Topic Two: Research Methods

Question 1

Variables

Answer 1

Characteristics of process or phenomenon we are interested in, that can vary in quantity or quality.

Question 2

Independent Variables (IV)

Answer 2

Something that we manipulate during an experiment to influence behaviour. Anything that you can SYSTEMATICALLY CHANGE to see the effect on the Dependent Variable. IV is independent of anything else that is going on.

e.g. Time of the exam (9 am, 1 pm)

Question 3

Dependent Variables (DV)

Answer 3

Something that we measure during an experiment to learn about behaviour. Anything that you can MEASURE. The DV depends on what happens with the IV.

e.g. Measuring exam results of the 9 am group and 1 pm group.

Question 4

Extraneous Variables/ Nuisance variables

Answer 4

Something that we do not measure in an experiment but has some influence on the DV. Anything UNWANTED or UNANTICIPATED that influences the DV but in a way not systematically related to IV.

e.g. Whether the exam room was noisy or not.

Question 5

Confounding Variables

Answer 5

Something that has a SYSTEMATIC INFLUENCE on the DV, that is DIFFERENT for EACH GROUP or level of IV.
Influences on the DV are not separable from the one or more groups/levels of IV you want to study.

e.g. Whether the exam room was noisy or not:
9am building work outside exam room
1pm no building work outside exam room
IV different exam times, but the unanticipated noise affecting one group and not the other, will affect DV and ruin experiment.

Question 6

Purpose of Experiments

Answer 6

To produce a statement of CAUSE AND EFFECT backed up by EMPIRICAL EVIDENCE
- the experimental method is the ONLY method to really provide findings about causation.

Question 7

What is the goal when designing an experimental procedure?

Answer 7

When designing an experimental procedure, the goal is to adequately CONTROL EXTRANEOUS & CONFOUNDING VARIABLES so that the effects of IV manipulations on DV can be determined.

Question 8

Direct observation

Answer 8

Variables can be observed and measured directly

Question 9

Indirect observation

Answer 9

Variables indirectly observed (the effects of the variable) using proxies for the behaviour that we want to study.

Question 10

Validity of OD

Answer 10

Measured what it has claimed to measure (a valid measure of what it is meant to measure)

Question 11

Hypothetical constructs vs operational constructs

Answer 11

abstract concept/ feeling vs operationally defined processes to measure

Question 12

Control group

Answer 12

to compare the effect of the environmental group

Question 13

Placebo group

Answer 13

such as sugar pills vs homoeopathic pills

Question 14

Random selection

Answer 14

of participants for research

Question 15

Random allocation

Answer 15

Assigning participants to a group receiving different conditions by chance.

Question 16

Blinding to condition

Answer 16

so that both participants and experimenter don’t know which group each participant has been allocated to

Question 17

Matching of situational variables

Answer 17

e.g. time

Question 18

Matching of subject variables

Answer 18

e.g. age and gender (if random selection is not possible)

Question 19

Blinding to condition (a double-blind experiment is the gold standard)

Answer 19

so that both participants and experimenter don’t know which group each participant has been allocated to

Question 20

Correlation

Answer 20

Linear RELATIONSHIP between 2 variables.

Question 21

No relationship

Answer 21

0 indicates no relationship between the variables

Question 22

Strong relationship

Answer 22

.60 - .79 = a strong relationship

.80 - 1.0 = a very strong relationship

Question 23

Correlation

Answer 23

Linear RELATIONSHIP between 2 variables. NOT causation.

Question 24

No relationship ( | ) or ( - )

Answer 24

0 indicates no relationship between the variables

Question 25

Positive relationship ( / )

Answer 25

If both variables increase or decrease together then this is called a positive correlation.

Question 26

Negative relationship ( \ )

Answer 26

If one variable increases while the other decreases then this is called a negative correlation.

Question 27

Strong relationship

Answer 27

Where the two variables correspond most of the time. .60 - .79 = a strong relationship
.80 - 1.0 = a very strong relationship

Question 28

Weak relationship

Answer 28

Where in a weak correlation the relationship is seldom found.
.00-.19 = a very weak relationship
.20 - .39 = a weak relationship

Question 29

Strength

Answer 29

is how closely the two variables are related to one another. So a strong correlation can be either positive (+) or negative (-). It is about how close the relationship is to either +1 or -1, and nothing to do with the direction.

