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Unit 3 & 4Research methods

VCAA Study Design Dot PointsExperimental research: construction of research hypotheses; identification and operationalization of independent and dependent variable; identification of extraneous and potential confounding variables including individual participant differences, non-standardised instructions and procedures, order effects, experimenter effect, placebo effects; ways of minimising confounding and extraneous variables including type of sampling procedures, type of experiment, counterbalancing, single and double blind procedures, placebos, standardised instructions and procedures; evaluation of different types of experimental designs including independent-groups, matched-participants, repeated measures; reporting conventions as per American Psychological Association (APA) format

Sampling procedures in selection and allocation of participants: random sampling; stratified sampling; random-stratified sampling; convenience sampling; random allocation of participants to groups; control and experimental groups

Techniques of qualitative and quantitative data collection: case studies; observational studies; self-reports; questionnaires

Statistics: measure of central tendency including mean, median and mode; interpretation of p-values and conclusions; evaluation of research in terms of generalising findings to the populationEthical principles and professional conduct: the role of the experimenter, protection and security of participants' rights; confidentiality; voluntary participation; withdrawal rights; informed consent procedures; use of deception in research; debriefing

Research HypothesisTestable prediction of the causal relationship between two variables

States how the independent variable will affect the dependent variable and outlines the population from which the sample has been selected.

IV

DVThe effect ofonThis is altered by the experimenter and only applied to the experimental group. (cause)

This changes as a result of the IV and is always whatever you are measuring.(effect)VariablesIndependent variableDependent variable

Extraneous variables Any variable other than the independent variable that can cause a change in the dependent variable and therefore affect the results in an unwanted way.

Makes it difficult to determine if any change in the DV was caused by only the IV

Includes participant, experimenter and environmental characteristics

Confounding variables Any variable other than the IV that has an unwanted effect on the DV making it impossible to determine which variable has caused the change

Differs from an extraneous because it produces a measurable change in the IV consistent with what was predicted in the hypothesis whereas an extraneous may or may not affect the DV

Are built in to the experimental design

E.g. If conducting a study on the effect of a drug on test performance but dont control for pre-existing differences in participant intelligence then this would potentially confound results.

Types of extraneous/confounding variablesIndividual participant differences

Demand characteristics

Placebo effects

Order-effects

Artificiality

Use of non-standardised instructions and procedures

Individual participant differencesDifferences in personal characteristics and experiences of individuals e.g. Age/sex/intelligence/personality/memory/physical health/motivation/emotional state etc

Can affect how a participant responds in an experiment

Try and control/minimise the influence of these variables prior to experiment (repeated measures/matched participants designs)

Order Effects

often occurs in the repeated measures designperformance in the second task may increase/improve because of experience gained by the first taskcan be controlled by counter-balancing

Types of order effects:Practise effects: performance influenced because youve had practise at the taskBoredom effects: if task is long/repetitive/not interesting may not perform as well as possible because of boredomFatigue effects: performance may get worse because theyre tiredCarry-over effects: influence a particular treatment or task has on performance on a subsequent treatment or task

Counter-balancingorder in which conditions of a repeated measures design are arranged so that each condition occurs equally often in that positionDone to counter the unwanted effects on performance of any one order

Between- participants counter-balancingInvolves counterbalancing the order in which the groups of participants are exposed to the experimental conditions

Within-participants counter-balancingRequires each participant be exposed to the same combination of conditionsE.g. All the treatment conditions in one order; then the treatment conditions again in the reverse order* Impact is balanced out over the entire experiment

Demand characteristicsDemand characteristics are cues expressed in the environment that communicates the kind of response that is expected from participants and leads them to believe that the research requires they respond in a particular way.

Participants dont necessarily respond to demand characteristics intentionally

E.g. If a researcher puts biscuits in front of a group on a table and said Normal people crave biscuits at this time of the day, so eat if you want to more likely to eat.

