UNIT 3 & 4 PSYCHOLOGY RESEARCH METHODS TOOLKIT

Transcription

1 UNIT 3 & 4 PSYCHOLOGY RESEARCH METHODS TOOLKIT Prepared by Lucie Young, Carey Baptist Grammar School lucie.young@carey.com.au Credit to Kristy Kendall VCE Psychology research methods workbook for some definitions included in this presentation.

2 STUDY DESIGN experimental research: construction of research hypotheses; identification and operationalisation of independent and dependent variables; 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 research 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 statistics: measures of central tendency including mean, median and mode; interpretation of p-values and conclusions; evaluation of research in terms of generalising the 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.

3 TABLE OF CONTENTS View in slideshow modeeverything is clickable! Click the home button on each page to return here. Hypotheses Variables (IV, DV), Operationalisation of variables Identification of extraneous and confounding variables: individual participant differences, nonstandardised instructions and procedures, order effects, experimenter effect, placebo effects Improvement of extraneous and confounding variables: sampling procedures, type of experiment, counterbalancing, single and double blind procedures, placebos, standardised instructions and procedures Evaluation of Research Designs Reporting conventions (APA format) Sampling procedures: Convenience sampling, Random sampling, Stratified sampling, random-stratified sampling Allocation procedures, control and experimental groups Qualitative and Quantitative data Case studies, observational studies, self reports Descriptive statistics Measures of central tendency: Mean, median and mode Inferential statistics and p-values Conclusions Generalisations ethical principles and professional conduct

4 RESEARCH HYPOTHESIS A hypothesis is a testable prediction about the relationship between two variables. An experiment looks to find out how the IV affects the DV. In any hypothesis, you must include 4 things: The Population that the hypothesis will apply to BOTH levels of the Independent Variable The Dependant Variable A specific prediction about what will occur Lets see what this actually looks like

5 HYPOTHESES Population being tested It is hypothesised that primary school aged children who drink warm milk before bed will have fewer nightmares than children who do not drink anything Both levels of the IV (must say both!) The specific prediction: the experimental group will have less nightmares than the control group The DV: what is being measured. Here it is the # of nightmares.

6 HYPOTHESIS TIPS You only need to write a research hypothesis. This means you do not have to operationalise each variable. You still need to be careful though. Put in detail if you can! Make sure you include BOTH levels of the IV You do not need to justify WHY you are making that prediction within the hypothesis itself. This is what the background info in an introduction section of a report is for. Make sure you include the 4 key things mentioned on the previous slide!

7 INDEPENDENT VARIABLE A variable is any condition that can change. The Independent variable (IV) is the variable that the researcher changes, manipulates, selects for or varies. This is to see the effect on the other variable, the DV. Usually in an experiment there is a control group and an experimental group. By manipulating the IV differently between these two groups, researcher can see the effect on the DV.

8 DEPENDANT VARIABLE The dependant variable is the condition that is affected by the IV and is also used to measure the effect of the IV. Usually a measure of performance, a number or score. Easy to identify as (usually) something that can be counted/quantified or compared.

9 VARIABLES TIPS If you struggle to remember which is which, look for the dependant variable first. This is the measure, the number, the count, or the quality being investigated. Its outcome depends on what the researcher does. Once you have found the DV, figure out what the other key variable is. This should be your IV.

10 IV DV EXAMPLES Eating carrots will improve a persons eyesight Eating carrots = independent variable. (note that carrots alone is not enough what are you doing with them????) Improvement in eyesight=dependent variable. Something you can measure!

11 OPERATIONALISATION This is very important. You MUST be able to operationalise each of your variables. Whenever you are asked for the IV and DV in an exam, 99% chance it will be asking for the operationalised IV and DV. Operationalisation just means detail on each variable and how it is administered or measured. You must dig deep in each scenario for as much detail as you can muster!

12 OPERATIONALISATION Remember the example from before? Eating carrots will improve a persons eyesight Lets pump this up with some more detail! IV= eating carrots DV= improvement in eyesight Detail about how many, how long, what sort, what you are doing with them. Operationalised IV= Eating 6 standard sized carrots daily for a period of 6 weeks Operationalised DV= percentage improvement in score on standard eyesight test from week 1 to week 6. Detail about how you will measure improvement and also what specific tests will be used (if you know)

13 EXTRANEOUS AND CONFOUNDING VARIABLES An extraneous variable is any variable other than the IV than causes a change in the DV. This has an unwanted effect on the experiment, because it makes any causal relationships between IV and DV hard to establish. EV s can be participant variables (ie. Intelligence levels), experimenter effects (ie non-standardised instructions), or situational variables (ie uncomfortable sleep lab). A confounding variable is when an EV is not controlled for, it will have systematic confounding effects on the experiment. These will happen time and time again until that variable controlled for. (look for things within the design of the experiment)

14 INDIVIDUAL PARTICIPANT DIFFERENCES Each person brings with them to an experiment their own individual differences. If particular differences are not controlled for, these may cause a change in the DV. (see Research Designs and how to control EV s) Examples could include: gender, age, intelligence, memory ability, brain injury, health, experience on a particular task or occupation, sleeping patterns. When looking for individual participant differences, you must choose the most relevant and explicit examples if you want to state them as EV s or CV s in the exam.

