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Psychology > Quantitative Research Methods > Flashcards

Quantitative Research Methods Flashcards

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1
Q

Evidence based practice

A

Best research evidence
Clinical expertise
Patient characteristics and values

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2
Q

Descriptive statistics

A

Condense a large amount of information into smaller pieces (summary) of information

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3
Q

Inferential statistics

A

Statistical information about a population from a sample of that population with a calculated degree of confidence

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4
Q

Test between different groups

A

T-test
Analysis of variance

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5
Q

Test relationships between variables

A

Correlation
Regression

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6
Q

Compare 2 groups

A

T-test

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7
Q

Compare 2 or more groups

A

ANOVA

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8
Q

Correlation

A

Explore the relationships between pairs of variables

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9
Q

Bivariate regression

A

Predict scores on one variable from scores on another variable

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10
Q

Multiple regression

A

Predict scores on a dependent variable from scores on a number of independent variables

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11
Q

Descriptive statistics

A

Frequencies
Percentages averages

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12
Q

Assumptions in statistics

A

An assumption is a condition that ensures that what you are attempting to do works

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13
Q

Nature of data

A

Continuous or categorical

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14
Q

Categorical data

A

Categories of data are best presented and interpreted with bar graphs

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15
Q

Continuous data

A

Data that can be measured on scale which can interpret median, mode and mean from

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16
Q

Population

A

Collection of units to which we want to generalise a set of findings or a statistical model

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17
Q

Sample

A

A smaller collection of units from a population used to determine truths about that population

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18
Q

The only equation you will ever need

A

Outcome=(model) + Error

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19
Q

Mean

A

The value from which the scores deviate least.

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20
Q

Type 1 error

A

Occurs when we believe that there is a genuine effect in our population when in fact there isn’t

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21
Q

Type II error

A

Occurs when we believe that’s there is no effect In The population when in fact there is

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22
Q

One-way analysis of variance is used when

A

You have only one independent variable (eg. gender)

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23
Q

Two-way analysis of variance is used when

A

You have two independent variables (gender, age group)

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24
Q

Independent variable

A

The proposed cause
A predictor variable
A manipulated variable

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25
Q

Dependent variable

A

The proposed effect
An outcome variable
Measured not manipulated

26
Q

NOIR

A

Nominal
Ordinal
Interval
Ratio

27
Q

Binary variable

A

There are only two categories
Eg. dead or alive

28
Q

Nominal variable

A

There are more than two categories

29
Q

Ordinal variable

A

The same as a nominal variable but the categories have a logical order
Eg. Fail, pass, merit, destinction

30
Q

Interval variable

A

Equal intervals on the variable represent equal differences in the property being measured. Cannot have a 0

31
Q

Ratio variable

A

Similar to interval variable but can have a 0 baseline

32
Q

Categorical variable

A

Binary
Nominal
Ordinal

33
Q

Continuous

A

Interval
Ratio

34
Q

Measurement error

A

The discrepancy between the actual value we’re trying to measure and the number we use to represent that value

35
Q

Validity

A

Whether an instrument measures what it set out to measure

36
Q

Content validity

A

Evidence that the content of a test corresponds to the content of the construct it was designed to cover

37
Q

Ecological validity

A

Evidence that the results of a study, experiment or test can be applied, and allow inferences, to real-world conditions.

38
Q

Reliability

A

The ability of the measure to produce the same results under the same conditions

39
Q

Test-retest reliability

A

The ability of a measure to produce consistent results when the same entities are tested at two different points in time

40
Q

Correlational research

A

Observing what naturally goes on in the world without directly interfering with it

41
Q

Cross-sectional research

A

This term implies that data come from people at different age points with different people representing each age point

42
Q

Experimental research

A

One or more variable is systematically manipulated to see their effect (alone or in combination) on an outcome variable.
Statements can be made about cause and effect.

43
Q

Between-group/between-subject/independent data collection

A

Different entities in experimental conditions

44
Q

Repeated measures (within-subject) data collection

A

The same entities take part in all experimental conditions.
Economical
Practice effects
Fatigue

45
Q

Null hypothesis Ho

A

There is no effect

46
Q

The alternate hypothesis H1

A

Aka the experimental hypothesis

47
Q

Statistical statement format

A

Statistic
Degrees of freedom
Value
Sognificance
Effect size

48
Q

Dependent samples t-test

A

Repeated measures design.
Whether the same group of individuals differ on a particular measure.
Before-After design

49
Q

Analysis of variance (ANOVA

A

Evaluate mean differences between two or more treatments or populations

50
Q

ANOVA key terms

A

Independent Variable= factor
Treatment (condition) of a factor = level

51
Q

ANOVA study with more than one factor

A

Factorial design
2 factors = two-way factorial design
4 factors = four-way factorial design
(Eg. Exercise: yes/no, personality: type A/B, type of meds: Panadol/ibuprofen, education level achieved: university/secondary

52
Q

One-way independent measures ANOVA

A

Independent measures design.
A seperate sample is taken for each level. (Eg. Age groups:suggest ‘mutual exclusivity’)
Repeated measures design: one sample of individuals are in both levels of treatment condition. (One boys heart rate taken once before and once after running a race)

53
Q

ANOVA decides if

A

Differences between the sample means represent real differences between the treatments. That is the treatments really do have different means and the sample data accurately reflects those differences.
There really is no difference between the treatments. The observed differences between samples are due to chance.

54
Q

Statistical hypothesis for ANOVA Ho

A

states there are no differences between the populations represented by the treatments

55
Q

Statistical hypothesis for ANOVA H1

A

The population mean for at least one treatment means is different from others

56
Q

F statistic

A

Simultaneously compares all sample means in a factor to determine whether two or more sample means differ significantly

57
Q

F statistic Formula

A

F= between groups variance
————————————
Within groups variance

58
Q

Between groups variance

A

Two possible explanations:
Treatment (experimental) effect.
Chance.
- individual differences
- experimental error

59
Q

Within treatment variance

A

Cannot occur because of a treatment effect! But can occur because of:
Chance
- individual differences
- experimental error

60
Q

F= treatment effect+ differences due to chance
——————————————————
Differences due to chance

A

When the treatment has no effect, then the differences between treatments (numerator) are entirely due to chance.
If the differences are due to chance, the numerator and the denominator should be approximately equal and the F-ratio should have a value around 1.

61
Q

ANOVA F statistic/formula: when the treatment does have an effect, causing differences between the samples

A

The between treatment differences (numerator) should be larger than the chance (denominator).
A large F-ratio indicates that the differences between treatments are greater than chance.
The treatment does have a significant effect.

62
Q

ANCOVA

A

Tests whether the IV still effects the outcome variable after the influence of the covariants has been removed

Psychology (9 decks)

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