Research Methods. Flashcards
Experimental method:
Involves the manipulation of an independent variable to measure the effect on the dependent variable. Experiments may be laboratory, field, natural or quasi.
Aim:
A general statement of what the researcher intends to investigate.
Hypothesis:
A clear, precise, testable statement that states the relationship between the variables to be investigated.
Directional hypothesis:
States the direction or the difference or relationship.
Non-directional hypothesis:
Does not state the expected direction.
Variables:
Any ‘thing’ that can vary or change within an investigation. Generally used in experiments to determine if changes in one thing result in changes in another.
Independent variable:
Some aspect of the experimental situation that is manipulated by the researcher- or changes naturally- so the effect on the DV can be measured.
Dependent varibale:
The variable that is measured by the researcher. Any effect on the DV can be measured.
Operationalisation:
Clearly defining variables in terms of how they can be measured.
Extraneous variables:
Any variable, other than the IV, that may have an effect on the DV if it is not controlled. Essentially nuisance variables that do not vary systematically systematically with the IV.
Confounding variables:
Any variable, other than the IV, that may have affected the DV so we cannot be sure of the true source of changes to the DV. They vary systematically with the iV.
Demand characteristics:
Any cue from the researcher in the research situation that may be interpreted by participants as revealing the purpose of the investigation. May lead to p/pants changing their behaviour within the research situation.
Investigator effects:
Any effect of the investigator’s behaviour (conscious or unconscious) on the research outcome (DV). This may include everything from the design of the study to the selection of, interaction with, the p/pants during research processes.
Randomisation:
The use of chance in order to control for the effects of bias when designing materials and deciding the order of conditions.
Standarisation:
Using exactly the same formalised procedures and instructions for all p/pants in a research study.
Experimental design:
The different ways in which the testing of p/pants can be organised in relation to the experimental conditions.
Independent groups design:
P/pants are allocated to different groups where each group represents one experimental condition.
Repeated measures:
All p/pants take part in all conditions of the experiment.
Matched pairs design:
Pairs of p/pants are first matched on some variables that may affect the DV. Then one member of the pair is assigned to condition A and the other condition B.
Random allocation:
An attempt to control for p/pant variables in an independent groups design which ensures that one p/pants has the same chance of being in one condition as any other.
Counterbalancing:
An attempt to control for the effects of order in a repeated measures design: half the p/pants experience the condition in one order and the other half in the opposite order.
Laboratory experiment:
An experiment that takes place in a controlled environment within which the researcher manipulates the IV and records the effect on the DV while remaining in strict control of extraneous variables.
Lab experiments: Strengths-
-High control over extraneous variables.
-High internal validity (more certain about cause and effect) ^
-Replicability is higher due to the controlled environment.
Lab experiments: Limitations-
-Lack generalisability, labs are artificial.
-Low external validity ^
-Demand characteristics as p/pants know they are being tested.
-Mundane realism as the tasks may not reflect real life.
Field experiment:
An experiment that takes place in a natural setting within which the researcher manipulates the IV and records the effect on the DV.
Field experiments: Strengths-
-Higher mundane realism
-High external validity as p/pants may not know they are being studied.
Field experiments: Limitations-
-Loss of control over extraneous variables.
-Precise replication is not possible.
-Ethical issues is p/pants are unaware it is happening they cannot consent and the study may invade privacy.
Natural experiment:
An experiment where the change in the IV is not brought about by the researcher but would have happened even if the researcher had not been there. The researcher records the effects on the DV.
Natural experiments: Strength-
-High external validity because they study real life.
Natural experiments: Limitations-
-Natural event may occur rarely, reducing the opportunities for research.This may also limit generalisability.
-P/pants may not be randomly allocated to experimental conditions: the researcher may be less suer whether the IV affected the DV.
Quasi-experiment:
A study that is almost an experiment but lacks key ingredients. The IV has not been determined by anyone- the ‘variables’ simply exist, such as being old or young. Strictly speaking this is not an experiment.
