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Research methods revision 2015

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- 1. VCAA Study Design Dot Points Experimental 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 population Ethical 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
- 2. Research Hypothesis Testable 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.
- 3. IV DVThe effect of on This 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) Variables Independent variable Dependent variable
- 4. 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
- 5. 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 don’t control for pre-existing differences in participant intelligence then this would potentially confound results.
- 6. Types of extraneous/confounding variables Individual participant differences Demand characteristics Placebo effects Order-effects Artificiality Use of non-standardised instructions and procedures
- 7. Individual participant differences Differences 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)
- 8. Order Effects often occurs in the repeated measures design performance in the second task may increase/improve because of experience gained by the first task can be controlled by counter-balancing Types of order effects: 1) Practise effects: performance influenced because you’ve had practise at the task 2) Boredom effects: if task is long/repetitive/not interesting may not perform as well as possible because of boredom 3) Fatigue effects: performance may get worse because they’re tired 4) Carry-over effects: influence a particular treatment or task has on performance on a subsequent treatment or task
- 9. Counter-balancing • order in which conditions of a repeated measures design are arranged so that each condition occurs equally often in that position • Done to counter the unwanted effects on performance of any one order Between- participants counter-balancing - Involves counterbalancing the order in which the groups of participants are exposed to the experimental conditions Within-participants counter-balancing - Requires each participant be exposed to the same combination of conditions - E.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
- 10. Demand characteristics Demand 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 don’t 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.
- 11. 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
- 12. 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
- 13. Placebo effect Occurs 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 procedure - By using placebos (fake treatments) so that the control group does everything the experimental group does
- 14. Experimenter Effect Occurs when there is a change in a participants response due to the experimenters actions rather than to the effect of the IV Can 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 behave - Can 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.
- 15. Single-blind procedures Participants are unaware of which condition they have been allocated to OR Experimenter is unaware of which condition participants have been allocated to Eliminates either participant expectations OR experimenter expectations
- 16. 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
- 17. Single-blind Vs Double-blind Similarity - Participants in both procedures are unaware of the particular condition in which they have been allocated Difference - In 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
- 18. • Sample • Population • Random sampling • Stratified sampling • Stratified random sampling • Convenience sampling • Control groups • Experimental groups • Random allocation
- 19. Sample Vs Population Sample Population A group that is a portion of a larger group chosen to be studied for research purposes A sample should be representative of the population Is the entire group of research interest
- 20. Sampling Procedures 1) Random sampling - every member of the population has an equal chance of being selected as a participant for a study e.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
- 21. Sampling Procedures 3) 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
- 22. Why is random sampling the preferred method of sampling? Because it is more likely that a sample gained in this way will: 1) Be representative of the population 2) Have participant variables distributed in the sample in the same proportion as they exist in the population
- 23. Control Vs Experimental Groups Control group Experimental group/s Is 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 DV Is exposed to the independent variable (IV) Can have numerous experimental groups
- 24. Random allocation Every participant selected for the experiment has an equal chance of being selected for any of the groups used (control/experimental)
- 25. -Independent Groups Design -Repeated Measures Design -Matched Participants Design
- 26. Research Designs Independent groups Repeated measures (within subjects) Matched Participants (between subjects) Key Features •Participants are randomly allocated to different groups • each group is assigned to only 1 condition (experimental/control) Each participant completes all experimental conditions •Participants are paired/grouped on relevant characteristics and then each member is allocated to different conditions • each participant completes only one condition Benefits •No order effects • no pre-testing required • Experiment not over a long time period – fewer drop outs •Elimination of participant related variables • ability to use fewer participants than IG • no pre-testing required •Needs fewer subjects than IG •Controls for participant variables • No order effects • Experiment not over long time period Limitations •Need more participants for strength of results • participant variables aren’t controlled •Order effect • Boredom effect – may be fatigued/bored by the time they do second task and wont perform as well •Time & expense required to collect info through pre- testing • If one participant drops off then both in the pair are lost to data pool
