In addition to unprotected sexual intercourse other variables must be present: Both partners must be fertile and the sex act must occur while the woman is ovulating. It is very difficult to establish conditional causality. Let's say that the researcher is studying the links between advertising spending and retail sales. It is nearly impossible to establish a conditional causal link between increased advertising spending and increased retail sales.
While there is probably a strong correlation between advertising spending levels and retail sales, as a former advertising executive I can attest that there are occasions when increased advertising spending may result in reduced retail sales. And, there are times when sales increase when a brand lacks advertising support. We could not, therefore, demonstrate conditional causality because advertising is not a necessary condition for retail sales. Contributory causality is the weakest form of causality.
Contributory causality is when the cause is neither necessary nor sufficient to bring about the effect. With contributory causality a change in the cause is associated with a change in the effect. Contributory causality does not require all variables that experience the cause to demonstrate the effect.
The cause, therefore, is neither necessary nor sufficient, but it does contribute to the effect. Causal marketing research can establish contributory causality.
Researchers could probably establish a contributory causal link between advertising spending and retail sales. With causal statements, the researchers must avoid a post hoc fallacy. A post hoc fallacy is based on the Latin expression.
This fallacy is based on the hasty conclusions that there is a causal relationship between two variables merely because the presumed cause precedes the effect.
Here is an example of a post hoc fallacy. Maria is years-old and drinks a glass of Tropicana Orange Juice everyday at breakfast. Maria does not have cancer. Therefore, drinking Tropicana Orange Juice is an effective part of a cancer prevention regime. Validity, as we discussed in our lesson on measurement, is the extent to which a measurement is free from sampling errors and systematic errors.
With experiments there are two kinds of validity that concern marketing researchers: Internal Validity and External Validity. Internal Validity refers to the extent to which variations in the response or dependent variable are due to changes in the independent or predictor variable.
Laboratory experiments tend to have higher internal validity and experiments conducted in the field—the marketplace—because the laboratory situation makes in easier to isolate the impact of other variables on the dependent variable.
External Validity is to which the extent the causal relationship measured in an experiment can be generalized to the population.
Field experiments offer higher levels of external validity than laboratory experiments. Extraneous variables are factors that may confound a researcher's ability to demonstrate causation.
Here is a partial list of extraneous variables marketing researchers confront:. History refers to events that are external to the experiment. These events occur at the same time as the experiment. History makes it more difficult for marketing researchers to get a "clean read" from their test market because the change in the dependent variable may be due to historical events and not the study's independent variables.
The longer the experiment the greater the probability that history will impact the research. Here is an example of this phenomenon from my own career. We made this trip to visit retailers.
Our goal was to check shelf placement, in-store displays, and the availability of our "saleable sample. As a consequence, Clairol's saleable sample tactic failed to achieve the hoped form levels of trial. Maturation refers to changes that occur to the test subjects during a test market that are not related to the test market. Maturation effects the test market.
The target markets preferences may change because of maturation factors—changes in test subjects' demographics, psychographics, usage behaviors rather than the test variables. The longer the test market, the more likely it will suffer from maturation. Imagine a two-year experiment conducted among teenagers for an Acne remedy. The normal aging of test subjects is a maturation effect, which could severely limit researchers' attempt to make sound conclusions from their findings.
Being part of an experiment changes people and could confound the results. The mere fact of being observed can cause people to change their attitudes and behavior. Advertisers frequently use "pre-post" persuasion tests to measure the effectiveness of advertising.
But, such tests can have a testing effect. The fact that people are asked to discuss their purchase intent before seeing an advertisement may influence their perception of the advertisement. Mortality refers to the loss of test subjects over time. This is an especially serious problem with longitudinal tests that measure test variables over a long period of time.
If a researcher is going to get a read of consumers' attitudes over several time periods, the impact of people dropping out of the study can undermine the validity of the study. Selection bias is an extraneous variable that undermines an experiment's validity. Selection bias occurs when the test group or control group is significantly different from the population in purports to represent.
Let's go back to Clairol's test market in Green Bay. Well, when conducting field experiments, marketing researchers look for small, relatively isolated markets, to represent the United States. The goal is to find markets that are "Little USAs. These extraneous variables are often called confounding variables as they undermine, or confound, the market researcher's ability to draw clear conclusions from an experiment.
When conducting an experiment, researchers attempt to control the influence of extraneous variables. Here are some of the techniques they use:. With an experimental research design, the researcher lays out how he or she will manipulate one of more independent variables and measure their effect on the dependent variable.
Some research designs involve no manipulation of independent variables. These non-experimental designs are called ex post facto , or after the effect, studies. Pre-Experimental Designs are the simplest form of experimental research designs. Pre-experimental designs have little or no control over extraneous variables. And, these designs do not randomly assign subjects to different treatments.
