Research Methods In Psychology

Sampling is the process of selecting a representative group from the population under study.

A sample is the participants you select from a target population (the group you are interested in) to make generalizations about.

Representative means the extent to which a sample mirrors a researcher’s target population and reflects its characteristics.

Generalisability means the extent to which their findings can be applied to the larger population of which their sample was a part.

• Volunteer sample: where participants pick themselves through newspaper adverts, noticeboards or online.
• Opportunity sampling: also known as convenience sampling, uses people who are available at the time the study is carried out and willing to take part. It is based on convenience.
• Random sampling: when every person in the target population has an equal chance of being selected. An example of random sampling would be picking names out of a hat.
• Systematic sampling: when a system is used to select participants. Picking every Nth person from all possible participants. N = the number of people in the research population / the number of people needed for the sample.
• Stratified sampling: when you identify the subgroups and select participants in proportion to their occurrences.
• Snowball sampling: when researchers find a few participants, and then ask them to find participants themselves and so on.
• Quota sampling: when researchers will be told to ensure the sample fits certain quotas, for example they might be told to find 90 participants, with 30 of them being unemployed.

Operationalization means making variables measurable and quantifiable. We must operationalise variables so they can be tested.

We can’t directly measure ‘happiness’, but we can count how many times someone smiles in two hours.

Operationalizing a variable lets other researchers replicate the study. That matters, because replication is how findings get checked for reliability.

Extraneous variables are anything other than the independent variable that could affect the results.

Some are participant variables, like intelligence, gender, or age. Others are situational, like lighting or noise.

Demand characteristics are one such variable: participants work out a study’s aims and start behaving accordingly, often to be a “good participant” (Orne, 1962).

Critics of Milgram’s obedience research argued on exactly these grounds. Participants may have worked out the shocks were fake and administered them anyway, simply because that seemed to be what the study wanted.

Extraneous variables must be controlled so they cannot confound the results.

Random allocation or a matched pairs design reduces participant variables. Situational variables are controlled through standardised procedures that treat every participant identically.

Investigator Effects and Blinding

Researchers can bias a study too, without meaning to. Rosenthal (1966) called this investigator (experimenter) effect: a researcher’s own expectations can subtly change their tone, warmth, or treatment of participants, and so change the results.

In a classic demonstration, experimenters were falsely told their rats were bred to be “maze-bright” or “maze-dull.” The “maze-bright” rats then learned faster. Yet all the rats had been randomly allocated from the same population (Rosenthal & Fode, 1963). The experimenters’ own expectations had shaped how they treated the animals.

The same effect works on people.

When teachers were told that certain randomly chosen pupils were about to “bloom” academically, those pupils went on to show greater IQ gains than their classmates. It was a self-fulfilling prophecy, created purely by what the teachers had been told (Rosenthal & Jacobson, 1968).

The standard fix is blinding. In a single-blind study, participants do not know which condition they are in. A double-blind study also keeps the researcher blind to each participant’s condition, so neither side’s expectations can bias the result.

Experimental design refers to how participants are allocated to each condition of the independent variable, such as a control or experimental group.

• Independent design (between-groups design): each participant is selected for only one group. With the independent design, the most common way of deciding which participants go into which group is by means of randomization. 
• Matched participants design: each participant is selected for only one group, but the participants in the two groups are matched for some relevant factor or factors (e.g. ability; sex; age).
• Repeated measures design (within groups): each participant appears in both groups, so that there are
  exactly the same participants in each group.
• The main problem with the repeated measures design is that there may well be order effects. Their experiences during the experiment may change the participants in various ways.
• They may perform better when they appear in the second group because they have gained useful information about the experiment or about the task. On the other hand, they may perform less well on the second occasion because of tiredness or boredom.
• Counterbalancing is the best way of preventing order effects from disrupting the findings of an experiment, and involves ensuring that each condition is equally likely to be used first and second by the participants.

If we wish to compare two groups on a given independent variable, the two groups must not differ in any other important way. Otherwise, we cannot be sure the IV caused any difference we find.

Case studies are in-depth investigations of a single person, group, event, or community. They draw on a range of sources, including the person themselves and their family and friends.

Researchers may combine interviews, psychological tests, observations, and experiments. Case studies are usually longitudinal, following the individual or group over an extended period.

Some of the best-known case studies in psychology were carried out by Sigmund Freud. He investigated the private lives of his patients in detail, aiming to understand and help them overcome their illnesses.

Case studies provide rich qualitative data with high ecological validity. Generalising from them is hard, though. Each case has unique characteristics that may not apply to anyone else.

Correlation means association. It is a measure of how far two variables move together, one treated as the predictor and the other as the outcome.

A correlational study takes two measures from the same group of participants and checks how closely they are associated.

The predictor is simply whichever variable is used to predict the outcome. It need not be a cause.

Relationships between variables can be plotted on a graph or summarised as a correlation coefficient.

