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Chapter 3

 

RESEARCH OVERVIEW

 

 

The Scientific Method

The development of the scientific method is usually credited to Roger Bacon, a philosopher and scientist from 13th-century England, although some argue that the Italian scientist Galileo Galilei played an important role in formulating the scientific method. Later contributions to the scientific method were made by the philosophers Francis Bacon and René Descartes. Although some disagreement exists regarding the exact characteristics of the scientific method, most agree that it is characterized by the following elements:

• Empirical approach

• Observations

• Questions

• Hypotheses

• Experiments

• Analyses

• Conclusions

• Replication

 

       There has been some disagreement among researchers over the years

regarding the elements that compose the scientific method. In fact, some researchers have even argued that it is impossible to define a universal approach to scientific investigation. Nevertheless, for over 100 years, the scientific method has been the defining feature of scientific research. Researchers generally agree that the scientific method is composed of the following key elements (which will be the focus of the remainder of this

chapter): an empirical approach, observations, questions, hypotheses, experiments, analyses, conclusions, and replication.

       Before proceeding any further, one word of caution is necessary. In the brief discussion of the scientific method that follows, we will be introducing several new terms and concepts that are related to research design and methodology. Do not be intimidated if you are unfamiliar with some of the content contained in this discussion. The purpose of the following is simply to set the stage for the chapters that follow, and we will be elaborating on each of the terms and concepts throughout the remainder of the book.

 

Empirical Approach

The scientific method is firmly based on the empirical approach. The empirical

approach is an evidence-based approach that relies on direct observation and experimentation in the acquisition of new knowledge. In the empirical approach, scientific decisions are made based on the data derived from direct observation and experimentation. Contrast this approach to decision making with the way that most nonscientific decisions are made in our daily lives. For example, we have all made decisions based on feelings, hunches, or “gut” instinct. Additionally, we may often reach conclusions or make decisions that are not necessarily based on data, but rather on opinions, speculation, and a hope for the best. The empirical approach, with its emphasis on direct, systematic, and careful observation, is best thought of as the guiding principle behind all research conducted in accordance with the scientific method.

 

Observations

An important component in any scientific investigation is observation. In this sense, observation refers to two distinct concepts—being aware of the world around us and making careful measurements. Observations of the world around us often give rise to the questions that are addressed through scientific research. For example, the Newtonian observation that apples fall from trees stimulated much research into the effects of gravity. Therefore, a keen eye to your surroundings can often provide you with many ideas for research studies.

       In the context of science, observation means more than just observing the world around us to get ideas for research. Observation also refers to the process of making careful and accurate measurements, which is a distinguishing feature of well-conducted scientific investigations. When making measurements in the context of research, scientists typically take great precautions to avoid making biased observations. For example, if a researcher is observing the amount of time that passes between two events, such as the length of time that elapses between lightning and thunder, it would certainly be advisable for the researcher to use a measurement device that has a high degree of accuracy and reliability. Rather than simply trying to “guesstimate” the amount of time that elapsed between those two events, the researcher would be advised to use a stopwatch or similar measurement device. By doing so, the researcher ensures that the measurement is accurate and not biased by extraneous factors. Most people would likely agree that the observations that we make in our daily lives are rarely made so carefully or systematically.

       An important aspect of measurement is an operational definition. Researchers define key concepts and terms in the context of their research studies by using operational definitions. By using operational definitions, researchers ensure that everyone is talking about the same phenomenon. For example, if a researcher wants to study the effects of exercise on stress levels, it would be necessary for the researcher to define what “exercise” is. Does exercise refer to jogging, weight lifting, swimming, jumping rope, or all of the above? By defining “exercise” for the purposes of the study, the researcher makes sure that everyone is referring to the same thing. Clearly, the definition of “exercise” can differ from one study to another, so it is crucial that the researcher define “exercise” in a precise manner in the context of his or her study. Having a clear definition of terms also ensures that the researcher’s study can be replicated by other researchers

 

Questions

After getting a research idea, perhaps from making observations of the world around us, the next step in the research process involves translating that research idea into an answerable question. The term “answerable” is particularly important in this respect, and it should not be overlooked. It would obviously be a frustrating and ultimately unrewarding endeavor to attempt to answer an unanswerable research question through scientific investigation. An example of an unanswerable research question is the following: “Is there an exact replica of me in another universe?” Although this is certainly an intriguing question that would likely yield important information, the current state of science cannot provide an answer to that question. It is therefore important to formulate a research question that can be answered through available scientific methods and procedures. One might ask, for example, whether exercising (i.e., perhaps operationally defined as running three times per week for 30 minutes each time) reduces cholesterol levels. This question could be researched and answered using established scientific methods.

