A laboratory experiment is an experiment conducted under highly controlled conditions.
The variable which is being manipulated by the researcher is called the independent variable and the dependent variable is the change in behaviour measured by the researcher.
All other variables which might affect the results are called confounding variables (also referred to as extraneous variables).
Strengths of Lab Experiments
+ The experimental method allows us to draw conclusions with far more certainty than any non-experimental method. If the independent variable is the only thing that is changed then it must be responsible for any change in the dependent variable.
+ Laboratory experiments allow for precise control of variables. The purpose of control is to enable the experimenter to isolate the one key variable which has been selected (the independent variable), in order to observe its effect on some other variable (the dependent variable); control is intended to allow us to conclude that it is the independent variable, and nothing else, which is influencing the dependent variable. Thus lab experiments allows us to establish cause (IV) and effect (DV).
+ Lab experiments can usually be easily replicated. The experimental method consists of standardised procedures and measures which allow it to be easily repeated.
Weaknesses of Lab Experiments
– It is not always be possible to completely control all variables. There may be other variables at work which the experimenter is unaware of.
– Laboratory experiments are not always typical of real life situations. These types of experiments are often conducted in strange and contrived environments and the participants may be asked to carry out unusual tasks. The behaviour of the participants may be distorted and not be like behaviour that would be carried out in the real world. Therefore, it should be difficult to generalise findings from experiments because they are not usually ecologically valid (true to real life).
– A further difficulty with the experimental method is demand characteristics. Demand characteristics are all the cues which convey to the participant the purpose of the experiment. If a participant knows they are in an experiment they may seek cues about how they think they are expected to behave.
– Another problem with the experimental method concerns ethics. For example, experiments often involve deceiving participants to some extent.
A field experiment is an experiment that is conducted in ‘the field’. That is, in a real world situation. In field experiments the participants are not usually aware that that they are participating in an experiment.
The independent variable is still manipulated unlike in natural experiments.
Strengths of Field Experiments
+ Field experiments are usually high in ecological validity and may avoid demand characteristics as the participants are unaware of the experiment.
Weaknessess of Field Experiments
– in field experiments it is much harder to control confounding variables and they are usually time consuming and expensive to conduct.
– in field experiments it is not usually possible to gain informed consent from the participants and it is difficult to debrief the participants.
Quasi or Natural Experiments
A quasi experiment is where the independent variable is not manipulated by the researcher but occurs naturally. These experiments are often called natural experiments.
In a quasi experiment the researcher takes advantage of pre-existing conditions such as age, sex or an event that the researcher has no control over such as a participants’ occupation.
Strengths of Quasi Experiments
+ participants are often unaware that they are taking part in an investigation and they may not be as artificial as laboratory experiments.
Weaknesses of Quasi Experiments
– it is harder to establish causal relationships because the independent variable is not being directly manipulated by the researcher.
An experimental design is a set of procedures used to control the influence of participant variables so that we can investigate the possible effects of the independent variable on the dependent variable. There are three basic experimental designs – independent measures design, repeated measures design and matched pairs design.
Independent measures design: consists of using different participants for each condition of the experiment. If two groups in an experiment consist of different individuals then this is an independent measures design.
- Strengths: this type of design has an advantage resulting from the different participants used in each condition – there is no problem with order effects
- Weaknesses: The most serious disadvantage of independent measures designs is the potential for error resulting from individual differences between the groups of participants taking part in the different conditions. Also an independent groups design may represent an uneconomic use of those participants, since twice as many participants are needed to obtain the same amount of data as would be required in a two-condition repeated measures design.
Repeated measures design: consists of testing the same individuals on two or more conditions.
- Strengths: The key advantage of the repeated measures design is that individual differences between participants are removed as a potential confounding variable. Also the repeated measures design requires fewer participants, since data for all conditions derive from the same group of participants.
- Weaknesses: The range of potential uses is smaller than for the independent groups design. For example, it is not always possible to test the same participants twice. There is also a potential disadvantage resulting from order effects, although these order effects can be minimised. Order effects occur when people behave differently because of the order in which the conditions are performed. For example, the participant’s performance may be enhanced because of a practice effect, or performance may be reduced because of a boredom or fatigue effect.