15 NON-STANDARDISED INSTRUCTIONS AND PROCEDURES EV s can occur if there are differences in the way the experimenter (or each experimenter if there is more than one) conducts their experiment each time they run it. This could be differences in the procedures carried out or the instructions given to participants If there are differences in administration each time, then this is an example of the research being non-standardised. This will make it hard to draw any conclusions because there is no consistency in experimentation.

16 ORDER EFFECTS An order effect might occur when (in a repeated measures design) the sequence in which a person does the tasks affects their performance on the tasks. This could be seen through either improving performance on second task due to practise, or worsening due to fatigue or boredom. This would confound results, creating a false reading of the effect of the IV on the DV. See counterbalancing for ways to improve order effects

17 EXPERIMENTER EFFECTS The experimenter effect occurs when there is a change in the participants behaviour as a result of the interaction or influence with the experimenter or researcher. This could be treating the experimental group differently to the control group, or dropping hints about what results they would expect to see. See double-blind procedures as a way to minimise experimenter effects.

18 PLACEBO EFFECTS The placebo effect is where participants will behave differently due to their own expectations about the treatment they are receiving. For example, if participants in the experimental group are given a smart pill and the control group is given no pill, the experimental group may perform better because they have been given something they believe will make them smart. Likewise the control group may perform worse because they believe they have been given no treatment. See placebo and single and double blind procedures for ways to reduce placebo effects

19 IMPROVEMENT OF EXTRANEOUS AND CONFOUNDING VARIABLES In an experiment, the researcher wants to minimise all extraneous variables, hopefully to achieve good (statistically significant) results. To do this they must be careful about their research and experimental design, their sampling procedures, and the ways that they eliminate potential problems in their experimentation

20 MINIMISING EV S: SAMPLING PROCEDURES The type of sampling procedure must be appropriate and minimise the chance of bias in the sample. For example- random sampling will ensure that each person has a chance, whereas convenience sampling is not based on chance and invites potential bias. For example- if there is a variable in the population that can impact on results in that particular experiment (age, gender, ethnicity etc) than stratified/random stratified sampling should be used. Remember, your sample should reflect your population. How should you sample to most accurately do this? Exam tips: you may be asked to justify why a certain technique was used, or to suggest the most appropriate sampling procedure to be used for a scenario. Always think: what will most accurately reflect my given population? See sampling procedures

21 MINIMISING EV S: TYPE OF EXPERIMENT The type of experimental design used must be carefully chosen to reduce EV s and CV s ie repeated measures experimental design eliminates differences in participant variables, but adds the potential for order effects. However if repeated measures is used, counterbalancing can be put in place to reduce order effects. The appropriate design could be different in each different scenario. It depends on the experiment that needs to be conducted. Read your scenario carefully, or think carefully about how you could design your experiment with as few problems as possible. Exam tips: you may be asked to justify why a certain experimental design was used, or to suggest the most appropriate experimental design to be used for a scenario.

22 COUNTERBALANCING Counterbalancing is used to control for the order effects in a repeated measures design. Counterbalancing is when the participants are divided into two groups. Each group of which are exposed to both conditions, but are exposed in different orders. ie investigating effect of new training technique vs. old training technique on number of goals scored. Researcher would have half the sample participate in the old training technique and then the new training technique The other half of the sample would participate in the new training technique then the old training technique. (important note- the groups are only in place to work out the order of treatment. Each person still takes part in all conditions, therefore is still repeated measures and not independent groups design don t get tricked!).

23 SINGLE AND DOUBLE BLIND PROCEDURES A single-blind procedure is where participants do not know which group they have been assigned to (experimental or control groups). This reduces the impact of participant expectations ( helps control for the placebo effect not eliminate!) Note too: it is possible to have a single blind procedure where the experimenter does not know which groups his participants are in but the participants do know. This is much less common and unlikely.

