Chapter 3 Research methods

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

Variables 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.

Variables 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.

Hypotheses Hypotheses are precise, testable statements. They can be...

EXPERIMENTAL HYPOTHESES Predict significant differences in the DV between the various conditions of the IV. CORRELATIONAL HYPOTHESES Predict significant patterns of relationship between two or more variables.

Experimental methods 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 methods involves 3 types: LABORATORY the researcher deliberately manipulates the IV manipulate strict control over extraneous variables

FIELD The reseacher deliberately manipulates the IV but does so in the subject's own natural environment.

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

Non-experimental methods OBSERVATIONS the precise measurement of naturally occuring behaviour in an objective way.

Non-experimental methods involves 3 types: NATURALISTIC the recording of spontaneously occurring behaviour in the subject's own natural environment.

CONTROLLED the recording of spontaneously occurring behaviour but under conditions contrived by the reseacher.

PARTICIPANT the reseacher becomes invoved in the everyday life of the subjects, either with or without their knowledge.

Data recording techniques BEHAVIOUR SAMPLING METHODS Event 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.

Data recording techniques Frequency 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

Data recording equipment Hand-written notes or coding systems. Audio-tape recording. Video One way mirrors in laboratories.

Sampling SAMPLING Sampling is the process of selecting subjects to study from the target population (a specified section of humankind).

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.

Samples should be of a sufficient size (e. g 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.

Types of sampling : RADOM Truly 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!).

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.

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.

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.

Data analysis- numerical summaries

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.

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.

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.

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.

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.

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.

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

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.

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.

STANDARD DEVIATION

Data analysis-pictorial summaries

BAR CHARTS

FREQUENCY POLYGON

PIE CHARTS

SCATTERGRAMS

NORMAL DISTRIBUTION

Data 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.