Independent variable (IV): Variable the experimenter manipulates - assumed to have a direct effect on the dependent variable. Is Made to change
Dependent variable (DV): Variable the experimenter measures, after making changes to the IV which are assumed to affect the DV.
Extraneous variables (Ex Vs): Other variables, apart from the IV, that might affect the DV. They might be important enough to provide alternative explanations for the effects, for example, confounding variables.
Directional Hypothesis - States the expected direction of the results. Example you are expected that people will remember more when studying in short bursts.
Non-Directional hypothesis - predicts that there will be a difference between the two conditions or two groups of participants.
Types of Experiments
Laboratory experiment: Artificial environment with tight controls over variables.
Field experiment: Natural environment with independent variable manipulated by researchers.
Natural experiment: Natural changes in independent variable are used - it is not manipulated.
- Tighter control of variables. Easier to comment on cause and effect
- Relatively easy to replicate.
- Enable use of complex equipment.
- Often cheaper and less time-consuming than other methods
- Demand characteristics - participants aware of experiment, may change behaviour.
- Lacks mundane realism
- Experimenter effects - bias when experimenter's expectations affect behaviour.
- Have low external validity, cant generalise.
A field experiment takes place anywhere in a natural setting; it could take place in a school, hospital, the street or an office
A field experiment is an experiment; the independent variable is manipulated. Not all field studies areexperiments.
- People may behave more naturally than in laboratory -higher realism.
- Easier to generalise from results.
- Often only weak control of extraneous variables - difficult to replicate.
- Can be time-consuming and costly.
- Situations in which it would be ethically unacceptableto manipulate the independent variable.
- Less chance of demand characteristics orexperimenter bias interfering.
- The independent variable is not controlled by the experimenter.
- No control over the allocation of participants to groups (random in a 'true experiment').
Three experimental designs are commonly used:
Independent groups: Testing separate groups of people, each group is tested in a different condition.
Repeated measures: Testing the same group of people in different conditions, the same people are used repeatedly.
Matched pairs: Testing separate groups of people - each member of one group is same age, sex, or social background as a member of the other group.
In each case, there are one or more experimental groups, where the independent variable has changed and acontrol group where the independent variable has not changed.
Independent group design
No control of participant variables.
More people are needed
Ways of dealing with the limitations
Randomly allocate partcipants to conditions which distributes participant variables evenly
Be prepared to spend more time and money
Avoids the problem of participant variables.
Fewer people are needed.
Order effects are more likely to occur.
Matched Pairs design
Reduces participant variables.
Avoids order effects.
Very time-consuming trying to find closely matched pairs.
Impossible to match people exactly, unless identical twins!
Correlation is a statistical technique used to quantify the strength of relationship between two variables.
- Calculating the strength of a relationshipbetween variables.
- Useful as a pointer for further, more detailed research.
- Cannot assume cause and effect, strong correlation between variables may be misleading.
- Lack of correlation may not mean there is no relationship, it could be non-linear.
For a correlational study, the data can be plotted as points on a scattergraph. A line of best fit is then drawn through the points to show the trend of the data.
If both variables increase together, this is a positive correlation.
If one variable increases as other decreases this is a negative correlation.
If no line of best fit can be drawn, there is no correlation.
Correlation can be quantified by using a correlation coefficient - a mathematical measure of the degree of relatedness between sets of data.
Participants should be told exactly about what the study is and what they have to do.
Psychologists shouldnt decieve participants. They have to be honest with them.
Right to withdraw
Have to give participants the right to withdraw
Protection from physical and psychological harm
They are not allowed to hurt people
Have to have files locked away. if they are not in use then they have to be shred.
Not allowed to be observed. invasion of privacy.
British psychologist society
BPS - You have to write to them if you want to do a case. they say yes or no if you can do it or not. they give you permission or not.
Presumptive consent - Hello, we are seeking for your permission for you to take part in our study which is childhood effects on relationships. you will have to take part in a short experiment. your details will be kept confidential and you are able to withdraw at any time. thank you.
Debrief - Dear participants thank you for taking part in my study. the study was regarding childhood effects on relationships. your results will be kept confidential. if you would like to withdraw then please let me know. if you need extra support you can call the smaritans.
Self - report techniques
When designing a questionnaire, there are several ways you can approach the study:
Use closed questions (fixed choice of answers), to generate data for easy analysis.
Use open questions (space to write any answer) for more detailed individual answers.
Keep questions and instructions clear and easy to understand.
Ask purposeful questions to help find information needed for the study.
Pre-code closed questions for quick analysis of the answers.
Carry out a pilot study first, a test run, making changes if needed.
Use attitude scales to test strength of feeling.
- Many people can be tested quickly. It is easy to generate quantitative data and easy to analyse.
- Used to collect large amounts of data about what peoplethink as well as what they do!
- Convenient - researcher does not need to be present as answers can be mailed so respondent has time to consider answers
- Can quickly show changes in attitudes or behaviour before and after specific events.
- Social desirability - people say what they think looks good.
- People may not tell the truth, especially on sensitive issues, for example, sexual behaviour.
- If researcher is present then this may affect answers. Also, postal surveys may have low response rate.
- Difficult to phrase questions clearly, you may obtain different interpretations of questions.
Interviews are face-to-face conversations, these can be unstructured, apparently informal chats, or they can be formal, structured interviews with pre-determined questions. For example, clinical tests used in psychiatry. Interviews are recorded for later, in-depth analysis.
- Detailed information can be obtained and avoids oversimplifying complex issues.
- Greater attention to individual's point of view this is important in clinical psychology.
- Unstructured, casual interviews may encourageopenness in answers.
- Difficult to analyse if unstructured and qualitative in nature.
- Time-consuming, expensive.
- Possible interviewer effects. For example, people affected by attractiveness of interviewer!
Quantitative & Qualitative
Research can be described as quantitative or qualitative.
Quantitative research: Gathers data in numerical form and is concerned with making 'scientific' measurements. Quantitative data analysis uses a barrage of inferential statistical tests.
Qualitative research: Gathers information that is not in numerical form. For example, diary accounts, open-ended questionnaires, unstructured interviews and unstructured observations.
Qualitative research is useful for studies at the individual level, and to find out, in depth, the ways in which people think or feel.
Analysis of qualitative data is difficult and requires accurate description of participant responses, for example, sorting responses to open questions and interviews into broad themes.
Quotations from diaries or interviews might be used to illustrate points of analysis.
Expert knowledge of an area is necessary to try to interpret qualitative data and great care must be taken when doing so, for example, if looking for symptoms of mental illness.
Accurate descriptions of individual behaviour patterns might be crucial to diagnosis, treatment and follow-up of a person with a mental disorder.
Quantitative. Central tendency and Dispersion
Below is a list of terms that are commonly used, it is important to know how to use them:
Arithmetic mean: All values in a set of data are added together and divided by the number of values (N). Used with normal distribution and interval level data. Not suitable for use where extreme values can distort the mean.The most sensitive measure of central tendency.
Median: All values are arranged in order, the middle value is the median. Used with interval or ordinal level data, the median is not affected much by extreme values.
Mode: The most frequent value or score in a set of data. Used with nominal data. Does not give any information about other values.
Range: Simple measure of dispersion- shows the total spread of data. Difference between highest and lowest scores in a set of data: top value minus bottom value plus 1. Affected by atypical, extreme values.
Standard Deviation: Measure of dispersion- shows degree of clustering of values around the mean. Calculating standard deviation (S): Square root of sum of all squared deviations from the mean, divided by N (or sometimes N-1). The most accurate measure of dispersion.