Hypotheses are statements about the prediction of the results, that can be verified or disproved by some kind of investigation.
There are four types of hypotheses:
- Null Hypotheses (H0) – these predict that no difference will be found in the results between the conditions. Typically these are written ‘There will be no difference…’
- Alternative Hypotheses (Ha or H1)– these predict that there will be a significant difference in the results between the two conditions. This is also known as the experimental hypothesis.
- One-tailed (directional) hypotheses – these state the specific direction the researcher expects the results to move in, e.g. higher, lower, more, less. In a correlation study, the predicted direction of the correlation can be either positive or negative.
- Two-tailed (non-directional) hypotheses – these state that a difference will be found between the conditions of the independent variable but does not state the direction of a difference or relationship. Typically these are always written ‘There will be a difference ….’
All research has an alternative hypothesis (either a one-tailed or two-tailed) and a corresponding null hypothesis.
Once the research is conducted and results are found, psychologists must accept one hypothesis and reject the other.
So if a difference is found, the Psychologist would accept the alternative hypothesis and reject the null. The opposite applies if no difference is found.
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 generalisations 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.
- A Volunteer sample is 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 is 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 is 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 is when you identify the subgroups and select participants in proportion with their occurrences.
- Snowball sampling is when researchers find a few participants, and then ask them to find participants themselves and so on.
- Quota sampling is when researchers will be told to ensure the sample fits with certain quotas, for example they might be told to find 90 participants, with 30 of them being unemployed.
Experiments always have an independent and dependent variable.
- The independent variable is the one the experimenter manipulates (the thing that changes between the conditions the participants are placed into). It is aassumed to have a direct effect on the dependent variable.
- The dependent variable is the thing being measured, or the results of the experiment.
Operationalization of variables means making them measurable/quantifiable. We must use operationalization to ensure that variables are in a form that can be easily tested.
For instance, we can’t really measure ‘happiness’ but we can measure how many times a person smiles within a two hour period.
By operationalizing variables, we make it easy for someone else to replicate our research. Remember, this is important because we can check if our findings are reliable.
Extraneous variables are all variables, which are not the independent variable, but could affect the results of the experiment.
It can be a natural characteristic of the participant, such as intelligence levels, gender, or age for example, or it could be a situational feature of the environment such as lighting or noise.
Demand characteristics are a type of extraneous variable that occurs if the participants work out the aims of the research study, they may begin to behave in a certain way.
For example, in Milgram’s research, critics argued that participants worked out that the shocks were not real and they administered them as they thought this was what was required of them.
Extraneous variables must be controlled so that they do not affect (confound) the results.
Randomly allocating participants to their conditions or using a matched pairs experimental design can help to reduce participant variables.
Situational variables are controlled by using standardized procedures, ensuring every participant in a given condition is treated in the same way
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 with respect to a given independent variable, it is essential to make sure that the two groups do not differ in any other important way.
All experimental methods involve an IV (independent variable) and DV (dependent variable).
Lab Experiments are conducted in a well-controlled environment, not necessarily a laboratory, and therefore accurate and objective measurements are possible.
The researcher decides where the experiment will take place, at what time, with which participants, in what circumstances, using a standardized procedure.
Field experiments are conducted in the everyday (natural) environment of the participants. The experimenter still manipulates the IV, but in a real-life setting. It may be possible to control extraneous variables, though such control is more difficult than in a lab experiment.
Natural experiments are when a naturally occurring IV is investigated that isn’t deliberately manipulated, it exists anyway. Participants are not randomly allocated, and the natural event may only occur rarely.
Case studies are in-depth investigations of a single person, group, event, or community. It uses information from a range of sources, such as from the person concerned and also from their family and friends.
Many techniques may be used such as interviews, psychological tests, observations and experiments. Case studies are generally longitudinal: in other words, they follow the individual or group over an extended period of time.
Case studies are widely used in psychology and among the best-known ones carried out were by Sigmund Freud. He conducted very detailed investigations into the private lives of his patients in an attempt to both understand and help them overcome their illnesses.
Case studies provide rich qualitative data and have high levels of ecological validity. However, it is difficult to
generalize from individual cases as each one has unique characteristics.
Correlation means association; it is a measure of the extent to which two variables are related. One of the variables can be regarded as the predictor variable with the other one as the outcome variable.
Correlational studies typically involve obtaining two different measures from a group of participants, and then assessing the degree of association between the measures.
The predictor variable can be seen as occurring before the outcome variable in some sense. It is called the predictor variable, because it forms the basis for predicting the value of the outcome variable
Relationships between variables can be displayed on a graph or as a numerical score called 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 looking at the scattergraph, if we want to be sure that a significant relationship does exist between the two variables, a statistical test of correlation can be conducted, such as Spearman’s rho.
The test will give us a score, called a correlation coefficient. This is a value between 0 and 1, and the closer to 1 the score is, the stronger the relationship between the variables. This value can be both positive e.g. 0.63, or negative -0.63.
A correlation between variables, however, does not automatically mean that the change in one variable is the cause of the change in the values of the other variable. A correlation only shows if there is a relationship between variables.
Correlation does not always prove causation as a third variable may be involved.
Interviews are commonly divided into two types: structured and unstructured.
In a structured interview, the interview situation is standardized as far as possible. Structured interviews are formal, like job interviews.