Quasi-experiments: Strengths-
-Controlled conditions, reducing extraneous variables.
Quasi-experiments: Limitations-
-Cannot randomly allocate p/pants to conditions and therefore there may be confounding variables.
Population:
A group of people who are the focus of the researcher’s interest, from which a smaller sample size is drawn.
Sample:
A group of people who tale part in a research investigation. The sample is drawn from a (target) population and is presumed to be representative of that population.
Sampling techniques:
The method used to select people from the population.
Bias:
In the context of sampling, when certain groups may be over or under-represented within the sample selected. This limits the extent to which generalisations can be made to the target population.
Generalisation:
The extent to which findings and conclusions from a particular investigation can be broadly applied to the population. This is made possible if the sample of p/pants is representative of the population targeted.
Random sampling:
Complete list of all members, all names are assigned a number, use a randomiser to select p/pants.
Systematic sampling:
Every nth member of the target population is selected .
Stratified sampling:
Create a list each p/pant has to meet in requirements, they are then selected randomly to make up the required amount of p/pants per strata.
Opportunity sampling:
Select anyone who is willing and available, often used when the target population is difficult to obtain.
Volunteer sampling:
P/pants selecting themselves to be part of the sample.
Random sample- Evaluation
-Free from researcher bias.
-Difficult and time consuming.
-May get an unrepresentative sample
-Selected p/pants may refuse to take part.
Stratified sample- Evaluation
-Avoids researcher bias.
-Representative sample.
-Generalisable findings are possible.
-Complete representation of target population is not possible.
Systematic sample- Evaluation
-Avoids researcher bias.
-Fairly representative.
Opportunity sampling- Evaluation
-Convenient
-Sample is unrepresentative to target population and cannot be generalised.
-Researcher bias is possible as they choose p/pants.
Volunteer sample- Evaluation
-Easy
-Volunteer bias is a problem (they may all be helpful, keen, curious)
Ethical issues:
When a conflict exists between the rights of participants in research studies and the goals of research to produce authentic, valid and worthwhile data.
BPS code of ethics:
A quasi-legal document produced by the British Psychological Society that instructs psychologists in the UK about what behaviour is and is not acceptable when dealing with participants. It is built around four major principles: respect, competence, responsibility and integrity.
8 Ethics:
-Informed consent
-Deception
-Protection of p/pants.
-Confidentiality
-General
-Withdraw data
-Debrief
-Peer review
Pilot studies:
A small-scale version of an investigation that takes place before the real investigation is conducted.
Single-blind procedure:
An attempt to control demand characteristics by withholding information from p/pants such as the aim.
Double-blind procedure:
The p/pants and researcher does not know the aims of the investigation.
Naturalistic observation:
Watching and recording behaviour in the setting within which it would normally occur.
Controlled observation:
Watching and recording behaviour within a structured environment.
Covert observation:
P/pants’ behaviour is watched and recorded without their knowledge or consent.
Overt observation:
P/pants’ behaviour is watched/recorded with their consent.
Participant observation:
The researcher becomes a member of the group whose behaviour they are watching.
Non-participant observation:
The researcher remains outside if the group whose behaviour they are watching.
Naturalistic observations- Evaluations:
+External validity
+Generalisable
-Replicability
-Extraneous variables.
Controlled observations-
Evaluations:
-Not easily applies to real life
+Lack of extraneous variables.
Covert observations-
Evaluations:
+No demand characteristics
+Validity
-Ethics
Overt observations-
Evaluations:
+Ethical
-Demand characteristics
Participant observation-
Evaluation:
+Insight into experiment- validity
-Lose objectivity
Non-participant observation-
Evaluation:
+less danger of losing objective
-Lose insight would have got from p/pant observation.
Behavioural categories-
When a target behaviour is broken up into components that are observable and measurable.
Event sampling-
A target behaviour or event is first established then the researcher records this event every time it occurs.
Time sampling-
A target individual or group is first established then the researcher records their behaviour in a fixed time frame.