 

Hypotheses

The next step in the scientific method is coming up with a hypothesis, which is simply an educated—and testable—guess about the answer to your research question. A hypothesis is often described as an attempt by the researcher to explain the phenomenon of interest. Hypotheses can take various forms, depending on the question being asked and the type of study being conducted.

       A key feature of all hypotheses is that each must make a prediction. Remember that hypotheses are the researcher’s attempt to explain the phenomenon being studied, and that explanation should involve a prediction about the variables being studied. These predictions are then tested by gathering and analyzing data, and the hypotheses can either be supported or refuted  on the basis of the data. In their simplest forms, hypotheses are typically phrased as “if-then” statements. For example, a researcher may hypothesize that “ if people exercise for 30 minutes per day at least three days per week, then their cholesterol levels will be reduced.” This hypothesis makes a prediction about the effects of exercising on levels of cholesterol, and the prediction can be tested by gathering and analyzing data. Two types of hypotheses with which you should be familiar are the null hypothesis and the alternate (or experimental) hypothesis. The null hypothesis always predicts that there will be no differences between the groups being studied. By contrast, the alternate hypothesis predicts that there will be a difference between the groups. In our example, the null hypothesis would predict that the exercise group and the no-exercise group will not differ significantly on levels of cholesterol. The alternate hypothesis would predict that the two groups will differ significantly on cholesterol levels.

 

Experiments

After articulating the hypothesis, the next step involves actually conducting the experiment (or research study). For example, if the study involves investigating the effects of exercise on levels of cholesterol, the researcher would design and conduct a study that would attempt to address that question. As previously mentioned, a key aspect of conducting a research study is measuring the phenomenon of interest in an accurate and reliable manner  In this example, the researcher would collect data on the cholesterol levels of the study participants by using an accurate and reliable measurement device. Then, the researcher would compare the cholesterol levels of the two groups to see if exercise had any effects.

 

Analyses

After conducting the study and gathering the data, the next step involves

analyzing the data, which generally calls for the use of statistical techniques. The type of statistical techniques used by a researcher depends on the design of the study, the type of data being gathered, and the questions being asked. Although a detailed discussion of statistics is beyond the scope of this text, it is important to be aware of the role of statistics in conducting a research study. In short, statistics help researchers minimize the likelihood of reaching an erroneous conclusion about the relationship between the variables being studied.

       A key decision that researchers must make with the assistance of statistics is whether the null hypothesis should be rejected. Remember that the null hypothesis always predicts that there will be no difference between the groups. Therefore, rejecting the null hypothesis means that there is a difference between the groups. In general, most researchers seek to reject the

null hypothesis because rejection means the phenomenon being studied (e.g., exercise, medication) had some effect.

       It is important to note that there are only two choices with respect to the null hypothesis. Specifically, the null hypothesis can be either rejected or not rejected, but it can never be accepted. If we reject the null hypothesis, we are concluding that there is a significant difference between the groups. If, however, we do not reject the null hypothesis, then we are concluding that we were unable to detect a difference between the groups. To be clear, it does not mean that there is no difference between the two groups. There may in actuality have been a significant difference between the two groups, but we were unable to detect that difference in our study.

       The decision of whether to reject the null hypothesis is based on the results of statistical analyses, and there are two types of errors that researchers must be careful to avoid when making this decision—Type I errors and Type II errors. A Type I error occurs when a researcher concludes that there is a difference between the groups being studied when, in fact, there is no difference. This is sometimes referred to as a “false positive.” By contrast, a Type II error occurs when the researcher concludes that there is not a difference between the two groups being studied when, in fact, there is a difference. This is sometimes referred to as a “false negative.” As previously noted, the conclusion regarding whether there is a difference between the groups is based on the results of statistical analyses. Specifically, with a Type I error, although there is a statistically significant result, it occurred by chance (or error) and there is not actually a difference between the two groups (Wampold, Davis, & Good, 2003). With a Type II error, there is a nonsignificant statistical result when, in fact, there actually is a difference between the two groups (Wampold et al.).

