Establishing Cause and Effect - Scientific Causality
WHAT CAUSES WHAT? Five criteria should be considered in trying to establish a causal relationship. The first three criteria are generally considered as requirements for identifying lation does not prove causation”? It is meant to No cause has its effect apart from some larger context involving other vari- ables. When, for. What are three criteria you consider to be the most important in a quality system? Note that the relationship between a cause and an effect is not limited to just. By adopting a model of cause and effect, scientists produce knowledge that can .. at the three criteria of causation above, you will notice that the relationship.
Let's put this same syllogism in program evaluation terms: This provides evidence that the program and outcome are related. Notice, however, that this syllogism doesn't not provide evidence that the program caused the outcome -- perhaps there was some other factor present with the program that caused the outcome, rather than the program. The relationships described so far are rather simple binary relationships. Sometimes we want to know whether different amounts of the program lead to different amounts of the outcome -- a continuous relationship: It's possible that there is some other variable or factor that is causing the outcome.
This is sometimes referred to as the "third variable" or "missing variable" problem and it's at the heart of the issue of internal validity. What are some of the possible plausible alternative explanations? Just go look at the threats to internal validity see single group threatsmultiple group threats or social threats -- each one describes a type of alternative explanation.
In order for you to argue that you have demonstrated internal validity -- that you have shown there's a causal relationship -- you have to "rule out" the plausible alternative explanations. How do you do that? One of the major ways is with your research design.
Let's consider a simple single group threat to internal validity, a history threat. Let's assume you measure your program group before they start the program to establish a baselineyou give them the program, and then you measure their performance afterwards in a posttest.
You see a marked improvement in their performance which you would like to infer is caused by your program. One of the plausible alternative explanations is that you have a history threat -- it's not your program that caused the gain but some other specific historical event. For instance, it's not your anti-smoking campaign that caused the reduction in smoking but rather the Surgeon General's latest report that happened to be issued between the time you gave your pretest and posttest.
How do you rule this out with your research design? One of the simplest ways would be to incorporate the use of a control group -- a group that is comparable to your program group with the only difference being that they didn't receive the program.
But they did experience the Surgeon General's latest report. If you find that they didn't show a reduction in smoking even though they did experience the same Surgeon General report you have effectively "ruled out" the Surgeon General's report as a plausible alternative explanation for why you observed the smoking reduction. A dependent variable is a variable whose values or qualities are presumed to change as a result of the independent variable.
In other words, the value or quality of a dependent variable depends on the value of the independent variable. Of course, this assumes that there is an actual relationship between the two variables.
If there is no relationship, then the value or quality of the dependent variable does not depend on the value of the independent variable. An independent variable is a variable whose value or quality is manipulated by the experimenter or, in the case of non-experimental analysis, changes in the society and is measured or observed systematically. Perhaps an example will help clarify. Promotion would be the dependent variable. Change in promotion is hypothesized to be dependent on gender.
Scientists use whatever they can — their own creativity, ideas from other fields, induction, deduction, systematic guessing, etc. There are no definitive guidelines for the production of new hypotheses.
The history of science is filled with stories of scientists claiming a flash of inspiration, or a hunch, which then motivated them to look for evidence to support, refute, or refine their idea or develop an entirely new framework. Prediction[ edit ] A useful quantitative hypothesis will enable predictions, by deductive reasoning, that can be experimentally assessed.
If results contradict the predictions, then the hypothesis under examination is incorrect or incomplete and requires either revision or abandonment. If results confirm the predictions, then the hypothesis might be correct but is still subject to further testing. Predictions refer to experimental designs with a currently unknown outcome. A prediction of an unknown differs from a consequence which can already be known.
Testing[ edit ] Once a prediction is made, a method is designed to test or critique it.9 Types of Hugs Will Shed Light on Your Relationship
The investigator may seek either confirmation or falsification of the hypothesis, and refinement or understanding of the data. Though a variety of methods are used by both natural and social scientists, laboratory experiments remain one of the most respected methods by which to test hypotheses. Scientists assume an attitude of openness and accountability on the part of those conducting an experiment. Detailed record keeping is essential, to aid in recording and reporting on the experimental results, and providing evidence of the effectiveness and integrity of the procedure.
They will also assist in reproducing the experimental results.
Introduction to Sociology/Sociological Methods - Wikibooks, open books for an open world
This is a diagram of the famous Milgram Experiment which explored obedience and authority in light of the crimes committed by the Nazis in World War II. The experiment's integrity should be ascertained by the introduction of a control or by observation of existing controls in natural settings. In experiments where controls are observed rather than introduced, researchers take into account potential variables e.
On the other hand, in experiments where a control is introduced, two virtually identical experiments are run, in only one of which the factor being tested is varied.
This serves to further isolate any causal phenomena. For example in testing a drug it is important to carefully test that the supposed effect of the drug is produced only by the drug.
Doctors may do this with a double-blind study: Neither the patients nor the doctor know who is getting the real drug, isolating its effects. This type of experiment is often referred to as a true experiment because of its design. It is contrasted with alternative forms below. Once an experiment is complete, a researcher determines whether the results or data gathered are what was predicted or assumed in the literature beforehand.
