1 chapter 3 research methods. 2 variables hypotheses experimental methods data recording techniques...

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Chapter Chapter 33

Research methodsResearch methods

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Variables Hypotheses Experimental methods Data recording techniques Sampling Experimental design Data analysis-numerical summaries Data analysis-pictorial summaries Data analysis-inferential statistics Data analysis-choosing inferential statistical

tests

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VariablesVariables

VARIABLES

A variable is any object, quality or

event that changes or varies in some

way. Examples include: aggression,

intelligence, time, height, amount of

alcohol, driving ability, attraction.

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VariablesVariables

OPERATIONALLISATION

Many of the variables that psychologists

are interested in are abstract concepts,

such as aggression or intelligence.

Operationalisation refers to the process

of making variables physically

measurable or testable.

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HypothesesHypotheses

Hypotheses are precise, testable

statements. They can be...

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EXPERIMENTAL HYPOTHESESPredict significant differences in the DV

between the various conditions of the IV.

CORRELATIONAL HYPOTHESESPredict significant patterns of

relationship between two or more

variables.

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EXPERIMENTS

the manipulation of the IV to see what

effect it has on the DV

attempt to control the influence of all

other extraneous variables.

Experimental methodsExperimental methods

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Experimental methods involves 3 types:

LABORATORY

the researcher deliberately manipulates the

IV

manipulate strict control over extraneous

variables

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FIELD

The reseacher deliberately manipulates

the IV

but does so in the subject's own natural

environment.

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NATURAL/QUASI

The IV is changed by natural occurrence

the reseacher just records the effect on

the DV

Quasi experiments are any where control

is lacking over the IV

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OBSERVATIONS

the precise measurement of naturally

occuring behaviour in an objective way.

Non-experimental methodsNon-experimental methods

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Non-experimental methods involves 3 types:

NATURALISTIC

the recording of spontaneously occurring

behaviour in the subject's own natural

environment.

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CONTROLLED

the recording of spontaneously occurring

behaviour

but under conditions contrived by the

reseacher.

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PARTICIPANT

the reseacher becomes invoved in the

everyday life of the subjects, either with or

without their knowledge.

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Data recording techniquesBEHAVIOUR SAMPLING METHODSEvent sampling

Key behavioural events are recorded every time they occur.

Time sampling

Behaviour is observed for discrete periods of time.

Point sampling

The behavior of just one individual in a group at a time is recorded.

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Data recording techniquesData recording techniquesFrequency grids

Nominal data is sbored as a tally chart for s variety of behaviouts.

Rating scales

Scores ordinal level data for a behaviour ,indicating the degree to which it is shown.

Timing behaviour

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Data recording equipmentData recording equipment

Hand-written notes or coding systems.

Audio-tape recording.

Video

One way mirrors in laboratories.

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SamplingSampling

SAMPLING

Sampling is the process of selecting

subjects to study from the target

population (a specified section of

humankind).

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Since the results of the study on the

sample will be generalised back to the

target population (through inference），samples should be as representative

(typical) of the target population as

possible.

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Samples should be of a sufficient size

(e.g.30) to represent the veriety of

individuals in a target population,but not

so large as to make the study

uneconomical in terms of time and

resources.

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Types of samplingTypes of sampling : :

RADOMTruly random sampling only occurs when

every member of a target population has an equal chance of being selected.

For example: Putting the names of every member of the

target population into a hat and pulling a sample out (without looking!).

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STRATIFIED

Involves dividing the target population

into important subcategories (or strata)

and then selecting members of these

subcategories in the proportion that they

occur in the target population.For example:

If a target population consisted of 75% women and 25% men, a sample of 20 should include 15 women and 5 men.

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OPPORTUNITY

Opportunity sampling simply involves

selecting those subjects that are around

and available at the time.An effort may

be made to not be biased in selecting

particular types of subject.

For example:

University psychologists may sample

from their students.

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SELF-SELECTING

Self-selecting samples consist of those

individuals who have consciously or

unconsciously determined their own

involvement in a study.

For example:

Volunteers for studies or passers by who

become involved in field studies, i,e.in

bystander intervention studies.

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Data analysis- numerical Data analysis- numerical summariessummaries

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NOMINAL

Nominal data is a simple frequency headcount (the number of times something occurred) found in discrete categories (something can only belong to one category) .

For example, the number of people who helped or did not help in an emergency.

Nominal data is the simplest data.

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ORDINAL

Ordinal data is measurements that can be put in an order, rank or position.

For example, scores on unstandardised psychological scales (such as attractiveness out of 10) or who came 1st, 2nd, 3rd,etc.in a race.

The intervals between each rank, however, are unknown ,i.e. how far ahead 1st was from 2nd.

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INTERVAL AND RATIO

Both are measurements on a scale, the intervals

of which are known and equal. Ratio data has a true zero point, whereas interval data can go into negative values.

For example, temperature for interval data (degrees centigrade can be minus) length or time for ratio data (no seconds is no time at all) .

The most precise types of data.

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MODE The value or event that occurs the most

frequently. The most suitable measure of central tendency

for nominal data. Not influenced by extreme scores; useful to

show most popular value. Crude measure of central tendency; not useful

if many equal modes.

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MEDIAN

The middle value when all scores are placed in rank order. The most suitable measure of central tendency for ordinal data.

Not distorted by extreme freak values, e.g.2,3,3,4,4,4,4,4,5,5,6,42.

However, it can be distorted by small samples and is less sensitive.

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MEAN

The average value of all scores. The most

suitable measure of central tendency for interval or ratio data.

The most sensitive measure of central tendency

for all data. However, can be distorted by extreme freak values.

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RANGE

The differenced between the smallest and largest value, plus 1.

For example,3,4,7,7,8,9,12,4,17,17,18 (18-3)+1=Range of 16

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SEMI-INTERQUARTILE RANGE

When data is put in order, find the first quartile (Q1) and third quartile (Q3) of the Q1 value from the Q3 value and divide the result by two.

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STANDARD DEVIATION

The average amount all scores deviate from the mean.

The difference (deviation) between each score is calculated and then squared (to remove minus values).

These squared deviations are then added up and their mean calculated to give a value known as the variance.

The square root of the variance gives the standard deviation of the scores.

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STANDARD DEVIATIONSTANDARD DEVIATION

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Data analysis-pictorial Data analysis-pictorial summariessummaries

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BAR CHARTS

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FREQUENCY POLYGON

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PIE CHARTS

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SCATTERGRAMS

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NORMAL DISTRIBUTION

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Data analysis-inferential statisticsData analysis-inferential statistics

Definition : A significant result is one where there is a low probability that chance factors were responsible for any observed difference, correlation or association in the variables tested.

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