support services
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

What are the three main steps in the research process?

A
  1. Prediction
  2. Design
  3. Analysis
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

What is a correlational design?

A

Exploring the strength and direction of a relationship between two variables
Observes ‘natural’ events

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

What is an experimental design?

A

Comparing two conditions (within-subject design) or two groups (between-subject design)
Controlled comparison of situations
Identifying a cause and effect

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

What statistical test do we use for correlational parametric data?

A

Pearsons

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

What assumptions are met if we are doing a Spearman test?

A

Correlational, non-parametric

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

What statistical test is used for between-subjects (independent) experimental designs?

A

Independent samples t-test

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

What are the assumptions if we are performing a paired samples t-test?

A

Experiment design
Within-group (repeated) design

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

What are the two types of regression?

A

Linear regression
Multiple regression

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

What is a regression design?

A

One or more predictor variable to explain variability on outcome variable

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

What is systematic variation?

A

Variability in scores is as a result of experimental manipulation
Desirable

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
17
Q

What is unsytematic variation?

A

Variability in scores is due to random or uncontrollable factors
Undesirable

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
18
Q

General Linear Model

A

Outcome = Model + Error

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
19
Q

How can analysis be done of the General Linear Model?

A

Linear/Multiple regression
ANOVA

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
20
Q

What does ANOVA mean?

A

Analysis Of Variance

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

What does partitioning the variance mean?

A

Separating systematic and unsystematic variance

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
22
Q

General Linear Model: Regression

A

Outcome variable = Predictor variables + Unexplained variance

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
23
Q

General Linear Model: ANOVA

A

DV Scores = Experimental conditions + Unexplained variance

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

What is a key assumption of parametric data?

A

Normal distribution of data

25
How can we assess normality?
Using a histogram Use a statistical test of normality such as the Shapiro-Wilk test Use a Q-Q plot
26
What does it mean if p<0.005 in terms of normality?
Data are different from normal distribution of data
27
What are box plots useful for?
Exploring outlying data points
28
What is a correlation coefficient?
Numbers that summarise the relationship on a scattergram
29
True or False? Correlations assume a linear relationship between variables
True
30
How do your report correlation?
The r value, degrees of freedom, and the p value Example - r(7) = 0.581, p = 0.034
31
How do we know if p is statistically significant in correlation?
p < 0.05
32
What are the assumptions of regression?
There is a continuous outcome variable (DV) There is normal distribution Linearity between the dependent and independent variables
33
What is the Green (1991) Rule of thumb?
Minimum sample size should be 50+8k Where k is the number of predictor variables
34
What is Variance according to Field (2018)?
Average error between the mean and the observations made
35
How should regression variance be reported?
Reported as the units measured squared Therefore, it should be square rooted
36
What is the shared variance between two variables?
The coefficient squared aka the coefficient of determination e.g. 0.30'2 = 0.09
37
What does the square of the coefficient correlation mean?
The relationship between the predictor variable and the outcome variable
38
What does b0 mean in simple regression?
The value of Y when X is 0
39
What does Ŷ represent in simple regression?
The dependent variable
40
What letter represents an error in the model?
ei
41
What does r squared represent?
The regression coefficient
42
What does the regression coefficient tell us?
Tells us how well the variance has been explained
43
What letter represents participants' scores in simple regression?
xi
44
In the simple regression formula, what letter represents the regression coefficient?
b1
45
What is the simple regression model formula?
Ŷi = (b0 + b1 xi ) +ei
46
What is the definition of multiple regression?
The contribution of several independent variables in predicting the dependent variable
47
What can we use to best explore the following? 1. How well a set of variables is able to predict an outcome 2. Which variable in a set of variables is the best single predictor of the outcome 3. Whether a predictor variable still predicts when controlling for a different variable
Multiple regression
48
What is another word for shared variance in multiple regression?
Multicollinearity
49
Is it more or less useful for two or more predictor variables to have more shared variance in predicting the DV?
Less useful because it is difficult to assess their unique contribution
50
Is it more or less useful for two or more predictor variables to have more shared variance in predicting the DV?
Less useful because it is difficult to assess their unique contribution
51
Is it true or false that we use ANOVA for hierarchal regression?
False, Hierarchal regression is entry method or forced entry and ANOVA is model comparison
52
What is good about the entry method with hierarchal regression?
It is based on theory testing Look at the unique influence predictor variables have on the outcome variable
53
What is bad about the entry method with hierarchal regression?
It has a strong subjective component
54
What is the entry method with hierarchal regression?
Predictor are entered in a separate block
55
What is the forced method with hierarchal regression?
All variables are entered into the model simultaneously
56
How do we see homoscedacity?
Plot ZRESID (Standardised residuals) against ZPRED (standardised predictor)
57
What is the term for normality of errors?
Residuals
58
Should tolerance be more or less than 0.2? (Menard,1995)
More than

Psychology (6 decks)

Brainscape helps you reach your goals faster, through stronger study habits.
© 2025 Bold Learning Solutions. Terms and Conditions