Generalisation and methods

Just as there are problems arising from making generalisations from a single measure, as discussed in the previous section, external validity can also be threatened when using a single method to measure a given construct. Known as mono-method bias, it threatens the construct validity of the measurement procedure you use [see the article: Construct validity]. Before we reflect back on the threat to external validity from using a single method, let's look at the problems that can arise from using a single method, based on our example in Study #2:

Study #2
The relationship between background music and task performance amongst employees at a packing facility

To briefly recap, our study examined the relationship between background music (i.e., construct A) and task performance (i.e., construct B) amongst employees at a packing facility (e.g., Amazon, Wal-Mart, Tesco, etc.). The independent variable was background music (i.e., construct A), whilst the dependent variable was task performance (i.e., construct B). One group of employees listened to background music whilst working (i.e., the treatment group), whereas the other group were not provided with any background music (i.e., the control group). The task performance of employees was measured in terms of the number of tasks employees perform correctly per hour. The method used to listen to background music was a loud speaker (i.e., stereo system), whilst task performance was measured using an e-packing system, which automatically collected data on the number of tasks correctly performed by employees.

Let's imagine some of the multiple methods that could be used to measure the two constructs (i.e., construct A, background music, and construct B, task performance):

Independent variable
Method #1: Listening to music through the loud speaker (i.e., stereo system)
Method #2: Listening to music using a personal iPod and headphones

Dependent variable
Method #1: Data automatically collected through the e-packing system
Method #2: Supervisor rating the speed of the packer

Note that sometimes a mono-method (i.e., a single method) is appropriate. For example, Method #1 for the dependent variable (i.e., data being automatically collected through the e-packing system, recording task performance accurately) may be the most accurate measure of task performance in this piece of research. After all, Method #1, where the supervisor rates the speed of the packer is more likely to result from experimenter bias or instrumental bias than an automated system that does not suffer from such bias. However, this is often not the case, and the use of multiple methods reduces the threat to construct validity and external validity.

When considering mono-method bias in your dissertation, you need to ask yourself:

If the measurement procedure consists of a single method to assess the independent and/or dependent variables, this can act as a threat to construct validity. This is because the method used may introduce bias, changing the scores on the independent or dependent variable. In addition, the use of a single method becomes a threat to external validity because we may be making generalisations from our results that are really only accurate when using a specific type of method.

The 'real world' versus the 'experimental world' (and external validity)

When making generalisations, whether to a wider population, or across populations, treatments, contexts/settings, or time, we are making generalisations from the experimental world to the real world. After all, irrespective of the quantitative research design that you use (i.e., descriptive, experimental, quasi-experimental or relationship-based research design), whenever participants know that they are taking part in research (i.e., experiments), there is the potential for that experimental world to influence the research findings (i.e., dependent variable) rather than the independent variables.

This raises a broad threat to external validity; that is, can we make generalisations from individuals that have experienced treatments (i.e., took part in the experiment) to people in the real world that have not experienced the same treatments (i.e., people who were not part of the experiment)?

In answering this question, there are three broad effects that may threaten the external validity of your results: testing effects, experimental effects and experimenter effects. Each is discussed in turn:

Testing effects

Testing effects, also known as order effects, multiple treatment interference, and reactive or interaction effects of testing, only occur in experimental and quasi-experimental research designs that have more than one stage; that is, research designs that involve a pre-test and a post-test. In such circumstances, the fact that the person taking part in the research is tested more than once can influence their behaviour/scores in the post-test, which confounds the results; that is, the differences in scores on the dependent variable between the groups being studied may be due to testing effects rather than the independent variable. Some of the reasons why testing effects occur include learning effects (practice or carry-over effects) and experimental fatigue. Each is discussed in turn:

Testing effects are not a problem in all studies. For example, as a 'general rule of thumb', testing effects are less likely to be a threat to external validity where there has been a large time period between the pre-test and post-test compared with experiments having a short interval between tests. You need to ask yourself: To what extent are learning effects a problem for the post-test in my experiment?

Experimental effects

Participants can behave differently when they are taking part in research compared to the way that they would behave in everyday life. Some of these differences in behaviour result from subject effects/novelty effect, compensatory rivalry, demoralization and compensation. Each is briefly discussed in turn:

Experimenter effects

Just as experimenter characteristics can threaten the internal validity of your research, they can also threaten its external validity. An experimenter effect, which results in experimenter bias, can threaten external validity across all types of experimental and quasi-experimental research design. Such an experimenter effect is typically unintentional, but arises because of (a) the personal characteristics of the researcher, which influences the choices made during a study; and (b) non-verbal cues that the researcher gives out that may influence the behaviour and responses of participants. Some of the more generic personal characteristics that may lead to bias include the experimenter's age, class, gender, race, and so forth.

Furthermore, in quantitative research, you often make predictions about the outcome of an experiment. These predictions may come in the form of directional hypotheses. We call something a directional hypothesis, rather than a non-directional hypothesis, because we making a prediction about the outcome of an experiment. For example: As physical activity increases, risk of heart disease decreases; As pay increases, employee motivation increases [see the section on Research (and null) hypotheses].

Seldom will you design an experiment thinking that nothing will happen, or having no idea about the potential outcome. For example, we think that a new teaching method will improve student exam performance, so we design an experiment to find out if this is the case; we think that introducing background music into a packing facility will increase employee task performance, so we design an experiment to test our directional hypothesis.

Since you, as the experimenter, may make such predictions, it is possible that certain personal biases will enter the research process. These personal biases are often exhibited in the experimenter's behaviour, which may include being more/less helpful/friendly/informative towards the different groups involved in the study in order to influence their behaviour. Whilst this may be an unconscious form of bias, it can lead to changes in the dependent variable that are not due only to the treatment (i.e., the independent variable), but also experimenter effects. If the measurement of the dependent variable is more qualitative, this may pose a more significant threat to internal validity (e.g., the experimenter makes the judgement of a student's performance on a scale of 1-10 instead of this measurement being less subjective, such as using a measurement device like a written test or behavioural scale).

Experimenter bias becomes a threat to external validity because the results that are obtained in a given study may simply reflect the personal biases of the researcher. If another researcher were to carry out the same study using a sample with very similar characteristics and the same research methods, different results may be obtained. Therefore, our ability to generalise from the results of a study that is subject to experimenter bias is threatened.

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