Research Methods

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  • Created by: GSHQ
  • Created on: 20-02-20 05:06

Volunteer Sampling

Volunteer (self-selected sample) sampling -> Participants volunteer themselves after seeing an advertisement for the experiment.

  • Access to a variety of participants – more likely to get a representative sample
  • Relatively cheap
  • Fast to find participants
  • Participants more likely to be co-operative
  • May be the only way to get a large enough sample
  • Good for when you do not have the information about who the relevant groups are
  • Likely to be a biased sample (volunteer bias) as there may be a certain type of person who volunteers e.g. students
  • The sample may be different to target population due to volunteering (may have a keen interest in topic/ may have less to do than others)
  • May try to be seen as socially acceptable/ show demand characteristics.
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Opportunity Sampling

Opportunity sampling -> Samples collected by asking individuals who are available at the time and fit the criteria you’re looking for. Participants are chosen as they are convenient; may have volunteered or may be familiar with the researcher.

  • Quick- no time spent planning or using sophisticated systems for selection
  • Cheap
  • Non-representative sample likely so it can be hard to generalise findings
  • May try to be seen as socially acceptable/ show demand characteristics
  • Researcher bias when selecting
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Random Sampling

Random sampling -> Every member of the population has an equal chance of being chosen. PPs are chosen mathematically using chance e.g. electoral register

 

  • Avoids bias as the researcher has no control who’s chosen
  • The law of probability says that the researcher will normally get a representative sample

 

  • Time-consuming- all potential PPs need to be identified  before a sample can be drawn
  • Small chance that a ‘freak’ sample may be drawn- unrepresentative
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Hypothesis definitions

Experiments -> a situation looking at cause & effect and involves the manipulation of a variable.

Hypothesis -> a statement that can be tested to see if it is true or not.

Research -> general prediction, not enough information to base an investigation on.

One-tailed hypothesis -> There will be an increase/decrease in the dependent variable caused by the independent variable.

Two-tailed hypothesis -> There will be a difference in the dependent variable caused by the independent variable.

Null hypothesis -> Base hypothesis that predicts no difference in results on the dependent variable by the independent variable.

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Variables definitions

Variables -> anything in an experiment which can come in different forms or in different values

Independent variable -> is the variable the experimenter changes or controls and is assumed to have a direct effect on the dependent variable.

Dependent variable -> is the variable being tested and measured in an experiment and is 'dependent' on the independent variable.

Operational variables -> (or operationalizing definitions) refer to how you will define and measure a specific variable as it is used in your study. When measuring aggression what would you consider to be aggression? Criteria needed.

Independent and Dependent Variables

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Types of Bias

Demand Characteristics -> Participants try to make sense of the study they are in and adjust their behaviour accordingly. They behave in the way they believe they should behave not the way they would normally act.

Social Desirability -> where a respondent gives an answer that is not necessarily true to look good in front of others.

Experimenter Bias -> the experimenters’ expectations or study influences the study. The experimenter may subtly communicate their expectations to PPs.

Observer Bias -> The presence of an observer may change the behaviour of those being observed.

 

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Validity

Validity -> Validity refers to a test's ability to measure what it is supposed to measure.

Internal validity -> whether a study’s results were really due to variables suggested by researchers when tested using their experiment methodology

Face validity -> whether the measuring tool appears to be doing what it should

Concurrent validity -> New measure test scores are correlated with those from an established test. If different tests come out with the same results.

External validity -> whether the results can be generalised if conducted in a different environment or using different participants

Ecological validity -> whether the behaviour measured in a test or method is representative of behaviour that naturally occurs.

Population validity -> how well the sample can be used to extrapolate the results to the population as a whole.

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Reliability

Reliability -> the consistency of findings; how much findings can be trusted. Reliability is the ability of a test or assessment to yield the same results when administered repeatedly. 

Internal reliability -> how consistently a method measures within itself. Whether the different questions in a questionnaire are all measuring the same thing.

Inter-rater reliability -> consistency between different researchers working on the same study in their findings/observations. There should be a high positive correlation between the scores of the different researchers.

Intra-rater rater reliability -> consistency of a researchers’ behaviour. A researcher should produce similar test results, or make similar observations or carry out interviews in the same way on more than 1 occasion 

External reliability -> how consistently a method measures over time when repeated

Test re-test method -> Participants take the same test on different occasions- a high correlation between test scores indicates the test has good reliability.

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Laboratory Experiments

Laboratory Experiments -> A laboratory experiment is an experiment conducted under highly controlled conditions. The variable which is being manipulated by the researcher is called the independent variable and the dependent variable is the change in behaviour measured by the researcher.