24 SINGLE AND DOUBLE BLIND PROCEDURES A double blind procedure is when the participants and the experimenter both do not know which group they have been assigned to. This is to help control for both the placebo effect and experimenter effects (not eliminate!!). This will be obvious in a scenario if there is a third party- ie research assistant- who does the allocating and knows the groups, but does not conduct the actual experiment.

25 PLACEBOS A placebo is a fake or false (non-effective) treatment. This is given to the control group. Neither group would know which treatment they receive. (see single and double blind procedures) This means that participant expectations will be less able to influence results. The placebo that is given will help to control (not eliminate) for the placebo effect. See placebo effect

26 STANDARDISED INSTRUCTIONS AND PROCEDURES Instructions to participants must be the same each time they are given. These could be written down and given to the participants to read, or read out like a script. Each experimenter must conduct the procedure of the experiment exactly the same each time it is done. This could be done by using a step by step list of instructions. Each participant must be treated in exactly the same way. Control and experimental groups should not be treated differently, apart from the specific variable being manipulated. See non-standardised instructions and procedures

27 EVALUATION OF RESEARCH DESIGNS The experimental design is very important. The three we use in psychology are: Repeated Measures Matched Participant Independent Groups You have to evaluate, which means you need to know what they are, how to use them, AND the strengths and weaknesses of each (yes plural = at least 2!!). You will also need to know when each is appropriate to use and when it is less appropriate. You will need to be able to justify this.

28 INDEPENDENT GROUPS Participants in the sample are randomly allocated to and have an equal chance of being in to either a control group or experimental group. Strengths Use of different participants in each condition means there is no order effects to control Use of different participants in each condition means that the time required to complete the study is often shorter, as two conditions can be conducted at one time. Inexpensive, quick, easier to administer. Use of different participants in each condition means that there is less chance of attrition or drop outs, compared with the repeated measures design. Limitations May not be a representative sample of the population as no differences between the two groups have been controlled for Above is particularly evident if there is a small sample size, so a disadvantage would be that this design requires more individuals to be used in order to reduce bias from uncontrolled participant variables.

29 INDEPENDENT GROUPS Use when the experiment does not require particular participant variables to be controlled (ie intelligence or memory ability will not impact on results) and when you can easily access large numbers of participants, to quickly set up and administer the experiment.

30 MATCHED PARTICIPANTS After pre- testing, participants are paired together based on their similarity to a certain characteristic researcher wishes to control for. One from each pair is assigned to control and other to experimental group. Strengths Participant variables are more constant across conditions, controlling this as possible confounding variables. Use of different participants in each condition means that the time required to complete the study is often shorter, as two conditions can be conducted at one time Use of different participants in each condition which may be conducted at the same time, means that there is less chance of attrition or drop outs, compared with the repeated measures design. Limitations Matching participants on all characteristics is unlikely to be able to occur. People still have individual differences. Defining the characteristic properly is also difficult. Matching participants is time consuming and costly. Twins are best for this as they have the most characteristics in common; however twins are uncommonly available in large numbers. Pre-testing may create an order effect or an expectation from the participants Attrition (drop outs) can still be a problem, if one part of the pair stops or discontinues, both sets of results must be removed

31 MATCHED PARTICIPANTS Use when you have a variable that is fairly easily defined and participants can be matched on ie age, gender, IQ score. Used when the participant variables would impact on the results. Use when order effects from Repeated measures would occur and need to be controlled.

32 REPEATED MEASURES Repeated measures is when each member of the sample participates in both the experimental and control conditions. Strengths Controls potential confounding variables arising from individual participant differences, because it uses the same participants in all conditions. Requires a relatively smaller number of participants compared with other experimental designs, as each participant acts in both the control and experimental groups Limitations Order effects may occur from the participant taking part in the first condition and then the second condition. Performance on second task may be enhanced or due to practise. Performance may be decreased on the second task due to fatigue or boredom. Repeating the conditions with the same participants takes time (particularly if delayed time to eliminate order effects). This means that it takes a lot longer for researcher to gather all data. Participants may be more likely to drop out between testing times. This means all of their results would then be removed

33 REPEATED MEASURES Used when the participant variables would impact on the results and need to be controlled. Use when a smaller number of participants is necessary or the variable is hard to define or control (ie brain damage)

34 APA REPORTING CONVENTIONS APA stands for American Psychological Association and is the standard format for all psychological reports. This should be familiar to you as being the sections of your ERA reports. Abstract: Summary Title: contains IV and DV Introduction: Contains background info, key terms, IV, DV, aim and hypothesis Method: contains participants, materials, and procedure Results: contains summarised data ie tables and graphs, and a statement of result Discussion: contains support for hypothesis, discussion of research methodology, implications, generalisations and conclusions.