Artificiality Laboratory based research often lacks realism and is different to real-life settings. The artificiality of the environment can produce demand characteristics that cause participants to react unnaturally.

e.g. In a sleep lab, would you sleep the same way in a strange bed as you would at home in your room/bed.

Can limit generalisability of results from lab to real-life contexts

Use of non-standardised instructions and procedures. Non-standardised instructions/procedures means they are not the same for all participants

If instructions/procedures are not standardised then they are not controlling for variables

Procedures involve participant selection/use of materials/data collection and recording etc

Placebo effectOccurs when there is a change in the response of participants due to their belief that they are receiving some kind of experimental treatment as opposed to the experimental treatment

This can be controlled for by using:A single/double blind procedureBy using placebos (fake treatments) so that the control group does everything the experimental group does

Experimenter EffectOccurs when there is a change in a participants response due to the experimenters actions rather than to the effect of the IVCan be controlled by using a single/double-blind procedure

Experimenter expectancy: cues the researcher provides about the responses participants should give in the experiment.

Self-fulfilling prophecy: tendency of participants to behave in accordance with how they believe an experimenter wants them to behaveCan be promoted by experimenters facial expressions, mannerisms, tone of voice etc.

Experimenter bias: unintentional bias in collection of data. If experimenter is aware of the purpose/hypothesis of the experiment, its possible for them to misinterpret responses or give unintentional assistance.

Single-blind proceduresParticipants are unaware of which condition they have been allocated toORExperimenter is unaware of which condition participants have been allocated to

Eliminates either participant expectations OR experimenter expectations

Double-blind procedures Neither the participants or the experimenters are aware of which condition the participants have been allocated to

Controls for participant and experimenter expectations

Single-blind Vs Double-blindSimilarityParticipants in both procedures are unaware of the particular condition in which they have been allocated

DifferenceIn a single-blind procedure, only EITHER the experimenter or participants are aware of the conditions to which participants have been allocated whereas in the double-blind both the participant AND experimenter is unaware

Which is more advantageous?- Double-blind as it controls for experimenter bias or expectancy in measuring the DV

Participant Selection & Allocation Sample Population Random sampling Stratified sampling Stratified random sampling Convenience sampling Control groups Experimental groups Random allocation

Sample Vs PopulationSamplePopulationA group that is a portion of a larger group chosen to be studied for research purposes

A sample should be representative of the populationIs the entire group of research interest

Sampling ProceduresRandom sampling- every member of the population has an equal chance of being selected as a participant for a studye.g. Put everyone's names in a hat & pull out required number

2) Stratified sampling- the population is divided into subgroups (strata) and then a sample is selected from each stratum in the same proportions as they exist in the research population

Sampling Procedures3) Stratified random sampling- the population is divided into subgroups (strata) - a random sample is selected from each stratum in the proportion in which they occur in the population

4) Convenience sampling- selecting a sample from the population based on factors such as cost, time, accessibility etc- not everyone in the population has an equal chance of being selected

Why is random sampling the preferred method of sampling?Because it is more likely that a sample gained in this way will:

Be representative of the population

Have participant variables distributed in the sample in the same proportion as they exist in the population

Control Vs Experimental GroupsControl groupExperimental group/sIs not exposed to the independent variable (IV)

Is used as a baseline for comparison with experimental groups in order to determine if the IV has caused some change in the DVIs exposed to the independent variable (IV)

Can have numerous experimental groups

Random allocationEvery participant selected for the experiment has an equal chance of being selected for any of the groups used (control/experimental)

Research Designs & ProceduresIndependent Groups DesignRepeated Measures DesignMatched Participants Design