 

  • Easily replicated
  • High levels of control over extraneous variables
  • Reliably establishes cause and effect

 

  • Lacks ecological validity (artificial setting)
  • Participants are likely to know they are taking part resulting in demand characteristics.
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Field Experiments

Field experiments -> are done in an every day (i.e. real life) environment of the participants. The experimenter still manipulates the independent variable, but in a real-life setting (so cannot really control extraneous variables).

 

  • Easier to generalise findings
  • Participants are in a natural environment, high ecological validity
  • Independent variable can be manipulated

 

  • Time-consuming
  • Confounding environmental variables mean that it less reliably establishes cause and effect.
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Quasi Experiments

Quasi-experiment -> Quasi-experiments contain a naturally occurring IV. However, in a quasi-experiment, the naturally occurring IV is a difference between people that already exists (i.e. gender, age). The researcher examines the effect of this variable on the dependent variable (DV).

 

  • Participants are in a natural environment- high ecological validity
  • More ethical as Participants aren’t manipulated as much

 

  • Confounding environmental variables- less reliable cause and effect
  • Have to wait for IV to occur naturally or for Participants with characteristics of the IV to be available
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Repeated Measures Design

Repeated measures design -> Repeated measures design is an experimental design where the same participants take part in each condition of the experiment. This means that each condition of the experiment uses the same group of participants.

  • As the same participants are used in each condition, participant variables (i.e., individual differences) are reduced.
  • Fewer people are needed
  • Greater chance of demand characteristics as they go through the procedure more than once (they figure out the purpose of the experiment)
  • Order effects need to be controlled

The extraneous variable ->> Participant variables, individual differences participants, intelligence, age and gender, etc. are controlled using repeated measures design and matched pairs design.

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Matched Pairs Design

Matched pairs design -> Participants with similar characteristics which might affect performance are paired up and one member of the pair is randomly assigned to the experimental group and the other to the control group. Each condition uses different but similar participants.

  • Reduces participant variables because the researcher has tried to pair up the participants so that each condition has people with similar abilities and characteristics.
  • Avoids order effects, and so counterbalancing is not necessary.
  • If one participant drops out you lose the data of two participants
  • Can be very time-consuming trying to find closely matched pairs.
  • Impossible to match people exactly, unless they are identical twins

The extraneous variable ->> Participant variables, individual differences participants, intelligence, age and gender, etc. are controlled using repeated measures design and matched pairs design.

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Controlling Variables

Situational variables -> outside influences on the experiment e.g. weather, time of day, noise, order effects (fatigue, boredom and fatigue). 

Standardisation -> all instructions given to PP, procedures followed must be identical for each PP.

Counterbalancing -> Researcher changes the order of tasks for each PP or uses ABBA technique (first half tested with alcohol first then no alcohol, the other half with no alcohol first then alcohol) Controls for order effects

Randomisation -> Order of tasks is decided on the toss of a coin or another method of selection (controls for order effect)

Investigator effects -> Any effect of the investigator’s behaviour (conscious or unconscious) on the research outcome (the DV). This may include everything from the design of the study to the selection of, and interaction with participants within the research situation.

Double bind -> Neither individuals nor researchers know who belongs to which condition preventing researcher influence.

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Independent Measures

Independent measures -> Different participants used for each condition of the experiment

  • No order effects as each PP only participate in an experiment once
  • Demand characteristics reduced as each PP only participates in experiment once
  • Individual Differences may influence results

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Fatigue effect, boredom and practise effects -> Carrying out a task repeatedly leads to changes in performance

Fatigue effect -> deterioration of performance across conditions as PPs become tired and bored

Practise effect -> improvement across conditions through familiarity with the task/ environment

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Controlled Observation

Controlled Observation -> The researcher decides where the observation will take place, at what time, with which participants, in what circumstances and uses a standardised procedure. Participants are randomly allocated to each independent variable group. Behaviour is coded so that the data collected can be easily counted and turned into statistics.

  • can be easily replicated by other researchers by using the same methodology. This means it is easy to test for reliability.
  • The data obtained is easier and quicker to analyze as it is quantitative (i.e. numerical) - making this a less time-consuming method compared to naturalistic observations.
  • Controlled observations are fairly quick to conduct which means that many observations can take place within a short amount of time. A large sample can be obtained resulting in the findings being representative and having the ability to be generalized to a large population
  • Controlled observations can lack validity due to demand characteristics. When participants know they are being watched they may act differently.
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Naturalistic Observation

Naturalistic observation -> Involves observing involves studying the spontaneous behaviour of participants in natural surroundings. The researcher simply records what they see the way they want to.