• If an increase in one variable tends to be associated with an increase in the other, then this is known as a positive correlation.
• If an increase in one variable tends to be associated with a decrease in the other, then this is known as a negative correlation.
• A zero correlation occurs when there is no relationship between variables.

After plotting the scattergraph, a statistical test of correlation, such as Spearman’s rho, confirms whether a significant relationship really exists between the two variables.

The test gives a score called a correlation coefficient, a number between −1 and +1 showing how strong the relationship is. Zero means no relationship at all.

The closer the score sits to either extreme, the stronger the relationship. A coefficient can be positive, such as 0.63, or negative, such as −0.63.

Interviews are commonly divided into two types: structured and unstructured.

• Structured interviews are formal. The interview situation is standardized as far as possible. Structured interviews are formal, like job interviews.

  A fixed, predetermined set of questions is put to every participant in the same order and in the same way. 

  Responses are recorded on a questionnaire, and the researcher presets the order and wording of questions, and sometimes the range of alternative answers.

  The interviewer stays within their role and maintains social distance from the interviewee.

• Unstructured interviews are informal, like casual conversations. A general conversation normally precedes them, and the researcher deliberately adopts an informal approach to break down social barriers.

  There are no set questions, and the participant can raise whatever topics he/she feels are relevant and ask them in their own way. Questions are posed about participants’ answers to the subject

  Unstructured interviews are most useful in qualitative research to analyze attitudes and values.

  Though they rarely provide a valid basis for generalization, their main advantage is that they enable the researcher to probe social actors’ subjective point of view. 

Questionnaire Method

Questionnaires can be thought of as a kind of written interview. They can be carried out face to face, by telephone, or post.

The choice of questions is important because of the need to avoid bias or ambiguity in the questions, ‘leading’ the respondent or causing offense.

• Open questions are designed to encourage a full, meaningful answer using the subject’s own knowledge and feelings. They provide insights into feelings, opinions, and understanding. Example: “How do you feel about that situation?”
• Closed questions can be answered with a simple “yes” or “no” or specific information, limiting the depth of response. They are useful for gathering specific facts or confirming details. Example: “Do you feel anxious in crowds?”
• Postal questionnaires seem to offer the opportunity of getting around the problem of interview bias by reducing the personal involvement of the researcher.

  Its other practical advantages are that it is cheaper than face-to-face interviews and can be used to contact many respondents scattered over a wide area relatively quickly.

Observations

• Covert observation is where the researcher doesn’t tell the participants they are being observed until after the study is complete. There could be ethical problems or deception and consent with this particular observation method.
• Overt observation is where a researcher tells the participants they are being observed and what they are being observed for.
• Controlled: behavior is observed under controlled laboratory conditions (e.g., Bandura’s Bobo doll study).
• Natural: Here, spontaneous behavior is recorded in a natural setting.
• Participant: Here, the observer has direct contact with the group of people they are observing. The researcher becomes a member of the group they are researching.  
• Non-participant (aka “fly on the wall): The researcher does not have direct contact with the people being observed. The observation of participants’ behavior is from a distance

This step matters. A pilot study is an initial run-through of the procedures on a few people before the full investigation begins. It saves time and, in some cases, money by catching flaws in the design early.

A pilot study can also help the researcher spot ambiguities or confusing wording in the information given to participants. It can catch task problems too.

Two problems a pilot study can catch are floor and ceiling effects. A task that is too hard produces a floor effect: none of the participants can complete it, so every score is low.

A task that is too easy produces the opposite, a ceiling effect, where almost everyone “hits the ceiling” with a near-perfect score.

Other times we want to track change instead. Longitudinal research gathers data from the same people repeatedly, over an extended period, as in studies of human development.

In cohort studies, participants share a common factor, such as age or occupation. This is one type of longitudinal design.

Mixed methods research combines more than one method in a single study. Doing so can strengthen validity.

Reliability is a measure of consistency, if a particular measurement is repeated and the same result is obtained then it is described as being reliable.

• Test-retest reliability:  assessing the same person on two different occasions which shows the extent to which the test produces the same answers.
• Inter-observer reliability: the extent to which there is an agreement between two or more observers.

Meta-analysis is a statistical procedure used to combine and synthesize findings from multiple independent studies to estimate the average effect size for a particular research question.

Meta-analysis goes beyond traditional narrative reviews by using statistical methods to integrate the results of several studies, leading to a more objective appraisal of the evidence.

This is done by looking through various databases, and then decisions are made about what studies are to be included/excluded.

• Strengths: Increases the conclusions’ validity as they’re based on a wider range.
• Weaknesses: Research designs in studies can vary, so they are not truly comparable.

A researcher submits an article to a journal, chosen for its audience or prestige. The journal then selects two or more experts in the same field to review it, unpaid.

The reviewers assess the study’s methods and design, the originality and validity of the findings, and the quality of its content, structure and language. Their feedback determines whether the article is accepted:

• Accepted as it is.
• Accepted with revisions the author must make.
• Revise and resubmit, sent back for a fresh review.
• Rejected, with no option to resubmit.

