CAUSAL STUDY DESIGN

A causal comparative research (which is also called ex post facto research) is a design where a researcher attempts to determine the possible cause-effect relationship by observing some existing consequences of recurring events and searching back through data. The researcher does not have direct control of independent variables because they have already occurred.

Causality studies may be thought of as understanding a phenomenon in terms of conditional statements in the form, “If X, then Y.” This type of research is used to measure what impact a specific change will have on existing norms and assumptions. Most social scientists seek causal explanations that reflect tests of hypotheses. Causal effect (nomothetic perspective) occurs when variation in one phenomenon, an independent variable, leads to or results, on average, in variation in another phenomenon, the dependent variable.

Conditions necessary for determining causality:

·Empirical association--a valid conclusion is based on finding an association between the independent variable and the dependent variable.

·Appropriate time order--to conclude that causation was involved, one must see that cases were exposed to variation in the independent variable before variation in the dependent variable.

·Nonspuriousness--a relationship between two variables that is not due to variation in a third variable.

What causality studies tell you

1. Causality research designs helps researchers understand why the world works the way it does through the process of proving a causal link between variables and eliminating other possibilities.

2. Replication is possible.

3. There is greater confidence the study has internal validity due to the systematic subject selection and equity of groups being compared.

What causality studies don't tell you

1. Not all relationships are casual! The possibility always exists that, by sheer coincidence, two unrelated events appear to be related [e.g., Punxatawney Phil could accurately predict the duration of Winter for five consecutive years but, the fact remains, he's just a big, furry rodent].

2. Conclusions about causal relationships are difficult to determine due to a variety of extraneous and confounding variables that exist in a social environment. This means causality can only be inferred, never proven.

3. If two variables are correlated, the cause must come before the effect. However, even though two variables might be causally related, it can sometimes be difficult to determine which variable comes first and therefore to establish which variable is the actual cause and which is the actual effect.

COHORT DESIGN:

Often used in the medical sciences, but also found in the applied social sciences, a cohort study generally refers to a study conducted over a period of time involving members of a population which the subject or representative member comes from, and who are united by some commonality or similarity. Using a quantitative framework, a cohort study makes note of statistical occurrence within a specialized subgroup, united by same or similar characteristics that are relevant to the research problem being investigated, rather than studying statistical occurrence within the general population. Using a qualitative framework, cohort studies generally gather data using methods of observation. Cohorts can be either "open" or "closed."

·Open Cohort Studies [dynamic populations, such as the population of Los Angeles] involve a population that is defined just by the state of being a part of the study in question (and being monitored for the outcome). Date of entry and exit from the study is individually defined, therefore, the size of the study population is not constant. In open cohort studies, researchers can only calculate rate based data, such as, incidence rates and variants thereof.

·Closed Cohort Studies [static populations, such as patients entered into a clinical trial] involve participants who enter into the study at one defining point in time and where it is presumed that no new participants can enter the cohort. Given this, the number of study participants remains constant (or can only decrease).

What cohort studies tell you

1. The use of cohorts is often mandatory because a randomized control study may be unethical. For example, you cannot deliberately expose people to asbestos, you can only study its effects on those who have already been exposed. Research that measures risk factors often relies on cohort designs.

2. Because cohort studies measure potential causes before the outcome has occurred, they can demonstrate that these “causes” preceded the outcome, thereby avoiding the debate as to which is the cause and which is the effect.

3. Cohort analysis is highly flexible and can provide insight into effects over time and related to a variety of different types of changes [e.g., social, cultural, political, economic, etc.].

4. Either original data or secondary data can be used in this design.

What cohort studies don't tell you

1. In cases where a comparative analysis of two cohorts is made [e.g., studying the effects of one group exposed to asbestos and one that has not], a researcher cannot control for all other factors that might differ between the two groups. These factors are known as confounding variables.

2. Cohort studies can end up taking a long time to complete if the researcher must wait for the conditions of interest to develop within the group. This also increases the chance that key variables change during the course of the study, potentially impacting the validity of the findings.

3. Because of the lack of randominization in the cohort design, its external validity is lower than that of study designs where the researcher randomly assigns participants.

CROSS –SECTIONAL DESIGN:

Cross-sectional research designs have three distinctive features: no time dimension, a reliance on existing differences rather than change following intervention; and, groups are selected based on existing differences rather than random allocation. Thecross-sectional design can only measure diffrerences between or from among a variety of people, subjects, or phenomena rather than change. As such, researchers using this design can only employ a relative passive approach to making causal inferences based on findings.

What cross-sectional research designs tell you

1. Cross-sectional studies provide a 'snapshot' of the outcome and the characteristics associated with it, at a specific point in time.

2.Unlike the experimental design where there is an active intervention by the researcher to produce and measure change or to create differences, cross- sectional designs focus on studying and drawing inferences from existing differences between people, subjects, or phenomena.

3. Entails collecting data at and concerning one point in time. While longitudinal studies involve taking multiple measures over an extended period of time,cross-sectional research is focused on finding relationships between variables at one moment in time.

4. Groups identified for study are purposely selected based upon existing differences in the sample rather than seeking random sampling.

5. Cross-section studies are capable of using data from a large number of subjects and, unlike observational studies, are not geographically bound.

6. Can estimate prevalence of an outcome of interest because the sample is usually taken from the whole population.

7. Because cross-sectional designs generally use survey techniques to gather data, they are relatively inexpensive and take up little time to conduct.

What Cross-sectional research designs don't tell you

1. Finding people, subjects, or phenomena to study that are very similar except in one specific variable can be difficult.

2. Results are static and time bound and, therefore, give no indication of a sequence of events or reveal historical contexts.

3. Studies cannot be utilized to establish cause and effect relationships.

4. Provide only a snapshot of analysis so there is always the possibility that a study could have differing results if another time-frame had been chosen.

5. There is no follow up to the findings.

Date: 2014-12-28; view: 1203