  • By being able to observe the flow of behaviour in its own setting studies have greater ecological validity.
  • Like case studies, naturalistic observation is often used to generate new ideas. Because it gives the researcher the opportunity to study the total situation it often suggests avenues of enquiry not thought of before.
  • These observations are often conducted on a small scale and may lack a representative sample. This may result in the findings lacking the ability to be generalized to wider society. 
  • Natural observations are less reliable as other variables cannot be controlled. This makes it difficult to repeat the study in exactly the same way.
  • The researcher needs to be trained to be able to recognise aspects of a situation that are psychologically significant and worth further attention.
  • With observations, we do not have manipulations of variables (or control over extraneous variables) which means cause and effect relationships cannot be established.
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Covert and Overt Observation

Covert observation ->  Participant’s behaviour is watched and recorded without their knowledge and consent.

The participants are unaware that they are the focus of the experiment and their behaviour is observed in secret. In order for the observation to be ethical, the behaviour must be public and naturally occurring.

  • High validity because participants are unaware they are being watched. Eliminates the problem of participant reactivity.
  • ethical concerns as participants are not able to consent and may not wish to be observed.

Overt observation -> The participant's behaviour is watched and recorded with their knowledge and consent.

  • The participant knows that they are being observed and are thus able to give informed consent.
  • ethically acceptable but the knowledge the participants have that they are being observed may act as a significant influence on their behaviour.

 

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Observation Design

Structured -> the behaviour being measured is clearly defined and recorded each time it occurs.

  • Easy to analyse and assess what’s going on
  • Reliable
  • Researchers may miss interesting behaviours as not on the checklist
  • Open to researcher bias

Unstructured -> the researcher will write down everything they see.

  • Appropriate for the research is small scale, otherwise, the information will be overwhelming
  • Detailed notes on participants behaviour
  • The observer may miss things the participants do while making notes
  • Difficult to compare qualitative data
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Sampling

Event Sampling ->  A target behaviour or event is first established then the researchers record this event every time it occurs.

  • Catches all behaviours you are looking for as looking continuously
  • Useful when behaviours recorded only happens occasionally
  • Difficult to concentrate for long periods of time

Time Sampling -> A targeted individual or group is first established then the researcher records their behaviour in a fixed time frame, say: every 60 seconds.

  • More focused as only watching for short periods of time
  • Observations may not be representative
  • May miss interesting behaviours
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Quantitative Data

Quantitative data -> Data that can be counted, usually given as numbers.

  • the data is relatively simple to analyse.
  • Quantitative data can be interpreted with statistical analysis. Because that is scientific it increases the data's reliability
  • Useful for testing and validating already constructed theories.
  • Quantitative experiments do not take place in natural settings. Low external validity.
  • Poor knowledge of the application of statistical analysis may negatively affect analysis and subsequent interpretation
  • Large sample sizes are needed for more accurate analysis. Small scale quantitative studies may be less reliable because of the low quantity of data, affects generalisability to larger populations
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Qualitative Data

Qualitative data -> Data that is expressed in words and non-numerical (although qualitative data may be converted to numbers for the purpose of analysis).

  • much broader in scope and gives the participant the ability to develop their thoughts and feelings on the given subject - high external validity
  • close researcher involvement, the researcher gains an insider's view of the field. This allows the researcher to find issues that are often missed
  • Qualitative analysis allows for ambiguities/contradictions in the data, which are a reflection of social reality
  • Conclusions often rely on the subjective interpretation of the researcher and these may be subject to bias.
  • It is difficult to apply conventional standards of reliability and validity.
  • Lengthy to collect the data and analyze it.
  • Expert knowledge of an area is necessary to try to interpret qualitative data, especially with research on mental illnesses, diagnosing them.
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Participant and Non-Participant Observations

Participant observation -> The researcher becomes a member of the group whose behaviour they are watching and recording.

  • More natural arrangement- high ecological validity
  • Insight into the participant's behaviour, increasing the validity of the findings
  • Difficult to take notes whilst taking part
  • The researcher may become too involved and lose objectivity. the lines between being a researcher become blurred and can be referred to as going native.
  • Researcher bias - observer may influence participants behaviour

Non-participant observation -> The researcher remains outside of the group whose behaviour they are recording.