There is a fixed, predetermined set of questions that are put to every participant in the same order and in the same way. Responses are recorded on a questionnaire, and the order and wording of questions, and sometimes the range of alternative answers, is preset by the researcher.
The interviewer stays within their role and maintains social distance from the interviewee.
Unstructured interviews are informal, like casual conversations. They are normally preceded by a general conversation, and the researcher deliberately adopts an informal approach in an attempt to break down social barriers.
There are no set questions, and the participant is allowed to raise whatever topics he/she feels are relevant and ask them in their own way. Questions are posed in relation to participants’ answers to the subject
In this kind of interview, much qualitative data is likely to be collected.
Unstructured interviews are most useful in qualitative research designed to analyze attitudes and values. Though rarely provide a valid basis for generalization, their main advantage is that they enable the researcher to probe the subjective point of view of social actors.
Questionnaires can be thought of as a kind of written interview. They can be carried out face to face, by telephone, or post.
The questions asked can be open-ended, allowing flexibility in the respondent’s answers, or they can be more tightly structured, requiring short answers or a choice of answers from given alternatives.
The choice of questions is important because of the need to avoid bias or ambiguity in the questions, ‘leading’ the respondent, or causing offense.
Postal questionnaire seems to offer the opportunity of getting round 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 a large number of respondents scattered over a wide area relatively quickly.
There are different types of observation methods:
- Covert observation is where the researcher doesn’t tell the participants that 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 that 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
A pilot study is a small scale preliminary study conducted in order to evaluate the feasibility of the key steps in a future, full-scale project.
A pilot study is an initial run-through of the procedures to be used in an investigation; it involves selecting a few people and trying out the study on them. It is possible to save time, and in some cases, money, by identifying any flaws in the procedures designed by the researcher.
A pilot study can help the researcher spot any ambiguities (i.e. unusual things) or confusion in the information given to participants or problems with the task devised.
Sometimes the task is too hard, and the researcher may get a floor effect, because none of the participants can score at all or can complete the task – all performances are low. The opposite effect is a ceiling effect, when the task is so easy that all achieve virtually full marks or top performances and are “hitting the ceiling”.
In cross-sectional research, a researcher compares multiple segments of the population at the same time
Sometimes we want to see how people change over time, as in studies of human development and
lifespan. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time.
In cohort studies, the participants must share a common factor or characteristic such as age, demographic, or occupation. A cohort study is a type of longitudinal study in which researchers monitor and observe a chosen population over an extended period.
Triangulation means using more than one research method to improve the validity of the study.
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.
A meta-analysis is a systematic review that involves identifying an aim and then searching for research studies that have addressed similar aims/hypotheses.
This is done by looking through various databases and then decisions are made about what studies are to be included/excluded.
Strengths: Increases the validity of the conclusions drawn 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. The choice of the journal may be determined by the journal’s audience or prestige.
The journal selects two or more appropriate experts (psychologists working in a similar field) to peer review the article without payment. The peer reviewers assess: the methods and designs used, originality of the findings, the validity of the original research findings and its content, structure and language.
Feedback from the reviewer determines whether the article is accepted. The article may be: Accepted as it is, accepted with revisions, sent back to the author to revise and re-submit or rejected without the possibility of submission.
The editor makes the final decision whether to accept or reject the research report based on the reviewers comments/ recommendations.
Peer review is important because it prevent faulty data from entering the public domain, it provides a way of checking the validity of findings and the quality of the methodology and is used to assess the research rating of university departments.
Peer reviews may be an ideal, whereas in practice there are lots of problems. For example, it slows publication down and may prevent unusual, new work being published. Some reviewers might use it as an opportunity to prevent competing researchers from publishing work.
Some people doubt whether peer review can really prevent the publication of fraudulent research.
The advent of the internet means that a lot of research and academic comment is being published without official peer reviews than before, though systems are evolving on the internet where everyone really has a chance to offer their opinions and police the quality of research.
Types of Data
- Quantitative data is numerical data e.g. reaction time or number of mistakes. It represents how much or how long, how many there are of something. A tally of behavioral categories and closed questions in a questionnaire collect quantitative data.
- Qualitative data is virtually any type of information that can be observed and recorded that is not numerical in nature and can be in the form of written or verbal communication. Open questions in questionnaires and accounts from observational studies collect qualitative data.
- Primary data is first-hand data collected for the purpose of the investigation.
- Secondary data is information that has been collected by someone other than the person who is conducting the research e.g. taken from journals, books or articles.
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 in genuine and represents what is actually out there in the world.
- 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.
- 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.
A significant result is one where there is a low probability that chance factors were responsible for any observed difference, correlation or association in the variables tested.
If our test is significant, we can reject our null hypothesis and accept our alternative hypothesis.
If our test is not significant, we can accept our null hypothesis and reject our alternative hypothesis. A null hypothesis is a statement of no effect.
In Psychology, we use p < 0.05 (as it strikes a balance between making a type I and II error) but p < 0.01 is used in tests that could cause harm like introducing a new drug.
A type I error is when the null hypothesis is rejected when it should have been accepted (happens when a lenient significance level is used, an error of optimism).
A type II error is when the null hypothesis is accepted when it should have been rejected (happens when a stringent significance level is used, an error of pessimism).
- 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.