As a consequence, the results of a test using a pre-experimental design are difficult to interpret. These designs are often used in testing television commercials because they are simple and relatively inexpensive. There are three types of pre-experimental designs: With a one-shot case study, test units—people, test markets, etc.
The standard notion for a treatment is the symbol "X. There is no random assignment of test subjects as there is only one treatment, and there is no control. Here is the standard notation for a One-Shot Case Study:. This research design has two significant flaws: A control group would, in this case, be a group that did not receive the treatment.
Without these restraints, this research design cannot establish internal or external validity. Despite these limitations, market researchers often use this design for testing new-to-the-market products. With this research design the test unit is measured twice, one before the test and once after the test. There is still no control group; which is to say, a group not receiving the treatment.
Here is the standard notation for a one-group pre-test - post-test study:. Marketing researchers often use this design to test changes in the marketing plan for established products. Compared to One-Shot Case Studies, this design has the advantage of taking two measurements: This allows the researcher to estimate the treatment effect by subtracting the pre-test measure from the post-test measure.
But, given the lack of a control, the validity of the conclusions are questionable. Extraneous variables like history can affect the results because the observed changes in the dependent variable might be due to factors outside the research design.
And, maturation can also be a problem as the observed changes to the dependent variable might be due to changes in the test subjects that are not related to the treatment. The experimental group is exposed to the treatment while the control group is not. Test units, however, are not randomly assigned to the control or experimental groups. Here is the standard notation for a Static Group study:. Measurements for both groups are made after the treatment is administered to the experimental group.
The treatment effect is measured as O 1 - O 2. Weaknesses of this research design stem from the fact that test units are not randomly assigned to the experimental or control groups and there are no pre-test measurements taken. True Experimental Designs are where the market researchers assign test units to treatments at random.
There are three basic types of True Experimental Designs: With this research design, test units are randomly assigned to the experimental and control groups.
The experimental group is exposed to the treatment and then both the experimental and control groups are measured. But, there is only one measurement is taken. The advantage of this research design is that the random assignment of the test units should produce roughly equal control and experimental groups before the treatment is administered.
And, the mortality for the control and experimental groups should be similar. With this research design, test units are randomly assigned to experimental and control groups. A pre-test measure is taken from both groups. Selection bias is controlled by the randomized assignments of test units. Mortality can be a problem if it is not relatively equal between the experimental and control groups. Other confounding influences must be controlled for so they don't distort the results, either by holding them constant in the experimental creation of data, or by using statistical methods.
There are often much deeper psychological considerations that even the respondent may not be aware of. There are two research methods for exploring the cause-and-effect relationship between variables:. Experiments are typically conducted in laboratories where many or all aspects of the experiment can be tightly controlled to avoid spurious results due to factors other than the hypothesized causative factor s.
Many studies in physics , for example, use this approach. Alternatively, field experiments can be performed, as with medical studies in which subjects may have a great many attributes that cannot be controlled for but in which at least the key hypothesized causative variables can be varied and some of the extraneous attributes can at least be measured. Field experiments also are sometimes used in economics , such as when two different groups of welfare recipients are given two alternative sets of incentives or opportunities to earn income and the resulting effect on their labor supply is investigated.
In areas such as economics , most empirical research is done on pre-existing data, often collected on a regular basis by a government. Multiple regression is a group of related statistical techniques that control for attempt to avoid spurious influence from various causative influences other than the ones being studied. If the data show sufficient variation in the hypothesized explanatory variable of interest, its effect if any upon the potentially influenced variable can be measured.
From Wikipedia, the free encyclopedia.
Causal research, also called explanatory research, is the investigation of (research into) cause-and-effect relationships.    To determine causality, it is important to observe variation in the variable assumed to cause the change in the other variable(s), and then measure the changes in the other variable(s).
Causal effect (idiographic perspective)When a series of concrete events, thoughts, or actions result in a particular event or indiv idual outcome. Example of an idiographic causal effect: An individual is .
Causal research, also known as explanatory research is conducted in order to identify the extent and nature of cause-and-effect relationships. Causal research can be conducted in order to assess impacts of specific changes on existing norms, various processes etc. Causal research should be looked at as experimental research. Remember, the goal of this research is to prove a cause and effect relationship. With this in mind, it becomes very important to have strictly planned parameters and objectives.
Definition of causal research: The investigation into an issue or topic that looks at the effect of one thing or variable on another. For example, causal research might be used in a business environment to quantify the effect that. Causal research can help businesses determine how changes they make will affect operations, so it's helpful for planning. Other fields such as science and economics also use this kind of research, and experimentation and statistical research may be performed to .