  • Researchers can take notes easily
  • Less influence on participant's behaviour, less chance of them going native
  • Loss of insight the researcher could have gained and can be too far removed from the people they are studying.
  • Less natural environment - more demand characteristics
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Questionnaire

Self-report techniques -> 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. Include interviews and Questionnaires

Questionnaires -> A set of written questions (sometimes referred to as ‘items’) used to assess a person’s thoughts and/or experiences.

  • Large sample questioned quickly
  • Large amounts of data collected about what people think and say they do
  • Efficient, the researcher doesn’t have to be there while completed
  • Social desirability, answering the way they want to be perceived instead of truthfully.
  • Untruthful responses to personal questions
  • Distortion to sampling frame; only those who can read or write can take part- affects generalizability
  • Postal surveys= low response rate- reduces validity as a less representative sample
  • May receive subjective interpretations to questions
  • Cannot be sure who completed it.
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Open and Closed Questions

Open Questions -> Allows respondent to freely express their opinion and view in their own words

  • Rich qualitative data- high validity
  • Respondent free to express what they really think
  • Qualitative data
  • Low reliability

Closed Questions -> Respondents must choose their answer from a fixed list of predetermined answers

  • Quantitative data, easily statistically analysed and compared
  • Reliable as can be easily repeated to obtain the same results
  • The respondents answer may not be represented in the choice- low validity
  • Loses richness of qualitative data
  • Unclear as to how the respondent understood the question

 

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Structured Interview

Structured interview -> Interviewer sticks to a strict list of questions and conducts the interviews in the exact same way (standardised procedure)

  • High intra-rater reliability
  • Standardised questions= high reliability
  • Specific info gathered
  • Easy to compare and analyse responses
  • Low validity as interviewees not free to expand on answers
  • Not a real insight into participants
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Unstructured Interview

Unstructured interview -> Interviewer has the freedom to vary questions and follow any line of enquiry

  • High validity- rich detailed responses
  • Can build a rapport- more likely to answer personal questions
  • Time-consuming
  • May not find all info needed
  • Difficult to analyse answers, the researcher must interpret answers
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Correlations

Correlation -> A relationship between 2 variables, participants provides data for both variables and a correlation shows strength and direction of a relationship

Positive correlation -> as the values of one co-variable increase, the values of the other co-variable increase (+1 coefficient)

Negative correlation -> as the values of one co-variable increase, the values of the other co-variable decrease (-1 coefficient)

No correlation -> There is no relationship between the 2 co-variables (0)

The correlation coefficient measures the strength and direction of a relationship

  • Can suggest ideas for experimental studies
  • Makes use of existing data, quick and easy to do
  • A little manipulation of behaviour- measures existing variables so high ecological validity
  • May be used when impractical or unethical to manipulate variables
  • Does not show cause and effect- possible 3rd variable influencing it
  • May lack internal or external validity
  • Can only be relied on if the way of collecting data for 2 variables was accurate
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Primary and Secondary Data

Primary data -> Information that has been obtained first-hand by the researcher for the purpose of a research project. In psychology, such data is often gathered directly from participants as part of an experiment, self-report or observation.

  • authentic data obtained for the purpose of the experiment obtained from the participants themselves 
  • Takes time and effort to produce compared to secondary data which can take minutes to produce

Secondary data -> Information that has already been collected by someone else and so pre-dates the current research project. In psychology, such data might include the work of other psychologists or government statistics.

  • inexpensive and can be accessed using minimal effort. 
  • lower quality data and accuracy
  • the data might be outdated, or incomplete
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Meta-Analysis

Meta-analysis -> ‘Research about research’, refers to the process of combining results from a number of studies on a particular topic to provide an overall view. This may involve a qualitative review of conclusions and/or a quantitative analysis of the results-producing an effect size.

consists of secondary data

  • Inconsistency if results across studies can be quantified. analyzed and corrected.
  • Hypothesis testing can be applied on summary estimates
  • Moderators can be included to explain variation between studies
  • The presence of publication bias can be investigated
  • Meta-analysis may discourage large definitive trials.
  • Increases the tendency to unwittingly mix different trails and ignore differences
  • Meta-analysis of several small studies may not predict the same results of a single large study
  • Sources of bias are not controlled by the method
  • A good meta-analysis of badly designed studies will result in bad statistics
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Measures of dispersion

Measures of dispersion -> the general term for any measures of the spread or variation in a set of scores.

Range -> A simple calculation of the dispersion in a set of scores which is worked out by subtracting the lowest score from the highest score and adding 1 as a mathematical correction

  • Easy to calculate
  • Unaffected by extreme values
  • Doesn’t indicate how widely or tightly spread of data is 

Standard deviation -> A sophisticated measure of dispersion in a set of scores. It tells us how much scores deviate from the mean by calculating the difference between the mean and each score. All the differences are added up and divided by the number of scores. This gives the variance. The standard deviation is the square root of the variance.

