Here is the final unit of four, giving advice as to what to include in the Evaluation section and how to avoid mistakes. Enjoy!
Here is a guide to writing the Analysis section. Enjoy!
True experiment, field experiment, quasi-experiment or natural experiment? The answer is often a wild look in the eyes and a shrug of the shoulders. It is not always easy to be certain! See below for an explanation of the differences. All sources used are referenced at the bottom of the page, and linked study summaries are, of course, from Psychology Sorted.
The easiest one to define is the true experiment.
Often called a ‘laboratory/lab’ experiment, this does not have to take place in a lab, but can be conducted in a classroom, office, waiting room, or even outside, providing it meets the criteria. These are that allocation of participants to the two or more experimental (or experimental and control) groups or conditions is random and that the independent variable (IV) is manipulated by the researcher in order to measure the effect on the dependent variable (DV). Other variables are carefully controlled, such as location, temperature, time of day, time taken for experiment, materials used, etc. This should result in a cause and effect relationship between the IV and the DV. Examples are randomised controlled drug trials or many of the cognitive experiments into memory, such as Glanzer and Cunitz_1966.
A field experiment is similar, in that individuals are usually randomly assigned to groups, where this is possible, and the IV is manipulated by the researcher. However, as this takes place in the participants’ natural surroundings, the extraneous variables that could confound the findings of the research are somewhat more difficult to control. The implications for causation depend on how well these variables are controlled, and on the random allocation of participants. Examples are bystander effect studies, and also research into the effect of digital technology on learning, such as that conducted by Hembrooke and Gay_2003.
A quasi-experiment is similar to either or both of the above, but the participants are not randomly allocated to groups. Instead they are allocated on the basis of self-selection as male/female; left or right-handed; preference for coffee or tea; young/old, etc. or researcher selection as scoring above or below and certain level on a pre-test; measured socio-economic status; psychology student or biology student, etc. These are therefore, non-equivalent groups. The IV is often manipulated and the DV measured as before, but the nature of the groups is a potential confounding variable. If testing the effect of a new reading scheme on the reading ages of 11 year olds, a quasi-experimental design would allocate one class of 11 year olds to read using the scheme, and another to continue with the old scheme (control group), and then measure reading ages after a set period of time. But there may have been other differences between the groups that mean a cause and effect relationship cannot be reliably established: those in the first class may also have already been better readers, or several months older, than those in the control group. Baseline pre-testing is one way around this, in which the students’ improvement is measured against their own earlier reading age, in a pre-test/post-test design. In some quasi-experiments, the allocation to groups by certain criteria itself forms the IV, and the effects of gender, age or handedness on memory, for example, are measured. Examples are research into the efficacy of anti-depressants, when some participants are taking one anti-depressant and some another, or Caspi et al._2003, who investigated whether a polymorphism on the serotonin transporter gene is linked to a higher or lower risk of individual depression in the face of different levels of perceived stress.
Finally, natural experiments are those in which there is no manipulation of the IV, because it is a naturally-occurring variable. It may be an earthquake (IV) and measurement of people’s fear levels (DV) at living on a fault line before and after the event, or an increase in unemployment as a large factory closes (IV) and measurement of depression levels amongst adults of working age before and after the factory closure (DV). As with field experiments, many of the extraneous variables are difficult to control as the research takes place in people’s natural environment. A good example of a natural experiment is Charlton (1975) research into the effect of the introduction of television to the remote island of St. Helena.
The differences between quasi experiments and correlational research, and between natural experiments and case studies are sometimes hard to determine, so I would always encourage students to explain exactly why they are designating something as one or the other. We can’t always trust the original article either – Bartlett was happy to describe his studies as experiments, which they were not! Here’s hoping these examples have helped. The following texts are super-useful, and were referred to while writing this post.:
Campbell, D.T. & Stanley J.C. (1963). Experimental and Quasi-Experimental Designs for Research. Boston: Houghton Mifflin (ISBN 9780528614002)
Coolican, H. (2009, 5th ed.). Research Methods and Statistics in Psychology. UK: Hodder (ISBN 9780340983447)
Shadish, W.R., Cook, T.D. & Campbell, D.T. (2001, 2nd ed.). Experimental and Quasi-experimental Designs for Generalized Causal Inference. UK: Wadsworth (ISBN 9780395615560)
How we develop our social identity is still a hot topic today, and for those of you studying the effect of technologies, especially social media, on social identity, there is a developing literature on the subject. But we should start with the classic minimal groups paradigm from Tajfel (1971), found in our new book Psychology Sorted, as it is still so relevant today.
