A2 Psychology Revision

Research Methods

Key Words

Empirical Data Data which can be physically seen.

Objectivity Data and research which is not subjective or bias.

Peer Review A third-party/external body within the same field, validates research.

Abstract A summary of a study/research.

Experiments Carrying out a test under controlled conditions/environments.

Self-Report Methods Participants are asked to report their own attitudes, abilities or feelings.

Pilot Studies A small-scale trail of a study to test all aspects of the design.

Inter-rater Reliability Two or more observers are present throughout the analysis of data.

Test Re-test Reliability A test is repeated a second time using the same sample and test.

Split-Half Method A test split between two different situations to see if the same results are found.

Stratified Sampling A group of participants selected according to their frequency within a population.

Inferential Statistics Test

Procedures for making inferences about the population from which samples are drawn.

Probability A numerical measure of the likelihood/chance that certain events will occur.

Significance A set of findings are sufficiently strong enough for us to accept.

Chance The extent to which something occurs randomly.

Reflexivity The process of self-examining qualitative research.

Content Analysis An indirect observation of somebody through their work (this is usually presented through trends.

Null Hypothesis A hypothesis stating “No findings”.

Alternative Hypothesis A hypothesis stating what you except to find from a successful test.

Observations

Proposed Theory

Testable Hypothesis

Conduct A Study

Draw Conclusion

Refine

Observations

Testable Hypothesis

Conduct A Study

Draw Conclusion

Propose Theory

Refine

ScienceAPPLICATION ONE: THE MAJOR FEATURES OF SCIENCE

Science is a means of finding out the truth and facts about our world; however in order for this to be achieved various factors must first be demonstrated throughout the scientific research;

Empiricism – Information is gained through direct observation or experiment.

Objectivity – Personal expectations should not affect what findings are recorded.

Replicability – Previously recorded methods and procedures are re-tested to see if the outcome is the same.

Control – Research attempts to find relationships through experimental methods which require a degree of control (e.g. we vary the independent variable and observe its effects on a dependent variable). We must ensure all other conditions are the same (controlled).

Theory Construct – A theory is a collection of general principles that explain observations and facts.

APPLICATION TWO: THE SCIENTIFIC PROCESS

Induction

This involves reasoning (creating a theory) from the particular to the general. An

example of this is Newton’s Laws.

Deduction

This involves reasoning from the general to the particular (starting with a theory and

looking for instances that confirm this). An example of this is Darwin’s Theory.

Popper (1935) proposed the hypothetico-deductive model which

suggests a theory should come first as this can be used to generate hypotheses, which

can be falsified.

Evaluation of Psychology Is A Science?Strengths Limitations

In the nineteenth century, psychologists wanted psychology to become a science because it would enable them to produce verifiable knowledge for their research. Psychologists claim that human behaviour can be measured as objectively as any other scientific variable. Claims that studies are ‘experimenter bias’ and experience ‘demand characteristics’ are just the same as an experimenter measuring a atoms behaviour (as it will change its format when observed).

Miller (1983) suggests that psychologists simply use the tools of a scientists (e.g. content analysis and quantative analysis), but the essence of science has eluded them.

Kuhn (1962) suggests psychology is not a science because it doesn’t have any single paradigm (a shared set of assumptions) e.g. the behavioural, cognitive and biological approaches.

Laing (1960) suggests that psychology and science have very different goals. Psychology uses an Idiographic approach – taking into account individual cases (he suggested that the only way to treat individuals was to view their own case), whereas science generalises using a nomothetic approach.

Validating New Knowledge/ResearchEXPLANATION ONE: THE ROLE OF PEER REVIEW

Peer review is the assessment of scientific work and research, by others who are experts within the same field. The intention of peer reviewing is to ensure that any research conducted and published is of a high and valid quality.

Peer review serves three main purposes;

Allocation of Research Funding – Research is paid for by various government and charitable bodies, therefore public bodies require reviews to enable them to decide which research is likely to be accurate, valid and worthwhile.

Publication of Research In Journals - Peer review has been used when publishing research into journals to help identify and prevent any incorrect or faulty data entering the public domain.