The editor makes the final decision, based on the reviewers’ comments.

Peer review keeps faulty data out of the public domain. It checks the validity of findings and the quality of the methodology, and it is used to assess university departments’ research ratings.

In practice, though, it has real problems.

• Slows publication: the review process can hold back new work for months.
• May suppress unusual findings: reviewers can reject work that challenges the field’s consensus, or a rival’s work.
• Cannot guarantee honesty: some doubt whether peer review reliably catches fraudulent research.
• Losing ground online: more research and commentary is published without formal peer review than before, though online communities increasingly review and critique work themselves.

• Qualitative data is non-numerical and is displayed in words. It is descriptive in nature. This type of data can come from things like interview transcripts or responses to open questionation. Open questions in questionnaires and accounts from observational studies collect qualitative data.
• Primary data is information obtained first-hand by the researcher specifically for the investigation they are conducting. Data collected by a researcher dealing directly with participants is primary data.
• Secondary data is information that already exists, having been collected by someone else or for a different purpose. E.g. meta-analysis: A type of secondary data analysis where researchers pool findings from multiple studies to create a single, overall conclusion.

Converting Qualitative Data to Quantitative Data

Sometimes researchers collect qualitative data and then convert it into numbers for analysis.

Content analysis quantifies qualitative content through coding or categorisation. It is a form of indirect observation, examining the artefacts, communications, or media that people produce.

It works by taking qualitative data, such as interview transcripts or diary extracts, and converting it into numerical form.

One way is to identify themes, words, phrases, or categories and count how often they appear. A simpler approach sorts scores into bands.

For example, mood scores could be grouped into ‘under 40′, ’40 to 60’, and ‘over 60’.

This gives a numerical result per participant or category. The method converts existing qualitative data; it does not involve collecting new data.

Validity means how well a piece of research actually measures what it sets out to, or how well it reflects the reality it claims to represent.

Validity is whether the observed effect is genuine and represents what is actually out there in the world.

• Internal validity is whether the change in the DV was really caused by the IV, rather than by confounding variables, demand characteristics, or investigator effects. Control, randomisation, and blinding all protect it.
• Concurrent validity is the extent to which a psychological measure relates to an existing similar measure and obtains close results. For example, a new intelligence test compared to an established test.
• Face validity: does the test measure what it’s supposed to measure ‘on the face of it’. This is done by ‘eyeballing’ the measuring or by passing it to an expert to check.
• Construct validity is whether a measure really captures the underlying concept it is meant to represent, rather than something else.
• Ecological validity is the extent to which findings from a research study can be generalized to other settings / real life.
• Temporal validity is the extent to which findings from a research study can be generalized to other historical times.

Features of Science

• Paradigm – A set of shared assumptions and agreed methods within a scientific discipline.
• Paradigm shift – The result of the scientific revolution: a significant change in the dominant unifying theory within a scientific discipline.
• Objectivity – When all sources of personal bias are minimised so not to distort or influence the research process.
• Empirical method – Scientific approaches that are based on the gathering of evidence through direct observation and experience.
• Replicability – The extent to which scientific procedures and findings can be repeated by other researchers.
• Falsifiability – The principle that a theory cannot be considered scientific unless it admits the possibility of being proved untrue.

The result is unlikely to be chance.

If the test is significant, we reject the null hypothesis and accept the alternative.

If it is not significant, we do the opposite: accept the null and reject the alternative.

A null hypothesis states there is no effect.

In Psychology, we normally use p < 0.05. This strikes a balance between a type I and a type II error. Sometimes a stricter bar is needed. Tests where a wrong result could cause harm, such as trialling a new drug, use the stricter p < 0.01.

A type I error rejects the null hypothesis when it should have been accepted. This is an error of optimism, from too lenient a significance level.

A type II error is the opposite mistake. It accepts the null hypothesis when it should have been rejected, an error of pessimism from too strict a level.

• Informed consent is when participants are able to make an informed judgment about whether to take part. It causes them to guess the aims of the study and change their behavior.
• To deal with it, we can gain presumptive consent or ask them to formally indicate their agreement to participate but it may invalidate the purpose of the study and it is not guaranteed that the participants would understand.
• Deception should only be used when it is approved by an ethics committee, as it involves deliberately misleading or withholding information. Participants should be fully debriefed after the study but debriefing can’t turn the clock back.
• All participants should be informed at the beginning that they have the right to withdraw if they ever feel distressed or uncomfortable.
• It causes bias as the ones that stayed are obedient and some may not withdraw as they may have been given incentives or feel like they’re spoiling the study. Researchers can offer the right to withdraw data after participation.
• Participants should all have protection from harm. The researcher should avoid risks greater than those experienced in everyday life and they should stop the study if any harm is suspected. However, the harm may not be apparent at the time of the study.
• Confidentiality concerns the communication of personal information. The researchers should not record any names but use numbers or false names though it may not be possible as it is sometimes possible to work out who the researchers were.