Question 30

Correlational design

Answer 30

is used when variables are measured but there is no manipulated independent variable.

Question 31

Third Variable Problem

Answer 31

is when there is an additional variable that we either didn’t know about, neglected to measure or control, or accidentally because systematically related to our independent variable or dependent variable.

Question 32

Correlational design

Answer 32

is used when variables are measured but there is no manipulated independent variable. Allows us to make predictions based on the relationship between variables.

Question 33

Longitudinal Study

Answer 33

A study that follows the same group of people over time.

Question 34

Placebo effect (a confounding variable)

Answer 34

When receiving something new or special treatment affects human behaviour.
e.g. participants thinking that they are receiving a new trialled drug, but it is only a sugar pill.

Question 35

Participant demand (a confounding variable)

Answer 35

When participants act in a way they think the experimenter wants them to behave.

Question 36

Experimenter expectations (confounding variable)

Answer 36

When the experimenter’s expectations influence the outcome of a study.
e.g if the experimenter knew who was in the placebo and who was in drug group, they might perceive the pill participants

Question 37

Experimenter expectations (confounding variable)

Answer 37

When the experimenter’s expectations influence the outcome of a study.
e.g if the experimenter knew who was in the placebo and who was in the drug group, they might PERCEIVE the results that are not truly there.

Question 38

Double-blind procedure

Answer 38

Neither the experimenter nor participants know who is in which group.

Question 39

Correlation coefficient

Answer 39

r = provides information about the strength of a relationship between two variables.

Question 40

Qualitative design

Answer 40

methodologies that help us investigate that we cannot manipulate as in experiments.
e.g including participant observation, case studies, and narrative analysis, surveys.

Question 41

Quasi-experimental design

Answer 41

An experiment that does not require random allocation. The existing groups become the independent variable.

Question 42

Surveys

Answer 42

Largely used to gather information for correlational studies. An effective way of reaching a large number of people at a low cost.

Question 43

Four basic types for numerical scale in measuring variables

Answer 43

Nominal scales - naming. To classify objects by assigning labels or numbers to them on the
basis of qualitative differences. Numbers on a nominal scale do not imply quantity. Numbers cannot be added or subtracted

Ordinal scales - rank; named and ordered e.g 1st, 2nd, 3rd in a race but doesn’t tell us any further specific information such as time difference between ranks. Numbers cannot be added or subtracted

Interval scales - named, ordered and has equal intervals between units of measure. e.g celsius scale has equal intervals of measure, numbers can be added and subtracted (but not ratios - multiplied/divided). Zero-point is arbitrary.

Ratio scales - named, ordered, equal intervals and meaningful zero starting point. Can be added/subtracted, multiplied/divided because there is a true zero starting point. e.g. weight, height and time

Question 44

Measures of central tendency

Answer 44

Mean (average; heavily influenced by outliers);

Median (the middle number of all data once in order, not influenced by outliers, useful for ordinal scales),

Mode (most frequently occurring data, useful for data collected on the nominal scale)

Question 45

Measures of variability

Answer 45

Range (the difference between the lowest and highest sets of data);

Standard deviation (is a measure of how spread out the individual scores are around the mean);

Sum of the deviations (above and below it will always cancel out and equal zero);

Variance (sum of the squared deviations);

The square root of the variance is the standard deviation.

Question 46

Bar graph vs histogram

Answer 46

Bar graph - generally used when the scale values represent discrete intervals, can be used for ordinal, interval, ratio and nominal data. Spaces between data sets of data on x-axis.

Histogram is used for continuous scale values. Can be used for ordinal, interval and ratio data, no spaces between data sets on x-axis because data is continuous.

Question 47

Frequency distribution

Answer 47

Upright line in the centre is the Mean; with either end of the range at the edges of the curve

Normal distribution - is a bell-shaped continuous distribution that is symmetrical about the mean.

Question 48

We should be reluctant to conclude that there is a difference in the population groups even though there is some difference between the samples where:

Answer 48

1) the difference in the sample means is small

2) there is a lot of variability in the sample measurements (with a lot of overlap between
the two sample distributions)

3) there is only a small number of sample measurements

Question 49

Statistical inference

Answer 49

allows us to draw a rational conclusion about the actual state of things on the basis of
incomplete information, but in accordance with statistical probabilities.