In order to establish that it is definitely the independent variable causing the effect on the dependent variable, all other potential variables in the environment must be controlled during an experiment. These extraneous variables include anything that may affect the dependent variable, such as noise, distractions, time of day, etc. Anything in the study’s environment is also referred to as a situational variable. If an extraneous variable does happen to affect the dependent variable, possibly because it wasn’t controlled, then it becomes a confounding variable. Furthermore, the environmental conditions of an experiment must remain exactly the same between participants, to ensure that any difference between participants’ results is purely down to the individual and not due to changes in the procedure. This is called standardisation. To ensure that an experiment is standardised even the instructions for the participant should be written down, so they are not delivered in a different way each time. Laboratory studies are better able to standardise their procedures and control extraneous variables than other types of experiments.

If using an independent groups design in a study then it is very important for participants to be randomly allocated to conditions if possible, in order to reduce the impact of participant variables. Participant variables include things such as age and IQ. Random allocation is the best way to get a spread of ages/IQs/etc. across all conditions (as long as the sample size if large enough!).

If possible, any trials during a study should be randomised. For example, imagine an experiment where a participant is sat at a computer and being flashed images of different people. Their instruction is to rate how happy they perceive the face to be. To avoid any patterns emerging in participants’ responses due to the order of presentation (because people are likely to make judgements based on the face they last saw too in such a subjective situation), the order of picture presentation should be randomised by the computer.

In a repeated measures design there is always the risk of order effects; where the participants’ performance on the second of two conditions due to practice effects, fatigue or boredom. For example, imagine if the participants in the above experiment were asked to complete the rating task whilst either listening to pleasant music or not, with the silence condition taking place first. By the second trial with pleasant music, the participants might simply be fed up of rating so many faces and this annoyance might lead them to rate the faces more negatively (which would impact on the study’s outcomes, making it seem like listening to pleasant music causes people to have a more negative perception of others’ emotions!) To cancel out such affects, the study should be counter-balanced. Half of participants should rate the faces in silence first and the other half should do so whilst listening to pleasant music. They should then switch.

Demand Characteristics & Investigator Effects

Demand characteristics

The idea that participants will behave the way they believe you want them to behave.  It could be that participants guess what the experiment is about, or at least think they’ve guessed, and this will influence their behaviour accordingly.This was a criticism of the Milgram procedure.  In Asch’s study on conformity, some of the participants said afterwards that they conformed because they didn’t want to mess up the experiment! Orne (1962) persuaded participants to do strange, if not very foolish things.  This argument is often used in the debate over hypnosis.  Orne, for example, persuaded his participants to put their hands into a tank containing a supposedly very venomous snake.  His most famous ‘experiment’ was to persuade participants to spend hours adding up random numbers and then getting them to tear up all their hard work!

This leads into the placebo effect, most commonly seen with treatments for health of psychological issues. If people are prescribed a fake drug (called a placebo), but told that they are receiving medication for their problem, a significant number of people will always report an improvement in their symptoms. For psychological disorders such as depression, a placebo pill seems to work around 30% of the time!

Reducing demand characteristics

The most common ploy is called the single-blind technique in which participants are not told details of the study, are deceived into believing that it’s about something different, or are unaware as to whether they are in the experimental or control condition (often called the placebo condition).

Experimenter effects

We know that some results in Psychology have been fiddled, some on a grand scale.  Obtaining ‘expected results’ like this can be deliberate. Or it can happen without intent.  We often find what we are expecting or hoping to find.  Having decided that women are worse drivers than men we notice bad driving by women, whilst ignoring similar driving by men.  When this happens in research it is called experimenter expectancy.  The classic example is Rosenthal and Lawson (1964).  They gave rats to students, telling some that their rats were ‘maze bright’ and could navigate a maze very quickly, and telling others that their rats were ‘maze dull’ and not very good at navigating a maze.  In fact the rats were all similar and allocated to each group of students randomly. You can probably guess the findings:  students with the supposedly maze bright rats found that their rats could navigate mazes significantly faster!

Experimenter effects can sometimes happen through non-verbal communication (an experimenter may express shock or surprise to a participant, leading them to alter their behaviours or responses), due to physical characteristics of the experimenter, which may lead to participants acting in a way they might not normally (affecting the validity of results), or when a researcher is unconsciously biased in their interpretation of results; maybe because they are expecting a certain outcome.

Reducing experimenter effects

The most common ploy is called the double-blind technique, in which neither the participants nor the researchers dealing with the participants know the conditions etc.  Obviously someone distant from the procedure still needs to know which participants are in which condition so that results can be analysed!  This procedure is commonly used in drug testing when genuine medicines are compared to placebos.

Remember that on top of this there is the ‘data analysis’ section, which involves some number crunching.  See contents page for details.  However, the worksheets provided in lessons should cover this are in sufficient detail so I’ll skip notes on this and concentrate on conformity instead.  Hope this has not been too heavy a read, I’ve tried to keep it brief and to the point!

Pilot Studies

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”.