LESSON ONE: TYPES OF EXPERIMENTS AIMS AND HYPOTHESES Research methods.
1 Chapter 3 Research methods. 2 Variables Hypotheses Experimental methods Data recording techniques...
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Transcript of 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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