       The typical convention in most fields of science allows for a 5% chance

of erroneously rejecting the null hypothesis (i.e., of making a Type I error). In other words, a researcher will conclude that there is a significant difference between the groups being studied (i.e., will reject the null hypothesis) only if the chance of being incorrect is less than 5%. For obvious reasons, researchers want to reduce the likelihood of concluding that there is a significant difference between the groups being studied when, in fact, there

is not a difference.

       The distinction between Type I and Type II errors is very important, although somewhat complicated. An example may help to clarify these terms. In our example, a researcher conducts a study to determine whether a new medication is effective in treating depression. The new medication is given to Group 1, while a placebo medication is given to Group 2. If, at the conclusion of the study, the researcher concludes that there is a significant difference in levels of depression between Groups 1 and 2 when, in fact, there is no difference, the researcher has made a Type I error. In simpler terms, the researcher has detected a difference between the groups that in actuality does not exist; the difference between the groups occurred by chance (or error). By contrast, if the researcher concludes that there is no significant difference in levels of depression between Groups 1 and 2 when, in fact, there is a difference, the researcher has made a Type II error. In simpler terms, the researcher has failed to detect a difference that actually exists between the groups.

       Which type of error is more serious—Type I or Type II? The answer to this question often depends on the context in which the errors are made. Let’s use the medical context as an example. If a doctor diagnoses a patient with cancer when, in fact, the patient does not have cancer (i.e., a false positive), the doctor has committed a Type I error. In this situation, it is likely that the erroneous diagnosis will be discovered (perhaps through a second opinion) and the patient will undoubtedly be relieved. If, however, the doctor gives the patient a clean bill of health when, in fact, the patient actually has cancer (i.e., a false negative), the doctor has committed a Type II error. Most people would likely agree that a Type II error would be more serious in this example because it would prevent the patient from getting necessary medical treatment.

       You may be wondering why researchers do not simply set up their research studies so that there is even less chance of making a Type I error. For example, wouldn’t it make sense for researchers to set up their research studies so that the chance of making a Type I error is less than 1% or, better yet, 0%? The reason that researchers do not set up their studies in this manner has to do with the relationship between making Type I errors and making Type II errors. Specifically, there is an inverse relationship between Type I errors and Type II errors, which means that by decreasing the probability of making a Type I error, the researcher is increasing the probability of making a Type II error. In other words, if a researcher reduces the probability of making a Type I error from 5% to 1%, there is now an increased probability that the researcher will make a Type II error by failing to detect a difference that actually exists. The 5% level is a standard convention in most fields of research and represents a compromise between making Type I and Type II errors.

 

Conclusions

After analyzing the data and determining whether to reject the null hypothesis, the researcher is now in a position to draw some conclusions about the results of the study. For example, if the researcher rejected the null hypothesis, the researcher can conclude that the phenomenon being studied had an effect—a statistically significant effect, to be more precise. If the researcher rejects the null hypothesis in our exercise-cholesterol example, the researcher is concluding that exercise had an effect on levels of cholesterol.

       It is important that researchers make only those conclusions that can be supported by the data analyses. For example, if a researcher conducted a correlational study and the results indicated that the two things being studied were strongly related, the researcher could not conclude that one thing caused the other. An oft-repeated statement that will be explained in later chapters is that correlation (i.e., a relationship between two things) does not equal causation. In other words, the fact that two things are related does not mean that one caused the other.

 

Replication

One of the most important elements of the scientific method is replication. Replication essentially means conducting the same research study a second time with another group of participants to see whether the same results are obtained (see Kazdin, 1992; Shaughnessy & Zechmeister, 1997). The same researcher may attempt to replicate previously obtained results, or perhaps other researchers may undertake that task. Replication illustrates an important point about scientific research—namely, that researchers should avoid drawing broad conclusions based on the results of a single research study because it is always possible that the results of that particular study were an aberration. In other words, it is possible that the results of the research study were obtained by chance or error and, therefore, that the results may not accurately represent the actual state of things. However, if the results of a research study are obtained a second time (i.e., replicated), the likelihood that the original study’s findings were obtained by chance or error is greatly reduced.

       The importance of replication in research cannot be overstated. Replication serves several integral purposes, including establishing the reliability (i.e., consistency) of the research study’s findings and determining whether the same results can be obtained with a different group of participants. This last point refers to whether the results of the original study are generalizable to other groups of research participants. If the results of a study are replicated, the researchers—and the field in which the researchers work—can have greater confidence in the reliability and generalizability of the original findings.

 

 

 

 

Tue, 10 May 2011 @23:12

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