The Scientific Method

The Scientific Method

The scientific method refers to the way in which scientists ask questions and the logic and methods employed to obtain answers. The scientific method is empirical, systematic, controlled, and objective. This approach differs from the way in which individuals may obtain knowledge in everyday life. As individuals, we have a tendency to rely on our own intuition when solving everyday problems, with little regard or serious consideration of the evidence on offer. While intuition is not completely without value, it remains subject to certain biases which may arise from our upbringing, environment, culture, morals, or attitudes/beliefs. One such bias is known as confirmation bias, which is the tendency to seek out information which is consistent with our beliefs at the expense of information which contradicts it, which is simply ignored. Similarly, illusory correlation refers to our tendency to perceive a relationship between events where none exists, resulting in our being more likely to notice information that is consistent with our beliefs than information which is not. As such, knowledge obtained through intuition alone cannot be considered empirical, systematic, controlled, or objective, and thus cannot be considered scientific. In place of intuition, the scientific method employs what is known as an empirical, or evidence based approach. This places a great emphasis on direct observation and experimentation. Key to both of these methods is control. Through controlled, systematic observation or experimentation, it becomes possible to identify the factors which influence or give rise to a phenomenon. Of all the methods a scientist employs in his pursuit of information, the experiment provides for the highest degree of control. In the context of psychology, a psychologist conducts an experiment by manipulating one or more factors (known as independent variables) and observes the effect of this manipulation on the behaviour of the participant (known as the dependent variable), if any. The data gained from this condition is then compared against the data gained from what is known as the control condition, which is the same set of circumstances as in the experimental condition minus the manipulation by the researcher. It then becomes possible to identify whether manipulation of a certain variable is correlated with a certain type of behaviour. In order to establish causation, the researcher must control for all viable alternative explanations, but exercising such a high level of control may result in the circumstances of the experiment being considered unrealistic and thus not generalizable back to the population in question. In order to properly answer any research question, we must have clearly defined concepts, or constructs. A psychological construct could constitute anything from aggression to intelligence to depression. A problem arises when using terms like these, as different individuals will likely have different ideas of what they mean. Psychologists can get around this through the use of what are known as operational definitions. An operational definition explains a concept solely in terms of the observable procedures used to produce and measure it. For example, anxiety can be operationally defined as the score a person produces when using a questionnaire such as the Taylor Manifest Anxiety Scale. In order to overcome problems which may arise from the use of operational definitions, such as invalid or arbitrary definitions, one must compare the operational definition in

question against one already tested and established. If participants score similarly using both definitions, it is likely that they are measuring the same thing. Any and all instruments employed by the researcher should be as accurate and precise as possible. Instruments should also be valid and reliable. Measurement which comes from these instruments may be either physical or psychological. Physical measures can include length, width etc, while psychological measures can include aggression or anxiety. Before conducting any kind of research, a researcher must formulate what is known as a hypothesis. A hypothesis can be described as a tentative explanation for something; a prediction concerning how one variable will affect another under certain circumstances, based on existing evidence which has been accumulated by other researchers in the area. For a hypothesis to be considered useful, it must be testable. A hypothesis is not testable either if its constructs are not clearly defined, if it employs circular logic, or if it appeals to ideas not recognised by mainstream science. Finally, when reporting an observed behaviour, a researcher should take care not to make any inferences regarding the behaviour without the data to back it up. It is important to remain as unbiased and objective as possible when reporting results, keeping all conclusions neutral and open to argument. In conclusion, the scientific method is characterised by an empirical approach, systematic and controlled observation, unbiased and objective reporting, clear operational definitions of constructs, accurate and precise instruments, valid and reliable measures, and testable hypotheses.