- 27. Cross-sectional research designs Data is collected at one time from participants of all ages and different groups are compared Strengths: - All data collected at once and readily available - Cheaper and less time consuming than longitudinal studies - Less chance of participants ‘dropping out’ of the study Weakness: - Large numbers of participants needed
- 28. Longitudinal research designs The same participants are investigated over a period of time Strengths: - Less interference from personal characteristics - In studies of progressive mental health conditions such as Alzheimers, this type of study is the only means of investigating how disease progresses Weaknesses: - Time consuming - Participant ‘drop out’ likely
- 29. -Types of data -Method of data collection
- 30. Qualitative vs. Quantitative data Qualitative: 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
- 31. Objective vs. Subjective data Objective: 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
- 32. Data collection techniques Case study Self-reports - Interviews - Questionnaries Observational studies - Naturalistic observation - Non-participant observation - Participant observation
- 33. Case study In depth study of an individual group or event Strengths: - Detailed information is collected - Can be used to create research hypotheses Weaknesses: - Time consuming - Cannot be generalised (until confirmed by experimental research)
- 34. Self-reports Participants 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 time - Can gain data on sensitive topics because of anonymity - Useful for collecting qualitative & quantitative data Weaknesses: - Rely on assumption participants will answer all questions and will answer honestly - Often participants give socially desirable answers - Subjective data (difficult to verify by researchers) - Depending on types of questions, answers can be restricted
- 35. Interviews Involve 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 characteristics - Easier 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)
- 36. Questionnaires Method of collecting written responses from participants Could be surveys or likert-type scales Strengths: - Easy to replicate & score - Provides a means of quantifying subjective data Weaknesses: - May be open to bias if a participant is trying to appear socially desirable - Could be difficult to analyse data if open-ended questions are used
- 37. Observational study Involves collection of data by carefully watching and recording behaviour as it occurs Types of observational studies - Naturalistic observations: Observation of voluntary behaviours occurring within the subjects natural environment by a researcher - Controlled 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 observations - Participant observation: when the researcher is an active member of the group being observed
- 38. • Experiments • Correlations • Descriptive Statistics •Inferential Statistics
- 39. Experiments Is 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)
- 40. Correlation studies Describe 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 = 0.00 Strong positive correlation = +1.00 Strong negative correlation = -1.00
- 41. Difference between Descriptive & Inferential Statistics Descriptive Inferential Only gives information about the nature of the data set Enables organisation of data Enables: • Testing of hypotheses • Determining statistical significance • Drawing conclusions from results • Generalisations of findings to population
- 42. Types of descriptive statistics Measures of central tendency - Mean: - average of all scores in a set of scores - Calculated by adding all scores together then dividing by total number of individual scores - Is accurate when all scores cluster around a central score; misleading if data is widely spread e.g. 2, 7, 3, 10, 15 (mean = 7.4)
- 43. Types of descriptive statistics Measures 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 scores - Useful when there’s limited data and when there is extreme scores as it’s not affected by these
- 44. Types of descriptive statistics Measures of central tendency - Mode: - most frequently occurring score in a data set - Used infrequently because it’s not representative of a complete set of data * So which is the preferred measure of central tendency???
- 45. Experimental Group Control Group - Effect of drugs on Driving performance 70 20 45 20 Do drugs affect driving performance? 28 20 23 20 Do 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
- 46. 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% (p≤0.05) 1% (p≤0.01) 0.1% (p≤0.001) You will be given a p-value on the exam and you need to compare it to the following statements: - if p≤0.05 then there is a significant difference - if p≤0.01 then there is a significant difference - if p≤0.001 then there is a significant difference You then need to say what this means in ‘plain English’
- 47. p values if p≤0.05 then there is a statistically significant difference - There 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 p≤0.01 then there is a statistically significant difference - There is less 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 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
- 48. p values if p≥0.05 then there is no significant difference - There 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 difference - There 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
- 49. Reliability Validity
- 50. Reliability Refers 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 consistency - Refers to the interrelatedness of questions in a psychological test in measuring the same ability or trait - High score means that all the rest items relate to or assess the same psychological characteristic
- 51. Validity Means 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 it’s not valid, but cannot be valid unless its reliable.
- 52. Conclusions A 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 it’s 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
- 53. Generalisations A 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

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