Assessing the Research Rating of Universities – All universities are expected to conduct research which is assessed in terms of its quality, future funding depends upon the rating given.

Evaluation of Peer ReviewStrengths Limitations

Peer review gives another way of validating data, and provides another safety precaution again potentially damageable research being leaked into society. By using researchers within the same field as the research, it may produce more reliable and accurate results.

Smith (1999) claimed peer review is “slow, expensive, profligate of academic time, highly subjective, prone to bias, easily abused, poor at detecting gross defects and useless at detecting fraud”. It isn’t always possible to find an appropriate expert to review proposed research, this means poor research may be passed because the review doesn’t understand.

Reviews do not have to disclose their identity, which could lead to rivalry.

Synoptic; Peer review favours publication-bias as it wants to promote ‘valid’ and positive research.

EXPLANATION TWO: REPORTING PSYCHOLOGICAL INVESTIGATIONS

A psychological investigation is recorded within a journal, journals are constructed using a...

Abstract - A summary of the study and its components. Introduction and Aim – This is what the research intends to study (includes hypothesises). Methodology – A detailed description of the research procedure is laid out (for replication). Results – This is what the research found. Discussion – The researcher may offer explanations and limitations about their research.

References – The full details of any journal articles/books that are mentioned.

Research Methods and Associated ConceptsEXPLANATION ONE: EXPERIMENTS

All experiments involved an IV (Independent Variable) and a DV (Dependent Variable). The IV is varied in order to see how this affects the DV, thus demonstrating a relationship.

When designing an experiment psychologists must take into account the various measures (experimental designs) which can be used, for example;

Repeated Measures - Each participant is tested on all of the IVs.

Independent Groups - Separate groups are tested for each IV.

Matched Pairs - Participants in an independent group are matched with participants in another group on key variables.

Laboratory Experiment – An experiment carried out within a controlled environment. Advantages;

High internal validity because extraneous variables are controlled.

Control increases replicability.

Disadvantages;Reduced external/ecological validity and mundane realism because of the controlled environment.

Experimenter effects (experimenter-bias and demand characteristics).

Field Experiment – An experiment carried out within a more natural environment, although some factors may still be controlled.

Advantages;Experimenter effects are minimal.

Increased external/ecological validity and mundane realism.

Disadvantages;Demand characteristics are unlikely but could still occur due to the controlled factors (IV).

Natural Experiment – An experiment which makes use of existing IVs (no control). Advantages;

No experimenter effects.

Increased external/ecological validity and mundane realism.

Disadvantages;Participants are not allocated to conditions which could reduce the experiments validity.

Natural studies are hard to replicate.

EXPLANATION TWO: RESEARCH METHODS

Self-Report Methods – Psychologists use questionnaires and interviews to find out what people think and feel. They can be structured or un-structured (although structured allows for better replication), and feature open or closed questions (open questions producing more un-expected,

qualitative data). Self-reporting methods do however feature demand characteristics as participants are more likely to portray themselves in a positive light.

Observational Studies – Psychologists use behavioural categories to record particular instances of behaviours, or various sampling methods, such as; time sampling (recording every 30 seconds) or event sampling (every time a behaviour occurs). Due to this we gain an accurate account of what is really happening, however this could also cause experimenter/observer bias (because their observations can be affected by their expectations).

Correlational Analysis – A correlational analysis does not demonstrate a cause, but is useful in identifying relationships between co-variables. Correlational analysis can use large data sets making the findings more reliable, and can easily be replicated. However they also lack internal and external validity, as the methods used may lack generlisability.

Case Studies – Case studies are detailed studies of a single individual, institution or event. It uses data from a range of sources, through various methods (questionnaires, observations and interviews) and are generally longitudinal. They may also identify and analyse complex interactions of many factors – unlike the experiments were many variables are held constantly. They do however fail to collate generalised data, and often use retrospective data (previously collated data) which may make the results unreliable.

Meta-Analysis – This is an analysis of a range of studies within the same topic/field, and can be used to draw overall conclusions and theories. This can also be used to assess the reliability and validity of general theories, models and approaches.