Goals of the Scientific Method

The scientific method has four main goals; Description, Prediction, Explanation, and Application. Description refers to the way in which scientists seek to describe events and the relationships between variables. It refers to the procedures used to define, classify, catalogue, or categorize events and their relationships to describe mental processes and behaviour. This can be done either via a nomothetic approach (large sample, quantitative) or ideographic approach (small sample/case study, qualitative). By only concerning itself with the population average, the nomothetic approach fails to account for individual differences, but researchers utilising this approach aim to highlight the similarities rather than the differences. Those who utilise the ideographic approach on the other hand argue that individual differences are too important to ignore and aim to provide rich descriptions, however they run the risk of their findings relating only to the isolated case they have studied. Whatever the approach, description provides the basis for the second goal of the scientific method by providing data with which to make predictions. Prediction is the second goal of the scientific method. Using previously gathered data, it becomes possible to make predictions regarding the relationships between different variables. When scores on one variable can be said to predict scores on a second variable, those variables are said to be correlated. A correlation exists when two different measures on the same group of people vary together. While correlational relationships do not necessarily imply causation, they enable us to make predictions. Furthermore, it is not necessary to understand why the relationship exists; only that it is there. Explanation is the third goal of the scientific method. While description and prediction are important goals in science, they are only the first steps in our ability to understand and explain a phenomenon. In order to explain something, we must understand what causes it, and this is typically done through experimentation. By manipulating the independent variable to establish what effect this has on the dependent variable, we can make what is known as causal inference. This requires three conditions to be met: co-variation of events (one event changes with the other), a time-order relationship (one event begins after the other, established by manipulation of IV), and the elimination of plausible alternative causes (the removal or minimization of confounding variables). Confounding occurs when two IV’s are allowed to co-vary simultaneously, resulting in it becoming impossible to determine which is affecting the DV. Confounding can be reduced through control of manipulation, balancing (for age, gender), and by holding conditions constant. A study with minimal confounding can be said to have internal validity. Application is the fourth and final goal of the scientific method, and refers to the process by which the results of research are utilised in such a way as to benefit society as a whole. While basic research concerns itself with the pursuit of knowledge for knowledge’s sake, applied research seeks to use any knowledge gained to create change, ideally for the betterment of society.

Research Research is the process through which new information is discovered. It can take the form of primary or secondary research.

Research Cycle 

Asking a question

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Identifying the important factors

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Formulating a hypothesis

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Collecting relevant info

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Testing the hypothesis

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Working with the hypothesis

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Reconsidering the theory

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Asking new question

What Makes Good Research? 

Based on logical rationale and tied to theory

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Based on the work of others

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Is incremental

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Convergent Validity: Multi Method Approach

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Is feasible

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Can be replicated

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Generates new questions or is cyclical in nature

Quantitative Research Methods 

Independent Group Designs

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Within Subjects Design

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Complex Designs

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Correlational Designs

Independent Group Design Experimental methods are particularly effective at establishing cause and effect relationships through manipulation of one or more variables in two or more conditions. Psychologists perform experiments in order to test hypotheses they derive from psychological theories. If the results of the experiment are consistent with the prediction made by the hypothesis, the theory receives support. If not, then the theory may need to be modified and tested in a new experiment. In this way, the process of research can be considered to be iterative, changing constantly as new information is discovered. Experiments are also used to test the effectiveness of treatment programmes. The essential ingredient to any experiment is control. Experimental control is gained through manipulation, holding conditions constant (factors which might influence what we are studying), and balancing (making sure one group is not smarter etc than the other). An experiment can be said to have internal validity when it meets the three conditions for establishing causality; co-variation of events, a time-order relationship, and elimination of plausible alternatives. Similarly, an experiment can be said to have external validity when the findings can be generalised back to a large population, in different settings and conditions. External validity can be increased by replicating the results of a study or by increasing internal validity. In independent group design, a separate group is tested for each level of the independent variable. As such, in order to achieve external validity, balancing of groups is extremely important. One way this can be achieved is through random groups design. In a random groups design, subjects are randomly assigned to comparable groups before implementation of the IV. While this does not eliminate differences completely, it makes it probable that any differences that do exist will be equally spread out between conditions. In order to ensure random groups design works effectively, samples of sufficient size are required. One method of assigning participants to random groups is block randomization. Block randomization refers to a simple process of randomly assigning participants to conditions. For example, think of a study with five conditions, labelled A B C D E. The order of these is rejigged at random, for example A B C D E becoming B D A C E. The first five participants are then placed in each of these groups, and the order of the conditions is rejigged again and the process is repeated until all participants are assigned a group.

Advantages to this method include groups of equal size (important as number of observations per group can affect reliability), and balancing of time-related variables due to each condition being tested in each block (can also balance changes in experimenters or within the population itself). In summary, block randomization will balance any characteristics of participants across the conditions of an experiment.