  • More precise than range as all data accounted for
  • Allows the researcher to know how much scores vary amongst themselves
  • Difficult to calculate
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Graphs

Scattergram -> A type of graph that represents the strength and direction of a relationship between co-variables in a correlational analysis. Depicts associations. The points correspond to the x and y co-variables

Bar chart -> A type of graph in which the frequency of each variable is represented by the height 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 (right) side of the peak and most of the distribution is concentrated to the left.

Negative skew -> A type of distribution in which the long tail is on the negative (left) side of the peak and most of the distribution is concentrated on the right.

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Peer Review

Peer review -> The assessment of scientific work by others who are specialists in the same field to ensure that any research intended for publication is of high quality.

Main aims of peer review:

  • To allocate research funding
  • To validate the quality and relevance of research
  • To suggest amendments or improvements
  • Peer review promotes and maintains high standards in research, which has implications for society and funding allocation so that it is assigned to high-quality research.
  • Helps to prevent scientific fraud, as submitted work is scrutinised.
  • It promotes the scientific process through the development and dissemination of accurate knowledge and contributes new knowledge to the field.
  • If anonymity is not maintained experts with a conflict of interest might not approve research to further their own reputation or career.
  • Contributes to the “file drawer effect” – as only statistically significant findings are published.  This means that findings that challenge existing understanding might be overlooked as they are not published
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Case Studies

Case studies -> an in-depth investigation, description and analysis of a single individual, group, institution or event.

Usually produce qualitative data. An in-depth history of the individuals or events is conducted using interview, observations and questionnaires, which may produce quantitative data as well.

Usually take place over a long period of time (longitudinal)

  • Provides detailed (rich qualitative) information.
  • Provides insight for further research.
  • Permitting investigation of otherwise impractical (or unethical) situations.
  • Can’t generalize the results to the wider population.
  • Researchers' own subjective feeling may influence the case study (researcher bias).
  • Difficult to replicate.
  • Time-consuming.
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Content Analysis

Content analysis -> a research technique that enables the indirect study of behaviour by examining communications that people produce, for example, in texts, emails, TV, film, and other media.

  • Offers a method to analyse a variety of forms of data including media and self-report methods so that insights into cultural trends and experiences can be understood.
  • Can report on data that may be copyrighted
  • The identification of suitable themes and codes is subjective and decided by the researcher alone, meaning that conclusions lack any scrutiny or objectivity

Coding -> the initial stage of a content analysis in which the communication to be studied is analysed by identifying each instance of the chosen categories (which may be words, sentences or phrases, etc.). Turning qualitative data into quantitative.

Thematic analysis ->  an inductive and qualitative approach to analysis that involves identifying implicit or explicit ideas within the data. Themes will often emerge once the data has been coded. Identifying patterns

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Ethical Issues

Ethical issues -> These arise when a conflict arises 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 (BPS) 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.

4 main components:

1. Informed Consent -> participants hsould be aware of the aims of the research, the procedures and their rights (the right to withdraw) and what their data will be used for.

2. Deception -> deliberately misleading participants or withholding information from participants any stage of the investigation. Decieved participants cannot give informed consent. Can be justified if the participant does not cause undue stress.

3. Protection from harm -> the participants should not be placed at any more risk that they would in their daily lives. Includes feeling inadequate or being placed under undue amounts of stress or pressure.

4. Privacy and confidentiality -> Particpants ahve the right to control information about themselves. This is the right to privacy. Confidentialtiy refers tot he right to have any personal data protected, under the law. Extends to the area the study took place such that institutions or geographical locations are not name.

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Ways of dealing with ethical issues

BPS code of conduct 

  • The British psychological societ (BPS) have devloped a set of ethical guidelines. Researchers that don't follow these guideline could lose their jobs.

Dealing with informed consent

  • Participants should be issued a consent letter or form detailing all relevant information that may effect their decsion to participate. For children under 16, a parent must sign.

Dealing with deception and protection from harm: debriefing

  • At the end of the study, participants should be given a full debrief where they are told the true aims of the investigation and details they were not supplied beofrehand, as well as the existence of other conditions.
  • Particpants need to be reminded oftheir right to withhold data and that their data will be protected.

Dealing with confidentiality

  • if personal detailsa re held they must be protected. Maintain anonymity of the participants.
  • Incase studies, initals are usually used when describing participants involved.
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