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Quantitative Research Methods Flashcards

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
Dependent variable
The proposed effect An outcome variable Measured not manipulated
26
NOIR
Nominal Ordinal Interval Ratio
27
Binary variable
There are only two categories Eg. dead or alive
28
Nominal variable
There are more than two categories
29
Ordinal variable
The same as a nominal variable but the categories have a logical order Eg. Fail, pass, merit, destinction
30
Interval variable
Equal intervals on the variable represent equal differences in the property being measured. Cannot have a 0
31
Ratio variable
Similar to interval variable but can have a 0 baseline
32
Categorical variable
Binary Nominal Ordinal
33
Continuous
Interval Ratio
34
Measurement error
The discrepancy between the actual value we’re trying to measure and the number we use to represent that value
35
Validity
Whether an instrument measures what it set out to measure
36
Content validity
Evidence that the content of a test corresponds to the content of the construct it was designed to cover
37
Ecological validity
Evidence that the results of a study, experiment or test can be applied, and allow inferences, to real-world conditions.
38
Reliability
The ability of the measure to produce the same results under the same conditions
39
Test-retest reliability
The ability of a measure to produce consistent results when the same entities are tested at two different points in time
40
Correlational research
Observing what naturally goes on in the world without directly interfering with it
41
Cross-sectional research
This term implies that data come from people at different age points with different people representing each age point
42
Experimental research
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
Between-group/between-subject/independent data collection
Different entities in experimental conditions
44
Repeated measures (within-subject) data collection
The same entities take part in all experimental conditions. Economical Practice effects Fatigue
45
Null hypothesis Ho
There is no effect
46
The alternate hypothesis H1
Aka the experimental hypothesis
47
Statistical statement format
Statistic Degrees of freedom Value Sognificance Effect size
48
Dependent samples t-test
Repeated measures design. Whether the same group of individuals differ on a particular measure. Before-After design
49
Analysis of variance (ANOVA
Evaluate mean differences between two or more treatments or populations
50
ANOVA key terms
Independent Variable= factor Treatment (condition) of a factor = level
51
ANOVA study with more than one factor
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
One-way independent measures ANOVA
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
ANOVA decides if
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
Statistical hypothesis for ANOVA Ho
states there are no differences between the populations represented by the treatments
55
Statistical hypothesis for ANOVA H1
The population mean for at least one treatment means is different from others
56
F statistic
Simultaneously compares all sample means in a factor to determine whether two or more sample means differ significantly
57
F statistic Formula
F= between groups variance ———————————— Within groups variance
58
Between groups variance
Two possible explanations: Treatment (experimental) effect. Chance. - individual differences - experimental error
59
Within treatment variance
Cannot occur because of a treatment effect! But can occur because of: Chance - individual differences - experimental error
60
F= treatment effect+ differences due to chance —————————————————— Differences due to chance
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
ANOVA F statistic/formula: when the treatment does have an effect, causing differences between the samples
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
ANCOVA
Tests whether the IV still effects the outcome variable after the influence of the covariants has been removed

Psychology (10 decks)

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