Threats to Internal validity As previously mentioned, internal validity refers to the certainty with which we can say that subject performance on the dependent variable can be attributed clearly and unambiguously to the effect of the independent variable(s). In other words, internal validity refers to the elimination of confounding variables, otherwise known as threats to internal validity. Testing intact groups is one such threat to internal validity. This is when groups that were formed prior to the study are left intact and assigned to different experimental conditions. Individual differences may not be properly balanced across such groups and as such, they are a threat to internal validity. If intact groups must be used, a matched pairs design is more appropriate. Balancing of extraneous variables is another threat to internal validity. An extraneous variable is a variable that is not directly of interest to the researcher but which may still confound the experiment, such as the number of researchers present, imbalance between males and females, etc. Extraneous variables can be reduced through balancing (block randomization). Subject loss is yet another threat to internal validity. When subjects begin an experiment but fail to complete it successfully, internal validity can be threatened. Subject loss can be either mechanical or selective. Mechanical subject loss occurs when a subject fails to complete and experiment due to an equipment failure (can include computer crashing, reading wrong instructions, interruptions etc). Mechanical subject loss does not typically threaten internal validity as it should not result in any systematic differences between the groups. Selective subject loss has a more profound impact on internal validity. This occurs when (a) subjects are lost differentially across conditions, (b) when some characteristic of the subject is responsible for the loss, and (c) when that characteristic is related to the dependent variable being measured in the study. The resulting imbalance between groups means that the effect on the dependent variable cannot be attributed solely to the presence of the independent variable. The impact of selective subject loss on internal validity can be minimized or eliminated either by pretesting subjects so that those who could be considered likely to drop out are excluded from the start, or by giving all subjects a pretest and randomly assigning

them to a group. This way, should selective subject loss occur, a participant with a similar score can be removed from the other group, restoring balance and thus internal validity. Demand characteristics are yet another threat to internal validity. This is when participants expectations of how to behave while taking part in a study result in a bias, confounding the end result. This can be avoided using a placebo group. Finally, experimenter effects can be balanced using a double blind procedure.

Within Subjects Design A within subjects design is an experimental design in which all participants take part in all conditions. This may give rise to what are known as practice effects, where participant familiarity with the experimental procedures or reduced motivation may affect performance and behaviour. Similarly to the way individual differences between subjects can be reduced but not eliminated by using block randomization, so too can the same be said for any practice effects which result from participants taking part in multiple conditions. When balanced across conditions, practice effects will not confound the independent variable to a significant degree. A researcher may choose to employ a WSD for a variety of reasons, including the need for fewer participants, efficiency/convenience, to increase the sensitivity of the experiment, or to study the behaviour of subjects over time. For example, if the IV has 2 levels, an IGD would need twice as many participants as a WSD, which may not always be possible. It is more convenient as the researcher will be able to spend less time recruiting, scheduling, and explaining the experiment. With regard to sensitivity, WSD is typically more sensitive due to the fact that individual differences between conditions are removed, as there is usually more variation between people than within people. Finally, any form of longitudinal research by definition requires the use of WSD. Because all subjects take part in all conditions of a WSD, there can be no confounding due to individual differences that would typically arise in an IGD. WSD has its own unique concerns with regard to internal validity however, the first of which is known as a practice effect. Because all participants in a WSD are tested twice, they are likely to experience practice effects. These can result in improved performance (due to experience) or decreased performance (due to fatigue). As such, it is essential that the researcher balance these effects properly. This balance can be achieved using either a complete or incomplete design. These help to counterbalance any practice effect between conditions. In a complete design conditions are administered to all participants using a different order of presentation each time. This way, practice effects are balanced within all participants. Can be done either through block randomization of conditions or ABBA counterbalancing. In block randomization the order of presentation of conditions is randomized and each participant takes part in each condition a number of times. In ABBA counterbalancing, each participant takes part in each condition as little as twice, but this is only appropriate when practice effects can be considered to be linear and anticipation effects are considered to be unlikely.

In an incomplete design, practice effects are balanced across subjects. This can be done either through testing all possible orders of presentation or selected orders of presentation. In the former, each participant is randomly assigned to a potential order, for example AB or BA. This way, practice effects are balanced across subjects. Because the number of possible orders increases exponentially for each condition that is added, testing all possible orders is typically only employed when there are four or less conditions. Testing selected orders can be done either using a Latin square or by starting with a random order and rotating systematically. In the latin square, each condition appears in each ordinal position once. In independent group design, error is the individual differences between subjects, while in within subjects design, error is the difference in the way participants are affected in different conditions.

Beins & McCarthy (2011) Dyer (2006) Coolican (2009) Shaughnessy, Zechmeister & Zechmeister (2011)