If the experiment appears successful - i. An experiment is not an absolute requirement. In observation based fields of science actual experiments must be designed differently than for the classical laboratory based sciences. Sociologists are more likely to employ quasi-experimental designs where data are collected from people by surveys or interviews, but statistical means are used to create groups that can be compared.
For instance, in examining the effects of gender on promotions, sociologists may control for the effects of social class as this variable will likely influence the relationship. Unlike a true experiment where these variables are held constant in a laboratory setting, quantitative sociologists use statistical methods to hold constant social class or, better stated, partial out the variance accounted for by social class so they can see the relationship between gender and promotions without the interference of social class.
The four components of research described above are integrated into the following steps of the research process. Identify your topic of interest and develop a research question in the form of a cause-and-effect relationship. Conduct a review of the literature: Access studies that have already been performed by other researchers and published in peer-reviewed journals.
You'll find out what is already known about the topic and where more research is needed.
Refine your research question in a way that will add new information to the existing research literature, expressing it in the form of a testable research hypothesis. This includes identifying two or more variables and articulating how one variable is thought to influence the other. Decide on a way to approach data collection that will provide a meaningful test of the research hypothesis. Some designs include data collection at only one point in time, but more complex questions require data gathering over time and with different groups of people.
Select a research method: Once a design has been established, one or more actual data gathering strategies will need to be identified. Each method comes with its own strengths and weaknesses, so sociologists are increasingly incorporating mixed-methods approaches in their research designs to enrich their knowledge of the topic. Some of the more popular research methods used by sociologists are: Operationalizing means deciding exactly how each variable of interest will be measured.
In survey research, this means deciding on the exact wording of the question or questions used to measure each variable, a listing of all possible responses to closed-ended questions, and a decision as to how to compute variables using multiple indicators. Identify the population and draw a sample: A population is the group a researcher is interested in learning about.
Social Research Methods - Knowledge Base - Establishing Cause & Effect
Is it all students at one particular University? All residents of the United States? All nonprofit organizations in a particular city?
Because it is frequently too expensive to try to collect data from all units in a population, a sample of those units is often selected. Samples that use principles of random selection, where every unit in the population has an equal chance of being included in the sample, have the best chance of reflecting the views and behaviors of the entire population of focus. Data collection must be systematic and rigorous so that procedural mistakes do not create artificial results. Powerful statistical packages today make data analysis easier than it has ever been.
Still, great care needs to be taken to accurately code the data i. Research results are shared with the larger community through presentations, reports, and publications in peer-reviewed journals. This allows others to consider the findings, the methods used, and any limitations of the study. Qualitative sociologists generally employ observational and analytic techniques that allow them to contextualize observed patterns in relation to existing hierarchies or assumptions within natural settings.
Thus, while the true experiment is ideally suited for the performance of quantitative science, especially because it is the best quantitative method for deriving causal relationships, other methods of hypothesis testing are commonly employed in the social sciences, and qualitative methods of critique and analysis are utilized to fact check the assumptions and theories created upon the basis of "controlled" rather than natural circumstances.
Evaluation and Iteration[ edit ] The scientific process is iterative. At any stage it is possible that some consideration will lead the scientist to repeat an earlier part of the process.
For instance, failure of a hypothesis to produce interesting and testable predictions may lead to reconsideration of the hypothesis or of the definition of the subject. It is also important to note that science is a social enterprise, and scientific work will become accepted by the community only if it can be verified and it "makes sense" within existing scientific beliefs and assumptions about the world when new findings complicate these assumptions and beliefs, we generally witness paradigm shifts in science .
All scientific knowledge is in a state of flux, for at any time new evidence could be presented that contradicts a long-held hypothesis, and new perspectives e. For this reason, scientific journals use a process of peer reviewin which scientists' manuscripts are submitted by editors of scientific journals to usually one to three fellow usually anonymous scientists familiar with the field for evaluation.
The referees may or may not recommend publication, publication with suggested modifications, or, sometimes, publication in another journal. Sometimes peer review inhibits the circulation of unorthodox work, and at other times may be too permissive.
The peer review process is not always successful, but has been very widely adopted by the scientific community. The reproducibility or replication of quantitative scientific observations, while usually described as being very important in a scientific method, is actually seldom reported, and is in reality often not done. Referees and editors often reject papers purporting only to reproduce some observations as being unoriginal and not containing anything new. Occasionally reports of a failure to reproduce results are published - mostly in cases where controversy exists or a suspicion of fraud develops.
The threat of failure to replicate by others as well as the ongoing qualitative enterprise designed to explore the veracity of quantitative findings in non-controlled settingshowever, serves as a very effective deterrent for most quantitative scientists, who will usually replicate their own data several times before attempting to publish.
Sometimes useful observations or phenomena themselves cannot be reproduced in fact, this is almost always the case in qualitative science spanning physical and social science disciplines. They may be rare, or even unique events. Reproducibility of quantitative observations and replication of experiments is not a guarantee that they are correct or properly understood.