Structured vs unstructured observations:
Structured:
-Easier and systematic
-Quantitative data
Unstructured:
-Qualitative data
-Harder to record and analyse
-Detailed
-Observer bias
Self-report technique:
Any method in which a person is asked to state or explain their own feelings, opinions, behaviours and/or experiences related to a given topic.
Questionnaire:
A set of written questions used to access a persons’ thoughts and/or experiences.
Interview:
A live encounter where one person asks a set of questions to assess an interviewee’s thoughts/experiences.
Open questions:
Does not have a fixed range of answers. Qualitative data.
Closed questions:
Offers a fixed number of responses. Quantitative data.
Structured interview:
Pre-determined set of questions that are asked in a fixed order.
Unstructured interview:
Similar to a conversation. General aim and topic that will be discussed.
Semi-structured interview:
Pre-determined list of questions but interviewers may veer off.
Questionnaire- Evaluations:
+Cost effective
+Can gather a lot of data quickly
+Low effort, researcher doesn’t have to be there.
+ Easy to analyse data
-Could be untruthful
-Demand characteristics: social desirability bias
Interviews- Evaluations:
Structured:
+Replicability
+Reduces differences between interviewers
-Cannot deviate
Open questions-
Questions for which there is no fixed choice of response.
Closed questions-
Questions for which there is a fixed choice of response.
Interview schedules-
A list of questions that the interviewer intends to cover.
Likert scales-
The respondent indicates their agreement with a statement using a scale of usually five points.
Rating scales-
Gets respondents to identify a value that represents their strength of feeling about a particular topic.
Fixed choice option-
Includes a list of possible options and respondents are required to indicate those that apply to them.
Correlation-
A mathematical technique in which a researcher investigates an association between two variables, called co-variables.
Co-variables-
The variables investigated within a condition. Referred to as independent and dependent variables because a correlation investigates the association between the variable.
Positive correlation-
As one co-variable increases so does the other.
Negative correlation-
As one co-variable increases the other decreases.
Zero correlation-
There is no relationship between the co-variables.
Correlations: Strengths-
-Useful tool for research
-Precise, quantifiable measure of the relation between co-variables.
-Quick and economical
-No need for manipulation of variables or controlled environment
-Not time consuming.
Correlations: Weakness-
-Says if related not why
-No cause and effect
-May be an intervening variable other than co-variables.
^Correlations can be misinterpreted.
Qualitative data-
Data expressed in words and non-numerical.
Quantitative data-
Data that can be counted, usually given as numbers.
Primary data-
Data obtained by the researcher for the purpose of a project.S
Secondary data-
Information that has already been collected by someone else so pre-dates the current research project.
Meta-analysis-
The process of combining from a number of studies of a particular topic to provide an overall view.
Qualitative data: Evaluation-
+ More detail
+ External validity
- Difficult to analyse
- Patterns and comparisons are hard to identify.
- Conclusions rely on subjective interpretations.
Quantitative data: Evaluation-
+ Simple to analyse
+ Easy for comparisons
+ Less open to bias
- May not represent real-life
- Less detailed
Primary data: Evaluation-
+ Authentic
+ Can be designed to fit the information the researcher requires.
- Time and effort
- Planning, preparation and resources
Secondary data: Evaluation-
+ Inexpensive
+ Easily accessed
+ Desired information already exists so no need for primary data
- May not be high quality and accuracy
- Out dated or incomplete
Descriptive statistics-
Use of graphs, tables and summary statistics to identify trends and analyse sets of data.
Measures of central tendency-
Averages that give us information about the most typical values of data.
Standard deviation-
A single value that tells us how far scores deviate from the mean.
Scattergram-
A type of graph that represents the strength and direction of a relationship between co-variables in a correlational analysis.
Bar chart-
A type of graph in which the frequency of each variable is represented by the heigh of the bars.
Normal distribution-
A symmetrical spread of frequency data that forms a bell-shaped pattern. The mean, median and mode are all located at the highest peak.
Skewed distribution-
A spread of frequency data that is not symmetrical, where the data clusters to one end.