35 APA REPORTING CONVENTIONS It is unlikely you will get a multiple choice or short answer question on the APA format. What is very likely is that in the extended response, you will be asked to write a section of an ERA report, ie. the introduction, or the discussion, or parts of both. You need to know the things that goes in each section, the way the text should flow, and the order that information would logically appear as per the reporting conventions. See ERA guidelines, or How to write sheets, on CLASSe for more help

36 POPULATION AND SAMPLE The group we wish to investigate and draw general conclusions about is called the population For practical reasons, we cannot test everyone in the population We draw a smaller group from the population to test, called the sample It is important to make sure that the sample is representative of the population (shares similar characteristics) for the best research conclusions to be made. We have different sampling techniques to do this.

37 CONVENIENCE SAMPLING Convenience sampling is when subjects are picked based on their availability at the time of the experiment. Quick, easy, cost effective. Convenience sampling usually presents a biased sample. They may not be representative of the population. Looks like: people on the street, people in a class, people who volunteer, first 10 people in the door

38 RANDOM SAMPLING Random sampling is where every member of the population has an equal chance of being selected for the sample being used in the study. Quick(er than stratified), relatively simple to employ. Less chance of bias, however can be open to bias if the population has under or over represented groups in it. Looks like: names of everyone in population being drawn out of hat, being put into a computer random name generator, being given a number and numbers drawn or rolled.

39 STRATIFIED AND RANDOM STRATIFIED SAMPLING Stratified sampling is to be used when there is diversity or under/over represented groups in the population. Stratified sampling involves breaking the population into groups or strata based on the characteristics you wish to control for in the sample. This could be age, gender, ethnicity etc. Once the population is divided into strata, participants are selected for the sample in the same proportions (ratios, percentages) that exist in the population. If random stratified sampling is used, this just means that the selection from the already defined strata uses the equal chance methods (ie names in hat, number generator)

40 EXAMPLE Researcher interested in memory ability of adults. Divides population into age brackets 21-30, etc Then selects participants from each age bracket, in the same proportion that exists in the population Ie, if there was 20% of adults aged in the population, there should be 20% of the sample also aged Looks like: when researcher needs to keep proportions of something controlled, look for gender, age, ethnicity control, selecting sample in same proportions. May or may not then select using random methods.

41 ALLOCATION TO CONTROL AND EXPERIMENTAL GROUPS Once a sample is chosen, participants must be allocated to either the control or experimental groups. The experimental group is the group that is exposed to the experimental condition where the IV is present. The control group is the group that is not exposed to the experimental condition (the IV). The control group provides the comparision or baseline performance on the DV against which the performance of the experimental group can be compared. The best way to ensure that participant characteristics are evenly distributed in both groups is through random allocation to groups. Random allocation is where each person in the sample has an equal chance of being in the control or the experimental group. This could be done via the same methods as random sampling: names in a hat, toss of a coin, numbers given then drawn out etc.

42 QUALITATIVE AND QUANTITATIVE DATA There are different types of data that can be collected in different ways. Data is sometime called empirical evidence in psychology Qualitative data are data that describes changes in the qualities of the behaviour and are often expressed in words. Often subjective due to different individual interpretations of the data. Example could be words to describe someone s facial expressions ie. happy, sad, surprised, very shocked.

43 QUANTITATIVE Quantitative data is that which takes a numerical or categorical form, and can be statistically analysed and measured. Often seen in numbers or counts ie. quantitative data example from the previous slide could be the number of times someone smiles while reading a page of comics. Tends to be based on statistical data so seen as less subjective and more objective.

44 CASE STUDIES A case study is an in-depth analysis of an individual, group or situation. They might include medical histories, interviews, observations, reports and other pieces of information. The benefits are that it allows researchers to gather a lot of information on the one person/group The limitations are that it may be quite specific to that individual and findings may not be generalised to the population. It is also time consuming.

45 OBSERVATIONAL STUDIES An observational study involves an individual watching another person or group, usually in their natural environment, and taking notes about the observations made. The benefits are that it allows a more natural environment eliminating some EV s. There is also less chance of someone responding in ways they perceive as favourable (if they don t know/forget they are being observed) The limitations are that the interpretation of the observations can be subjective and open to observer bias.

46 SELF-REPORTS A self-report method is used when an individual comments on their own thoughts, emotions and perceptions when answering a series of questions asked by the researcher. Advantages are that it allows the researcher to gain valuable information about things that are not overt. A disadvantage is that participants may not answer truthfully. The self reports could be in the form of a: Survey: verbal/written questions. Can be open or closed questions Questionnaire: written questions. Can be open or closed questions. Often uses a rating scale. Interview: usually face to face or telephone questions. Might be structured or unstructured. Could also be both closed or open questions.

