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





Theory construction

  • Scientific process

Induction - Reasoning from particular to general

Deduction - Reasoning from general to particular

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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

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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

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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

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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


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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

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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

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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

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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

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Scientific method - validating knowledge - A01

  • Correlational analysis

Concerned with relationship between two variables

Does not demonstrate causality

Other variables may influence any measured relationship

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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

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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

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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

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Scientific method - Ethics - A01

  • Ethical issues with humans

Informed consent and Deception

Harm - What constitues too much?

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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

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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

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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

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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

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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)

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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

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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

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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)

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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

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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

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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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