Positive skew-
A type of distribution in which the long tail is on the positive side of the peak and most of the dstribution is concentrated on the left.
Negative skew-
A type of distribution in which the long tail is on the negative side of the peak and most of the distribution is concentrated on the right.
Statistical testing-
Provides a way of determining whether hypotheses should be accepted or rejected.
Sign test-
A statistical test used to analyse the difference in scores between related items.
Accepted level of probability-
0.05/ 5%
Peer review-
The assessment of scientific work by others who are specialists in the same field to ensure that my research intended for publication is of high quality.
Peer review: Evaluation-
-Anonymity, reviewers may criticise work due to being unknown as it is a competitive field.
-Publication bias, not publishing research that did not meet their criteria.
-Especially critical of work that does not meet their own views/ research.
Content analysis-
Analysing the content of secondary data by creating a code before and sample method (eg. every second page & tally number of stereotypes)
Thematic analysis-
Converts qualitative data into quantitative by creating a category/code afterwards and tallying the number of times they appear in the data.
Inter-observer reliablilIty-
Another observer repeats the test and compares their results with yours to see if you have high (1) or low agreement (0), this is a kappa score.
Test-retest-
Giving the same group of p/pants the same test at a different time and assessing the score similarities.
Standarisation-
To ensure that each procedure is robust and repeated consistently across trials. This will improve reliability.
Ecological validity-
The ability to generalise the research results to different environments and achieve the same results.
Mundane relaism-
How realistic are the tasks to the real world.
Temporal validity-
The ability for the research results to be generalised to different time periods.
Population validity-
Can the research results be generalised to other samples of p/pants.
Concurrent validity-
To compare your research results to other similar results in the field and assessing if they are similar findings
Face validity-
The extent in which the test measures what it claims to measure.
5 features of a science-
Empirical methods-observable/quantitative data.
Objectivity- no bias/opinions.
Replicability
Theory construction-general laws/principles can be made.
Hypothesis testing- test + refine/ theory + test.
Nominal data-
Named categories
Ordinal data-
Data that can be ordered.
Interval data-
Data with equal measurements in-between each value and that can go below 0.
Alternative hypothesis-
A testable statement about the relationship/difference/association between 2+ variables.
Null hypothesis-
An assumption that there is no relationship/ difference/ association. Nothing is going on. We aim to reject our null hypothesis.
Type 1 error-
False positive. I’ve rejected the null hypothesis when i should have accepted it.
Type 2 error-
You fail to reject the null hypothesis and believe there isn’t a negative effect when there is one.
Repeated measures-
All p/pants do each condition. this can cause order effect so we need to counterbalance (do them out of order).
Independent experimental design-
Separate groups do separate conditions and we need to randomly allocate p/pants to groups.
Matched pairs design-
2 groups of p/pants who are matched on a characteristic, typically the DV. Best to conduct a pilot study to consider which variables need controlling.
Directional hypothesis-
Hypothesis predicts the direction of the result (X will have a positive effect on Y)
Non-directional-
My hypothesis states there is a difference but doesn’t state which way.
One tailed hypothesis-
Using a directional hypothesis with past studies to back your predicitions.
Two tailed hypothesis-
Non-directional hypothesis.
Calculated value-
The number they give you in the exam.
Critical value table-
The table you plot the score into.
Sign test-
Nominal data
Testing difference (related)
Repeated measures/ matched pairs.
Wilcoxon-
Ordinal (rank or scale)
Testing difference (related)
Repeated measures/matched pairs.
Related t-test (parametric)
Interval
Testing difference (related)
Matched pairs/ repeated measures.
Chi-squared-
Nominal
Testing difference (unrelated)
Independent groups
OR
Nominal
Testing association or correlation.
Spearman’s rho
Ordinal
Testing association or correlation
Mann-Whitney
Ordinal
Testing difference (unrelated)
Independent groups
Unrelated t-test (parametric)
Interval
Testing difference (unrelated)
Independent groups
Pearson’s R (parametric)
Interval
Testing association or correlation.