Errors can all too often creep into more than one laboratory or pattern of interpretation mathematical or qualitative utilized by scientists. Correlation and Causation[ edit ] This diagram illustrates the difference between correlation and causation, as ice cream consumption is correlated with crime, but both are dependent on temperature. Thus, the correlation between ice cream consumption and crime is spurious.
In the scientific pursuit of quantitative prediction and explanation, two relationships between variables are often confused: While these terms are rarely used in qualitative science, they lie at the heart of quantitative methods, and thus constitute a cornerstone of scientific practice.
Correlation refers to a relationship between two or more variables in which they change together.
- Establishing Cause & Effect
- Introduction to Sociology/Sociological Methods
- Establishing Cause and Effect
A positive correlation means that as one variable increases e. A negative correlation is just the opposite; as one variable increases e. Causation refers to a relationship between two or more variables where one variable causes the other.
In order for a variable to cause another, it must meet the following three criteria: Ice cream consumption is positively correlated with incidents of crime. Employing the quantitative method outlined above, the reader should immediately question this relationship and attempt to discover an explanation.
It is at this point that a simple yet noteworthy phrase should be introduced: If you look back at the three criteria of causation above, you will notice that the relationship between ice cream consumption and crime meets only one of the three criteria they change together.
The real explanation of this relationship is the introduction of a third variable: Ice cream consumption and crime increase during the summer months. Thus, while these two variables are correlated, ice cream consumption does not cause crime or vice versa.
Both variables increase due to the increasing temperatures during the summer months. It is often the case that correlations between variables are found but the relationship turns out to be spurious. Clearly understanding the relationship between variables is an important element of the quantitative scientific process. Quantitative and Qualitative[ edit ] Like the distinction drawn between positivist sociology and Verstehen sociology, there is - as noted above in the elaboration of general scientific methods - often a distinction drawn between two types of sociological investigation: For instance, social class, following the quantitative approach, can be divided into different groups - upper- middle- and lower-class - and can be measured using any of a number of variables or a combination thereof: Quantitative sociologists also utilize mathematical models capable of organizing social experiences into a rational order that may provide a necessary foundation for more in depth analyses of the natural world importantly, this element of quantitative research often provides the initial or potential insights that guide much theoretical and qualitative analyses of patterns observed - numerically or otherwise - beyond the confines of mathematical models.
Quantitative sociologists tend to use specific methods of data collection and hypothesis testing, including: Further, quantitative sociologists typically believe in the possibility of scientifically demonstrating causation, and typically utilize analytic deduction e.
Finally, quantitative sociologists generally attempt to utilize mathematical realities e. Qualitative methods of sociological research tend to approach social phenomena from the Verstehen perspective. Rather than attempting to measure or quantify reality via mathematical rules, qualitative sociologists explore variation in the natural world people may see, touch, and experience during their lives.
As such, these methods are primarily used to a develop a deeper understanding of a particular phenomenon, b explore the accuracy or inaccuracy of mathematical models in the world people experience, c critique and question the existing assumptions and beliefs of both scientists and other social beings, and d refine measurements and controls used by quantitative scientists via insights gleaned from the experiences of actual people.
While qualitative methods may be used to propose or explore relationships between variables, these studies typically focus on explicating the realities people experience that lie at the heart or foundation of such relationships rather than focusing on the relationships themselves. Qualitatively oriented sociologists tend to employ different methods of data collection and analysis, including: Further, qualitative sociologists typically reject measurement or quantities essential to quantitative approaches and the notion or belief in causality e.
Finally, qualitative sociologists generally attempt to utilize natural realities e. While there are sociologists who employ and encourage the use of only one or the other method, many sociologists see benefits in combining the approaches. They view quantitative and qualitative approaches as complementary.
Results from one approach can fill gaps in the other approach. For example, quantitative methods could describe large or general patterns in society while qualitative approaches could help to explain how individuals understand those patterns. Similarly, qualitative patterns in society can reveal missing pieces in the mathematical models of quantitative research while quantitative patterns in society can guide more in-depth analysis of actual patterns in natural settings.
In fact, it is useful to note that many of the major advancements in social science have emerged in response to the combination of quantitative and qualitative techniques that collectively created a more systematic picture of probable and actual social conditions and experiences.
Subjective[ edit ] Sociologists, like all humans, have values, beliefs, and even pre-conceived notions of what they might find in doing their research. Because sociologists are not immune to the desire to change the world, two approaches to sociological investigation have emerged. By far the most common is the objective approach advocated by Max Weber. Weber recognized that social scientists have opinions, but argued against the expression of non-professional or non-scientific opinions in the classroom.
Weber did argue that it was acceptable for social scientists to express their opinions outside of the classroom and advocated for social scientists to be involved in politics and other social activism. The objective approach to social science remains popular in sociological research and refereed journals because it refuses to engage social issues at the level of opinions and instead focuses intently on data and theories.
The objective approach is contrasted with the critical approach, which has its roots in Karl Marx's work on economic structures.