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Psychology > Quantitative Research Methods Sem 1 Yr 2 > Flashcards

Quantitative Research Methods Sem 1 Yr 2 Flashcards

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1
Q

What are the 4 key principles of ethics?

A

Respect
Scientific Integrity
Social Responsibility
Maximise benefit and minimise harm

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2
Q

What is Primary Research?

A

Requires participant permission
Self conducted

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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

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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

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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

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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
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7
Q

What are the three main steps in the research process?

A
  1. Prediction
  2. Design
  3. Analysis
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8
Q

What is a correlational design?

A

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

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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

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10
Q

What statistical test do we use for correlational parametric data?

A

Pearsons

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11
Q

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

A

Correlational, non-parametric

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12
Q

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

A

Independent samples t-test

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13
Q

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

A

Experiment design
Within-group (repeated) design

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14
Q

What are the two types of regression?

A

Linear regression
Multiple regression

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15
Q

What is a regression design?

A

One or more predictor variable to explain variability on outcome variable

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16
Q

What is systematic variation?

A

Variability in scores is as a result of experimental manipulation
Desirable

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17
Q

What is unsytematic variation?

A

Variability in scores is due to random or uncontrollable factors
Undesirable

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18
Q

General Linear Model

A

Outcome = Model + Error

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19
Q

How can analysis be done of the General Linear Model?

A

Linear/Multiple regression
ANOVA

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20
Q

What does ANOVA mean?

A

Analysis Of Variance

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21
Q

What does partitioning the variance mean?

A

Separating systematic and unsystematic variance

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22
Q

General Linear Model: Regression

A

Outcome variable = Predictor variables + Unexplained variance

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23
Q

General Linear Model: ANOVA

A

DV Scores = Experimental conditions + Unexplained variance

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24
Q

What is a key assumption of parametric data?

A

Normal distribution of data

25
Q

How can we assess normality?

A

Using a histogram
Use a statistical test of normality such as the Shapiro-Wilk test
Use a Q-Q plot

26
Q

What does it mean if p<0.005 in terms of normality?

A

Data are different from normal distribution of data

27
Q

What are box plots useful for?

A

Exploring outlying data points

28
Q

What is a correlation coefficient?

A

Numbers that summarise the relationship on a scattergram

29
Q

True or False? Correlations assume a linear relationship between variables

A

True

30
Q

How do your report correlation?

A

The r value, degrees of freedom, and the p value
Example - r(7) = 0.581, p = 0.034

31
Q

How do we know if p is statistically significant in correlation?

A

p < 0.05

32
Q

What are the assumptions of regression?

A

There is a continuous outcome variable (DV)
There is normal distribution
Linearity between the dependent and independent variables

33
Q

What is the Green (1991) Rule of thumb?

A

Minimum sample size should be 50+8k
Where k is the number of predictor variables

34
Q

What is Variance according to Field (2018)?

A

Average error between the mean and the observations made

35
Q

How should regression variance be reported?

A

Reported as the units measured squared
Therefore, it should be square rooted

36
Q

What is the shared variance between two variables?

A

The coefficient squared aka the coefficient of determination
e.g. 0.30’2 = 0.09

37
Q

What does the square of the coefficient correlation mean?

A

The relationship between the predictor variable and the outcome variable

38
Q

What does b0 mean in simple regression?

A

The value of Y when X is 0

39
Q

What does Ŷ represent in simple regression?

A

The dependent variable

40
Q

What letter represents an error in the model?

A

ei

41
Q

What does r squared represent?

A

The regression coefficient

42
Q

What does the regression coefficient tell us?

A

Tells us how well the variance has been explained

43
Q

What letter represents participants’ scores in simple regression?

A

xi

44
Q

In the simple regression formula, what letter represents the regression coefficient?

A

b1

45
Q

What is the simple regression model formula?

A

Ŷi = (b0 + b1 xi ) +ei

46
Q

What is the definition of multiple regression?

A

The contribution of several independent variables in predicting the dependent variable

47
Q

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

A

Multiple regression

48
Q

What is another word for shared variance in multiple regression?

A

Multicollinearity

49
Q

Is it more or less useful for two or more predictor variables to have more shared variance in predicting the DV?

A

Less useful because it is difficult to assess their unique contribution

50
Q

Is it more or less useful for two or more predictor variables to have more shared variance in predicting the DV?

A

Less useful because it is difficult to assess their unique contribution

51
Q

Is it true or false that we use ANOVA for hierarchal regression?

A

False, Hierarchal regression is entry method or forced entry and ANOVA is model comparison

52
Q

What is good about the entry method with hierarchal regression?

A

It is based on theory testing
Look at the unique influence predictor variables have on the outcome variable

53
Q

What is bad about the entry method with hierarchal regression?

A

It has a strong subjective component

54
Q

What is the entry method with hierarchal regression?

A

Predictor are entered in a separate block

55
Q

What is the forced method with hierarchal regression?

A

All variables are entered into the model simultaneously

56
Q

How do we see homoscedacity?

A

Plot ZRESID (Standardised residuals) against ZPRED (standardised predictor)

57
Q

What is the term for normality of errors?

A

Residuals

58
Q

Should tolerance be more or less than 0.2? (Menard,1995)

A

More than

Psychology (6 decks)

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