The predominant 1960s theory of social identity formation came from Sherif et al.’s (1961) study which led to the development of his 1966 realistic conflict theory that competition for scarce resources is the foundation for group (social) identity, and also one cause of conflict. Think of the worldwide competition for water and oil on a large scale and maybe sporting competitions on a smaller scale. Why do you think that schools have ‘houses’, ‘sporting colours’, ‘house badges’?
However, Tajfel’s research contradicted this, demonstrating that only minimal conditions were necessary for group identity to form: his experiment randomly allocated schoolboys to two groups. The boys thought they had been allocated their group according to their preference for a painting by either Klee or Kandinsky, but this was a deception and the allocation was random. This perception of belonging to a certain group was enough for boys to show in-group favouritism when allocating virtual money via a complex matrix of rules. The minimal groups paradigm formed the basis of Tajfel and Turner’s social identity theory, which remains a powerful explanation of in-group favouritism and out-group discrimination.
The three sequential steps Tajfel & Turner (1979) deemed necessary for social identity to form are:
- social categorisation – we understand that people (and things) can be grouped
- social identification – we identify with a group
- social comparison – we compare ourselves favourably with another group
Social comparison underlies stereotyping, gang fights (though these can also be seen as competition for scarce resources), between-class competitions, girl/boy competition, online identities…how many more can you think of?
Tajfel’s theory can be used extensively in the curriculum, from his lab experiments in the 1970s (research methods), to an argument for the formation of stereotypes (sociocultural approach), to an explanation of how competition and maybe even conflict is generated in human relationships, to how images are cultivated socially on Snapchat, Instagram and (amongst us oldies) Facebook for cognitive psychology. This is an example of a classic theory that can be easily accessed through Psychology Sorted.
Let’s get started! This is a useful summary for teachers and students of the process for the new IA (internally-assessed student-conducted experiment…now you see why the name is shortened 🙂 This will first be assessed in May 2019, and I’m sure some of you are getting started soon.
Group work is mandatory. Up to 4 students in a group, and preferably each group conducting a different experiment, so you don’t run out of participants. The experiment is run together by the group to collect the raw data, but every section, and all data calculations, have to be performed and written about individually.
Statistical Analysis must be conducted by everyone. Descriptive statistics identify if there is a difference between the two conditions and inferential statistical analysis tells you whether or not this difference is significant at the p<0.05 level. Unless you are an expert statistician, it is easier to just manipulate the independent variable once to give two conditions under which you measure the dependent variable. Plan how you are going to do this, and which tests you need to use before even starting your experiment.
Ethical Considerations – be sure that your experiment will cause no harm or stress to the participants, who may not be animals or young children. Conformity experiments are not allowed, because they are stressful, and you may not ask your participants to eat or drink anything in order to test the effects. Neither may you deprive them of sleep. Your appendices at the very end of your report should contain a blank copy of the informed consent form, a copy of your briefing and debriefing notes, raw data tables and your calculations for the analysis.
IA Report – This needs a header containing the following information: title; your IB candidate code and the codes of all group members; date, month and year of submission; no. of words.
The Report should be between 1800 and 2200 words and split into the following 4 sections:
Introduction (6 marks) – Contains the aim of the experiment, and explains the link between the experiment and the model or theory on which it is based. (Most likely your experiment will be based on another study or experiment, but you need to know the underlying theory and show the link). The hypotheses should be written out carefully, and contain the operationalised independent and dependent variable. It is probably easier to write these separately first and then combine them to make the hypothesis.
Exploration (4 marks) – This is where you describe your procedure, including the design, sampling technique, participant characteristics, controlled variables and materials. Write it very carefully, as you will want to refer back to it later in your last (Evaluation) section.
Analysis (6 marks) – Consists of correctly chosen and applied descriptive and inferential statistics. The descriptive statistical analysis results should be shown in a bar chart (graph) that is carefully labelled. The inferential statistics results need to be interpreted in terms of what they show about the hypothesis. Do you have to accept or reject your null hypothesis, and why?
Evaluation (6 marks) – This is where you explain your results, in relation to the theory/model and study on which you based your experiment. You need to explain the strengths and limitations of your design, sample and procedure and suggest how you could have improved upon what you did. We cannot always anticipate the effect of decisions we made earlier when deciding how to conduct the experiment, but we can explain their effect at the end.
All IAs need a list of references at the back, and the appendices follow this. They do not count towards the word count.
Remember – it doesn’t have to be a complex experiment. The simpler the better. Old ‘favourites’ from Cognitive Psychology always do well: Loftus and Palmer, Stroop, Peterson & Peterson and Bransford & Johnson are all tried and tested studies from the area of memory.