EXPLANATION THREE: SAMPLING TECHNIQUES

Opportunity Sampling – Participants are selected by using people who are most easily available, this is an much easier and quicker way of gathering participants, but is not representative and provides no means of drawing generalised conclusions.

Volunteer Sampling – Participants are selected by asking for volunteers, which can provide access to a variety of participants, making the study more representative. However by using volunteers, the sample is more likely to be highly motivated with free time, impacting again on generalising the research.

Random Sampling – Participants are gathered using a random number technique, the target audience is first identified and then each proposed participants given a number, this number is then randomly selected by either a lottery-technique (‘drawn from a hat’) or from a random number generator. This type of sampling produced an un-bias sample because everybody has an equal chance of being selected.

Stratified and Quota Sampling – Sub-groups (strata) within a population are identified, and a predetermined number of participants are then selected from these groups (in proportion to their representation within the population). In stratified sampling this is done using a random-number technique, in quota sampling this is done using opportunity sampling. This method is more representative than the others, and is more likely to produce generalised data.

Snowball Sampling – Participants are first identified, and then asked to find other suitable participants themselves; this process then continues until enough participants are gathered. This

method of gathering participants may produce biased samples, because the individuals involved may only target a limited variety of population.

Reliability and ValidityEXPLANATION ONE: RELIABILITY

The reliability of any method of measurement (e.g. interview, questionnaire) refers to how consistent it measures something. A reliable test will give similar results, in similar circumstances.

There are two different types of reliability;

Internal Reliability – This refers to whether a test-method is consistently measuring what it should be. It can be improved using a split-half test (see below).

External Reliability – This refers to how consistent the test-method is over time, when repeated in different situations. It can be improved using a test-retest method (see below).

As mentioned above, we can assess the reliability of a test-method using the following;

Split-Half Method A method of assessing internal reliability by comparing two halves of a test-method (e.g. a questionnaire) to see if they produce the same score.

Test-Retest Method This assesses external reliability, by comparing the results of a test given in two different situations. If a positive correlation is found, it has strong external reliability.

Inter-Rater Reliability Two or more observer’s record individual scores on the test/participants, after comparing the results, if a positive correlation is found there is good reliability.

EXPLANATION TWO: VALIDITY

The validity of any method of measurement refers to how truly/realistically it measures something. A valid test will measure what it is supposed to measure – nothing else!

Like reliability, there are two different types of validity;

Internal Validity – How much the findings of a test-method are due to the manipulation of a variable, rather than another factors. (For example, if a manipulated IV affects an DV, the test-method has good internal validity).

External Validity – How much the findings of a test-method can be generalised to different settings, e.g. the environment (ecological validity) and people (population validity).

As mentioned above, we can assess the validity of a test-method using the following;

Face Validity The test-method looks like it is measuring what it is supposed to.

Construct Validity The test-method tests the theory about the variable it is measuring.

Concurrent Validity The test-method has a strong correlation with an already established test.

Content Validity The test-method tests everything (required).

Temporal Validity The test-method can be endured over time and are not era-dependant.

Population Validity The test-method can be generalised to the whole population.

Context-Ecological Validity

The test-method can be generalised to the general/natural environment.

Ethical Issues In Psychological ResearchISSUE ONE: ETHICAL ISSUES WITH HUMAN PARTICIPANTS

The BPS (2006) Guidelines – The British Psychological Society outlined four ethical issues which psychologists must follow, they include;

Respect Psychologists must show respect for the dignity and worth of all persons. This includes standards of privacy, confidentiality and informed consent (the participants should know what they are agreeing too). Participants should also have the right to withdraw from the research at any point.

Intentional deception (lack of informed consent) is only acceptable when it is necessary to protect the integrity of the research and when the deception is disclosed at the earliest opportunity.

Competence Psychologists should maintain high standards in their professional work.

Responsibility

Psychologists have a responsibility to their clients, general public and to the science of Psychology; this includes protecting the participants from harm, as well as debriefing the participants.

Integrity Psychologists must be accurate and honest.