Research DesignsIndependent groupsRepeated measures(within subjects)Matched Participants(between subjects)Key FeaturesParticipants are randomly allocated to different groups each group is assigned to only 1 condition (experimental/control)Each participant completes all experimental conditionsParticipants are paired/grouped on relevant characteristics and then each member is allocated to different conditions each participant completes only one conditionBenefitsNo order effects

no pre-testing required

Experiment not over a long time period fewer drop outsElimination of participant related variables ability to use fewer participants than IG no pre-testing requiredNeeds fewer subjects than IGControls for participant variables No order effects Experiment not over long time periodLimitationsNeed more participants for strength of results

participant variables arent controlledOrder effect Boredom effect may be fatigued/bored by the time they do second task and wont perform as wellTime & expense required to collect info through pre-testing If one participant drops off then both in the pair are lost to data pool

Cross-sectional research designsData is collected at one time from participants of all ages and different groups are compared

Strengths:All data collected at once and readily availableCheaper and less time consuming than longitudinal studiesLess chance of participants dropping out of the study

Weakness:- Large numbers of participants needed

Longitudinal research designsThe same participants are investigated over a period of time

Strengths: Less interference from personal characteristicsIn studies of progressive mental health conditions such as Alzheimers, this type of study is the only means of investigating how disease progresses

Weaknesses:Time consumingParticipant drop out likely

Collecting the dataTypes of dataMethod of data collection

Qualitative vs. Quantitative dataQualitative: description of characteristics of what is being studied e.g. Emotional state (happy/sad)

Quantitative: refers to measurements (numerical info about the variables being studied)- allows more precise and detailed analysis of results through statistical procedures

Objective vs. Subjective dataObjective: information based on measurement of participant responses. - each person using an objective measure correctly will obtain the same result

Subjective: are based on opinion and largely based on self-reports given by participants- Often info cant be verified by the researcher

Data collection techniquesCase studySelf-reports- Interviews- QuestionnariesObservational studiesNaturalistic observationNon-participant observationParticipant observation

Case studyIn depth study of an individual group or event

Strengths: Detailed information is collectedCan be used to create research hypotheses

Weaknesses:Time consumingCannot be generalised (until confirmed by experimental research)

Self-reportsParticipants written or spoken responses to questions, statements or instructions presented by the researcher.

Types include interviews and questionnaires

Strengths:Can collect a large amount of data from a large number of people in a short amount of timeCan gain data on sensitive topics because of anonymityUseful for collecting qualitative & quantitative data

Weaknesses:Rely on assumption participants will answer all questions and will answer honestlyOften participants give socially desirable answersSubjective data (difficult to verify by researchers)Depending on types of questions, answers can be restricted

InterviewsInvolve interaction between the participant and experimenter

Structured interview: Participants are asked a set of pre-determined questions with a fixed choice of responses (yes/no/always/often/sometimes etc)Used to ensure all participants are treated in the same way & to avoid demand characteristicsEasier to analyses and compare data across participants but less detailed data is obtained

Unstructured interview: researcher has an overall aim of what data should be collected but questions asked can vary widely from participant to participant- gets more detailed data but harder to analyse (relies on objectivity of researcher)

QuestionnairesMethod of collecting written responses from participants

Could be surveys or likert-type scales

Strengths: Easy to replicate & scoreProvides a means of quantifying subjective data

Weaknesses: May be open to bias if a participant is trying to appear socially desirableCould be difficult to analyse data if open-ended questions are used

Observational studyInvolves collection of data by carefully watching and recording behaviour as it occurs

Types of observational studiesNaturalistic observations: Observation of voluntary behaviours occurring within the subjects natural environment by a researcherControlled observations: observations of voluntary behaviours within a structured environment (such as a lab)

Non-participant observation: when researchers try to conceal their presence when making observationsParticipant observation: when the researcher is an active member of the group being observed

Analysing & Interpreting data Experiments Correlations Descriptive StatisticsInferential Statistics

ExperimentsIs used to find out if there is a cause-effect relationship between behaviours or events of interest

E.g. If number of trial exams completed improves exam performance

In an experiment, the researcher manipulates the way in which a behaviour of event (IV) occurs in order to test a predicted event on another behaviour or event of interest (DV)

Correlation studiesDescribe the strength of relationship between 2 variables - Positive correlation = both variables increase or decrease at the same time- Negative correlation = indicates that as one variable increases, the other decreases

