Psychology A2 - Research methods
Applicaton of scientific method in psychology. Designing psychological investigations. Data analysis and reporting on investigations.
- Created by: Emily
- Created on: 11-12-11 17:36
Scientific method - Science - A01
- Major features of science
Empiricism
Objectivity
Reliability
Control
Theory construction
- Scientific process
Induction - Reasoning from particular to general
Deduction - Reasoning from general to particular
Scientific method - Science - A02
- Commentary - is psychology scientific?
Scientific research is desirable
Psychology share the goals of science
Kuhn - no single paradigm
Lack of objectivity and control leads to experimenter bias and demand characteristics
- Commentary - Are goals of science appropriate?
Nomothetic Vs Idiographic
- Synoptic links
Scientific approach is - Reductionist - reduces complex phenomena to simple ones. Determinist - Searches for causal relationships
Scientific method - validating knowledge - A01
- Peer review
Serves three main purposes: Allocation of research finding, Publication in scientific journals, research assesment excercise
Research published on the internet requires new solutions
Scientific method - validating knowledge - A02
- Commentary
May be unachievable ideal
Anonymity allows honesty and objectivity
Publication bias favours positive results
MAy lead to preservation of the status quo
Scientific method - validating knowledge - A01
- Conventions of scientific reporting
Abstract - summary of study
Introduction/aim - literature review and research intentions
Method - procedures and design of study
Results - descriptive and inferential statistics
Discussion - outcomes and implications of study
References
Scientific method - validating knowledge - A02
- Synoptic links
Some changes in science are not logical changes but represent a shift in perspective (Paradigm shift)
Burt research - an example of scientific fraud
Designing investigations - Research methods - A01
- Experiments
IV varied to see effect on DV
Lab experiment - high on internal validity. Low on external validity
Field Experiment - More natural environment but more issues of control than lab. experiment
Natural experiment - Uses naturally occuring IVs but cannot conclude causality
Experimental design: Repeated measures, independent groups, matched pairs
Scientific method - validating knowledge - A01
- Self-report methods
Questionnaires and interviews
Structured interviews - more easily repeated
Unstructured interviews - Questions that evolve are dependent on answers given
May involve open (respondent provides own answer) or closed (Respondent chooses specific answer) questions
Main problem: Social desirability bias
Scientific method - validating knowledge - A01
- Observational studies
Observing behaviour through behavioural categories
Sampling methods - Time and Event sampling
Open to subjective bias - Observations affected by expectations
Scientific method - validating knowledge - A01
- Correlational analysis
Concerned with relationship between two variables
Does not demonstrate causality
Other variables may influence any measured relationship
Scientific method - validating knowledge - A01
- Case studies
Detailed study of individual, institution or event
Generally longitudinal following individual or group over time
Allows study of complex interaction of many variables
Difficult to generalise from specific cases
Scientific method - Design issues - A01
- Relaibility
Experimental research - allows for replication of study
Observations - inter-observer reliability can be improved through training
self-report - internal reliability (split half) and external reliability (test - retest)
- Validity
Internal validity - does study test what it was indended to test?
External validity - can results be generalised to other situations and people?
Lab. experiments not necessarily low in external validity
If low in mundane realism, reduces generalisability of findings
A01 - continues
In observations, internal validity affected by observer bias
Self-report techiques, issues of face and concurrent validity
- Sampling techniques
Opportunity - Most easily available participants
Volunteer - E.g. through adverts but subject to bias
Random - All memebers of target population much have equal chance of selection
Stratified and quota - Different subgroups within sample, leads to more representative sample
Snowball - researcher directed to other similar potential participants
Scientific method - Ethics - A01
- Ethical issues with humans
Informed consent and Deception
Harm - What constitues too much?