The code of conduct (above) outlines ethical guidelines which psychologists should follow; in addition to this ethical committees assess research for its ethical principles. They may punish psychologists for not complying, and normally offer education to psychologists about their duties as researchers.

Socially Sensitive Research – Sieber and Stanley (1988) defined socially sensitive research as “studies in which there are potential social consequences or implications, either directly for the participants in the research or the class of individuals represented”.

ISSUE TWO: ETHICAL ISSUES WITH NON-HUMAN ANIMALS

Why Use Non-Human Animals in Research? – There are four main reasons why researchers would use non-human animals in research, they include;

Some researchers want to study animals because they find them fascinating, and generally want to help them succeed in life.

Animals offer the opportunity for greater control and objectivity in research procedures.

We may use animals when we can’t use humans, for example Harlow’s Monkey Experiment.

Humans and animals have enough of their physiological and evolutionary past in common, that we may justify conclusions drawn from experiments involving one, to the other. Although this is still under-debate.

Moral Justification of Using Animals in Research – There are three main arguments to the justification of using animals in research, they include;

Sentient Beings – There is evidence that animals respond to pain, however this is not the same as conscious awareness. Some primates also show evidence of conscious awareness.

Speciesism – Singer (1990) argued that discrimination on the basis of species in no different from racial or gender discrimination, and thus suggests the use of animals is an example of speciesism.

Animal Rights – Singer’s view is utilitarian, meaning whatever produces the greater food for the greater number is ethically acceptable. So if animal research can stop pain and suffering it is justifiable.

Constraints Currently in Place – The Animal (Scientific Procedures) Act of 1986, requires that animal research should only be carried out in licensed laboratories, with licensed researchers, on licensed projects. Licenses are only granted under the following conditions;

Potential findings are important enough to justify the use of animals. The research cannot be done using non-animal methods. The minimum number of animals is used. Any discomfort or suffering is kept to a minimum.

Russel and Birch (1959) also created the three R’s;

Reduction – Use of fewer animals. Replacement – Where possible use of alternative methods should be used. Refinement – Use of improved techniques to reduce stress.

Graphical Representation of DataREPRESENTATION ONE: CORRELATIONS

A correlation coefficient informs us about how closely co-variables (in a correlational analysis) are related, they can be displayed between -1 and +1.

0 0.5 1 1.5 2 2.50

0.51

1.52

2.53

3.54

4.5

Positive

0 0.5 1 1.5 2 2.5 3 3.5 4 4.50

0.51

1.52

2.53

3.54

4.5

Negative

0.5 1 1.5 2 2.5 3 3.50

0.51

1.52

2.53

3.54

4.5

No Correlation

REPRESENTATION TWO: DIFFERENT TYPES OF GRAPHS AND CHARTS

There are four main types of graphs and charts; Scatter graphs, Bar Charts, Histograms and Frequency Polygons. They are each used for individual purposes, outlined below;

Inferential Analysis, Probability and SignificanceEXPLANATION ONE: DEFINITIONS

Probability - Inferential statistics allow psychologists to look at patterns within results to see if they have arisen by chance. If it could not have arisen by chance, the pattern is described as significant.

Significance – In order to carry out an inferential test, you need a null hypothesis (H0 which will be used when results show nothing is found) and an alternative hypothesis (H1 which will be used when results show a correlation/relationship). Results are said to be significant when a large (significant) difference if found between variables and results.

Chance – Inferential tests allow us to determine whether a pattern in the data, could have arisen by chance or whether the effect occurred because there was a real difference/correlation. To do this a significance level is used (often p<0.05 – meaning a probability of 95% that the results are not due to chance). If the research wants to be more certain, they us a probability level of p<0.01 (99%) or p<0.001 (99.9%).

EXPLANATION TWO: USING INFERENTIAL TESTS

Types of Data – When deciding which test to use you may need to identify the level of measurement that was used, these are explained below;

NominalData which can be

categorized.

OrdinalData which can be ordered

(differences may not be equal).

IntervalData measured in equal

intervals.