Correlation-coefficient is a measure of the strength No correlation correlation-coefficient = 0.00Strong positive correlation = +1.00Strong negative correlation = -1.00

Difference between Descriptive & Inferential StatisticsDescriptiveInferentialOnly gives information about the nature of the data set

Enables organisation of dataEnables:Testing of hypothesesDetermining statistical significanceDrawing conclusions from resultsGeneralisations of findings to population

Types of descriptive statisticsMeasures of central tendency

Mean: - average of all scores in a set of scoresCalculated by adding all scores together then dividing by total number of individual scoresIs accurate when all scores cluster around a central score; misleading if data is widely spreade.g. 2, 7, 3, 10, 15 (mean = 7.4)

Types of descriptive statisticsMeasures of central tendency

Median: middle score of a set of scores - when there is an even number of scores, median is average of two middle scoresUseful when theres limited data and when there is extreme scores as its not affected by these

Types of descriptive statisticsMeasures of central tendency

Mode: - most frequently occurring score in a data setUsed infrequently because its not representative of a complete set of data

* So which is the preferred measure of central tendency???

Experimental GroupControl Group

- Effect of drugs on Driving performance70204520Do drugs affect driving performance?

28202320Do drugs affect driving performance?

Do drugs affect driving performance?

Do drugs affect driving performance?

A test of statistical significance will allow us to discover whether the difference between the control and experimental groups was due to the IV (drugs) or chance factors such as extraneous variables etc.Inferential Statistics Statistical Significance

46See Study guide sheets given for examples.

A p-value is the probability of the difference between two averages being due to chance factors rather than the effect of the IV.

Psychology will allow either: 5% (p0.05) 1% (p0.01) 0.1% (p0.001)

You will be given a p-value on the exam and you need to compare it to the following statements:- if p0.05 then there is a significant difference- if p0.01 then there is a significant difference- if p0.001 then there is a significant difference

You then need to say what this means in plain English

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p valuesif p0.05 then there is a statistically significant differenceThere is less than a 5 in 100 (or 5%) chance that the difference between groups was due to chance alone and not the IV

if p0.01 then there is a statistically significant differenceThere is less than a 1 in 100 (1%) chance that the difference between groups was due to chance alone and not the IV

if p0.001 then there is a statistically significant difference- There is a less than 1 in 1000 (0.1%) chance that the difference between groups was due to chance alone and not the IV

p valuesif p0.05 then there is no significant differenceThere is more than a 5 in 100 (or 5%) chance that the difference between groups was due to chance alone and not the IV

if p 0.01 then there is no significant differenceThere is more than a 1 in 100 (1%) chance that the difference between groups was due to chance alone and not the IV

if p 0.001 then there is no significant difference- There is more than 1 in 1000 (0.1%) chance that the difference between groups was due to chance alone and not the IV

Quality of ResearchReliabilityValidity

ReliabilityRefers to the consistency, dependability and stability of results obtained over time.

E.g. Every time you test using the same device, you would expect the same/similar result.

Internal consistencyRefers to the interrelatedness of questions in a psychological test in measuring the same ability or traitHigh score means that all the rest items relate to or assess the same psychological characteristic

ValidityMeans that the research study has produced results that accurately measure the behaviour or event that it claims to have measured.

A measure can be reliable even if its not valid, but cannot be valid unless its reliable.

Conclusions & Generalisations

ConclusionsA decision or judgement about what the results obtained from research means

The following factors need to be considered when deciding whether a conclusion can be made- whether results support the hypothesis or not- extraneous variables - statistical significance (if its not significant no conclusion can be drawn)

* When drawing a conclusion, you must be confident that any change in the dependent variable was due to the independent variable and not other variables

GeneralisationsA decision about whether the findings of a study can be applied to other members of the population from which the sample was drawn.

Whether the results can be generalised depends on:- sample size- whether the sample is representative of the population- the possible impact of extraneous variables

Ethics