47 DESCRIPTIVE STATISTICS A descriptive statistic is a way to summarise, organise and describe raw data, so that it can be more easily interpreted. Be careful- if a descriptive statistic alone is given, you cannot draw any cause and effect relationships, and therefore limited conclusions and generalisations See inferential statistics and p-values, conclusions and generalisations The most common forms are: Percentages Graphs of all kinds Tables Measures of central tendency

48 MEASURES OF CENTRAL TENDENCY Measures of central tendency involve calculations to show how scores fall in a data set. The 3 most common ones used are mean, median and mode. Mean: Also called the average. All scores in the data set are added up (summed) and then the total score is divided by the number of pieces of data in the set. Median: Is the middle number in an ordered set of scores. You MUST make sure the data set is firstly organised from smallest to largest entry. The median is the middle number in this ordered set. If the data set is an even number, you must take the mean (average) of the two middle numbers. Mode: is the most commonly or most frequently occurring number in the data set. Each measure may be useful depending on circumstances. Often small samples or outliers can make some less useful.

49 INFERENTIAL STATISTICS Inferential statistics allow us to infer cause and effect relationship between variables. They allow us to say that the IV has affected the DV. They allow conclusion to be formed and generalisations to be made. They are statistical calculations. One that you need to be able to interpret is the p-value (usually from a t-test). You do not have to actually calculate this in Yr. 12.

50 P-VALUE An acceptable level of error is set by the researchers before the experiment begins. Convention is for this to be set at p<0.05. It is possible for it to be set lower (ie. P<0.01) but you will be told of this. p<0.05 The probability that results occur due to chance Is less than 5 %

51 P-VALUE: INTERPRETATIONS If p<0.05 Note this usually means less than or equal to. Results are statistically significant Results (changes in DV) are due to the effects of the IV. The likelihood of chance is less than 5%. Hypothesis can be accepted; conclusions can be drawn Generalisations may be able to be made. If p>0.05 Results are not statistically significant (do not say insignificant) Results are likely to be due to chance alone Hypothesis is rejected; no conclusions can be drawn 3 step method to p-value success: 1. Restate the obtained p-value 2. State if statistically significant or not 3. Explain what this means in terms of likelihood of chance

52 CONCLUSIONS A conclusion is a decision or judgement about the research results compared to the hypothesis. For a conclusion to be made, there should be a statistically significant result. This means you can only draw conclusions if you have been given inferential stats, not descriptive stats.

53 GENERALISATIONS Generalising to a population is the main goal of psychological research. A generalisation is when a research finding can be applied from the sample to the broader population. A generalisation should only be made if the following four things are all met. 1. The results must be statistically significant (inferential stats only!!!) 2. The sample is representative of the population you wish to generalise to 3. The sampling method was appropriate (unbiased) 4. Extraneous and confounding variables have been controlled for to the best of ability (no obvious flaws)

54 ETHICS the role of the experimenter is to protect their participants psychological and physical welfare, to uphold the integrity of the profession, to be fair and just towards all involved and to provide a benefit mankind with their research. protection and security of participants rights involves upholding 6 key ethical principles.

55 ETHICAL PRINCIPLES (CVWIDD) Confidentiality: researcher must collect, retain and dispose of all information related to participants in a manner that does not disclose their identity within the research. Participants must not be connected to their results or publically identified. Voluntary participation: each participant has the right to choose to willingly take part in the research and must not be coerced, forced or tricked into taking part. Withdrawal rights: each participant may leave the research at any time without negative consequences or pressure to continue, and may remove their results from the data set at any time. Informed consent procedures: researcher must first fully explain to the participant about the true nature and the risks of the experiment, and then obtain written permission on a consent form in order to take part. If under 18 or incapable, parental/guardian consent should be given. use of deception in research: deception should not be used in research unless it is really necessary. It may only be used when knowing true nature of the study would change the results, and when the deception would not cause harm to the participants. If deception occurs, participants need to be fully debriefed. Debriefing: debriefing occurs after the experiment has concluded. During debriefing, a researcher must inform the participant about the results and the true nature of the experiment, and correct any misconceptions. The researcher must provide counselling if harm has occurred.

56 LAST WORDS Remember that Research Methods is a big component of the exam, but it can also be fairly predictable! Think about the likely questions you would be asked for each key word. Think about how each might look or present themselves in a scenariowhat does a placebo look like? What clues are there to show they re using stratified sampling? Practice is really the key to success. Keep looking for different extended responses and research evaluations. After a while it will become second nature! Good Luck!