Calculating the Critical Value - A test statistic calculated for any set of data is called the observed value (because it is based on the observations/results found). To decide if this observed value is significant, it is compared to a critical value (found within a critical values table – different for every inferential test).

To find a critical value, you need three pieces of information;

Degree of Freedom (df) In most cases this is the number of participants (N), for independent group studies there are two values for N, one for each group N1 and N2.

One-Tailed or Two-Tailed Test A one-tailed (directional hypothesis) predicts the direction of the results, whereas a two-tailed (non-directional hypothesis) does not.

Significance Level Normally p<0.05.

Some tests are significant if the observed value is greater than the critical value, while some tests are the reverse. To work out which one we should use, we say;

“If there is an R, then the observed value should be g R eate R than the critical value (e.g. speaRman’s Rho and chi-squaRe). If there is no R (e.g. mann-whitney and wilcoxon) then the observed value should be less than the critical value)”.

EXPLANATION THREE: ERRORS

There are two main errors, a type one error and a type two error.

Type One Errors (False-Positive) – This occurs when we wrongly accept the alternative hypothesis (and thus wrongly reject the null hypothesis), the results are due to chance therefore we must accept the null hypothesis.

This type of error is caused by a high significance level (e.g. 10%).

Type Two Errors (False-Negative) – This occurs when we wrongly accept the null hypothesis (and thus wrongly reject the alternative hypothesis), the results are accurate.

This type of error is caused by a low significance level (e.g. 1%).

Nominal Data? Chi-Squared

Correlation?

Independent Groups?

Spearman’s Rho

Mann-Whitney U

Wilcoxon T

Yes

Yes

Yes

No

No

No

Inferential TestsEXPLANATION ONE: CHOOSING A TEST

Chi-Square The hypothesis predicts a difference

between two conditions or an association between co-variables.

Data is independent (recorded only once).

Data is nominal (e.g. in categories) not a percentage.

Spearman’s Rho The hypothesis predicts a correlation

between two co-variables.

The two sets of data are pairs of scores from one person or thing (they are related).

The data are ordinal or interval not nominal.

Mann-Whitney U The hypothesis predicts a difference

between two sets of data.

The two sets of data are from separate groups of participants (independent groups).

The data are ordinal or interval not nominal.

Wilcoxon T The hypothesis predicts a difference

between two sets of data.

The two sets of data are pairs of scores from one person or a matched-pair (e.g. related).

The data are ordinal or interval not nominal.

Qualitative DataEXPLANATION ONE: DEFINITIONS

Quantitative Data – This is data which represents how much, how long or how many there are of something. It is presented in a numerical form.

Qualitative Data – This is any data which is not presented in a numerical form. It is normally gathered from un-structured interviews/questionnaires, unstructured observations and case studies.

EXPLANATION TWO: QUALITATIVE DATA ANALYSIS

There are many ways of analysing qualitative data, including; discourse analysis (studying written or oral discourses), ethnography (observing people in their natural environments) and IPA (understanding how people make sense of their own experiences).

Coding – This is a way of identifying categories, themes, phrases or keywords that may be found in any set of data. This is done to help refine the researcher’s findings, and help them be more objective in their approach.

The categories, themes, phrases and keywords are selected by one of two approaches;

Thematic Analysis (Top-Down Approach) – Codes represent ideas and concepts from an existing theory/explanation (e.g. clinical characteristics).

Grounded Theory (Bottom-Up Approach) – Codes and categories emerge from the research.

Summarising the Data – The behavioural categories can later be used to summarise the data. For example, the categories or themes may be listed, or examples represented using quotes from participants. It is also possible to create quantitative data by counting the frequency of occurrences within each category.

EXPLANATION THREE: VALIDITY AND REFLEXIVITY

The traditional approach in psychology claims that there is one real world, and quantitative research seeks to discover that reality – validity is a measure of the extent to which that has been achieved.

The qualitative approach denies the existence of any one world, claiming each individuals perspective is reality. Qualitative researchers acknowledge the need for reflexivity (the recognition that a researcher’s attitudes have an influence on the research they conduct). Validity of this research may be demonstrated using triangulation (comparing the results from a variety of studies of the same thing or person).