Scientific method - Ethics - A01
- Code of conduct
Respect for worth and dignity of participants
Right to privacy, confidentiality, informed consent and right to withdraw
Intentional deception only acceptable in some circumstances
Competence - retaining high standards
Protection from harm and debriefing
Integrity - being honest and accurate in reporting
Use of ethical guidelines in conjunction with ethical committees
Socially sensitive research - potential social consequences for participants
Scientific method - Ethics - A01
- Ethical issues with non-humans
Reasons for animals use - Offers opportunity for greater control and objectivity, can't use humans, physiological similarities
Moral issues - sentience (Experience pain and emotions)
Specieism - form of discrimination against non-human species
Animal rights - regan (1984) no animal research is acceptable
Do animals have rights if they have no responsibilities?
Animal research subject to strict legislation (animals act; BPS guidelines)
The 3Rs: Reduction, Replacement and Refinement
Data analysis - Probability and significance - A01
- Probability and significance
Probability = likelihood that a pattern of results could arise by chance
If probability extremely unlikely, then result is statistically significant
Inferential tests determine whether chance or real trend in data
Probability levels represent acceptable level of risk (e.g. p<=0.05) of making a type 1 error
More important research, more stringent significance levels
Type 1 error = null hypothesis rejected when true.
Type 2 error = null hypothesis accepted when false
Data analysis - Probability and significance - A01
- Inferential tests
Different research designs require different tests
Different tests for different levels of measurement (Nominal, ordinal, interval, ratio)
Tests yield observed values and then compared to critical values to determine significance level
One-tailed test = directional hypothesis
Two tailed test = non - directional hypothesis
Data analysis - Inferential tests - A01
- Spearmans Rho
Hypothesis predicts correlation between two variables. Each person is measured on both varibles. Data is at least ordinal (i.e. not nominal)
- Chi - square
Hypothesis predicts differences between two conditions or association between two variables. Data is independent. Data in frequencies (Nominal). Expected frequencies in each cell must not fall below 5.
- Mann-whitney U
Hypothesis predicts difference between two sets of data. Indepedent groups design. Data at least ordinal (i.e. Nominal)
- Wilcoxon T
Hypothesis predicts differences between two sets of data. Related design (repeated measures or matched pairs). Data at least ordinal (i.e. not nominal)
Data analysis - Descriptive statistics - A01
- Central tendency
Indicates typical or average score
Mean = sum of all scores divided by number of scores - Unrepresentative if extreme scores
Median = Middle value in ordered list of scores - not affected but extreme scores but not as sensitive as meal
Mode = most common value - not useful if there are many modes in a set of scores
Data analysis - Descriptive statistics - A01
- Measures of dispersion
Indicate spread of scores
Range = difference between highest and lowest score - not representative if extreme score
Standard deviation = spread of data around mean. - Precise measure but influence of extreme scores not taken into account
Data analysis - Descriptive statistics - A01
- Graphs
Bar chart = illustration of frequency, height of bar represents frequency
Scatter gram = illustration of correlation, suitable for correlational data. Indicates strength of correlation and direction (positive or negative)
Data analysis - Qualitative data - A01
- Key points
Quantitative methods not relevant to 'real life'
Qualitative represents world as seen by individual
Emphasises collection of subjective information from participant
Data sets tend to be large
Qualitative data cannot be reduced to numbers
Can be examined for themes
Data analysis - Qualitative data - A01
- Methods of analysis
Coding using top-down approach (Thematic analysis) = codes represent ideas/themes from existing theory
Coding used bottom-top approach (grounded theory) = codes emerge from data
Behavioural categories used to summarise data
Reflexivity indicates attitudes and biases of researcher
Validity demonstrated by triangulation
Reliability checked by inter-rater reliability
Data analysis - Qualitative data - A01
- Quantitative Vs Qualitative
Quantitative - easy to analysis, produces neat conclusions, but - oversimplifies reality and human experience
Qualitative - represents true complexities of behaviour through rich detail of thoughts, feelings etc.. But - more difficult to detect patterns and subject to bias of subjectivity.
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