Order effects act as a confounding variable but can be reduced by using counterbalancing. If there are two conditions in an experiment the first participant can do the first condition first and the second condition second. The second participant can do the second condition first and the first condition second and so on. Therefore any order effects should be randomised.
Matched pairs design: consists of using different participants for each condition of the experiment but participant variables are controlled by matching pairs of variables on a key variable.
- Strengths: combines benefits of both independent measures design & repeated measures design
- Weaknesses: Although this design combines the key benefits of both an independent and repeated measures design, achieving matched pairs of participants is a difficult and time consuming task which may be too costly to undertake. Successful use of a matched pairs design is heavily dependent on the use of reliable and valid procedures for pre-testing participants to obtain matched pairs.
When carrying out experiments it is expected that the researcher will start with a hypothesis.
A hypothesis is a testable, predictive statement. The hypothesis will state what the researcher expects to find out.
It is important that the independent and dependent variables are clearly stated in the hypothesis.
- One-tailed hypothesis: when a hypothesis predicts the expected direction of the results. E.g. participants who are tested at 10am will perform significantly better on a memory test than participants who are tested at 10pm.
- Two-tailed hypothesis: when a hypothesis does not predict the expected direction of the results. E.g. there will be a difference in performance on a memory test between participants who are tested at 10am and participants who are tested at 10pm
When conducting an experiment it is important that we have an alternate hypothesis and a null hypothesis.
- Alternate hypothesis: the hypothesis that states the expected results. It is alternative to the null hypothesis.
- Null hypothesis: is not the opposite of the alternate hypothesis it is a statement of no difference. E.g. there will be no significant difference on the performance on a memory test between participants who are tested at 10am and participants whom are tested at 10pm.
The reason we have a null hypothesis is that the statistical tests that we use are designed to test the null hypothesis.
Experiments produce quantitative data which can be analysed statistically. Statistics are a method of summarising and analysing data for the purpose of drawing conclusions about the data.
We can make a distinction between descriptive and inferential statistics.
Descriptive statistics give us a way to summarise and describe our data but do not allow us to make a conclusion related to our hypothesis.
When carrying out an experiment there are two main ways of summarising the data using descriptive statistics. The first way is to carry out of measure of central tendency (mean, median or mode) for each of the two conditions.
- The Mean: is the arithmetic average that indicates the typical score in a data set and is calculated by adding all the scores together in each condition and then dividing by the number of scores. Useful because… it takes all of the scores into account but can be misleading if there are extreme values.
- The Median: is calculated by finding the mid point in on ordered list. The median is calculated by placing all the values of one condition in order and finding the mid- point. Useful because… it can be used when there are extreme values. However, not all of the scores are taken into account. The median can also not be used when data are nominal.
- The Mode: when looking at a set of scores the mode is the score that applies to the greatest number of participants. Useful because… it can be used on nominal data. However, it does not tell us anything about other scores in the distribution; they often are not very ‘central’ and they tend to fluctuate from one random sample of a population to another more often than either the median or the mean.
The second way of summarising and describing data is to calculate a measure of dispersion. This simply shows us the spread of a set of data.
A simple way of calculating the measure of dispersion is to calculate the range.
- The Range is the difference between the smallest and largest value in a set of scores.
Types of Data
- Nominal data = data in separate categories such as number of runners that finished a marathon.
- Ordinal data = data that are ordered such as the order of runners that finished the marathon ‘first, second, third and so on.’
- Interval data = data that are measured using a public unit of measurement. For example this could be the times in which the runners finished the marathon.
When conducting experiments it is important to operationalise the variables. That is, stating a clear way that the independent variable is going to be manipulated and the dependent variable is to be measured.
For example if an experiment was to be carried out to see if time of day affected memory it would be important to operationalise the variables of time of day and memory. We might operationalise time of day as 10am and 10pm and operationalise memory as performance on a memory task. For example number of words recalled out of a list of fifty words.
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