Causal-Comparative Design - SAGE Research Methods
This animation explains the concept of correlation and causation. The objective of much research or scientific analysis is to identify the extent. Items 1 - 19 of 19 A causal-comparative design is a research design that seeks to find relationships between independent and dependent variables after an. some variable on taking one action relative to the effect of taking a different action . . How should we design the causal study to avoid making such strong and.
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Action Research in Education: Guilford, ; Gall, Meredith. Chapter 18, Action Research. Norman Denzin and Yvonna S. SAGE,pp. Writing and Doing Action Research.
Causal research - Wikipedia
Sage, ; Reason, Peter and Hilary Bradbury. Handbook of Action Research: Participative Inquiry and Practice. Case Study Design Definition and Purpose A case study is an in-depth study of a particular research problem rather than a sweeping statistical survey or comprehesive comparative inquiry.
It is often used to narrow down a very broad field of research into one or a few easily researchable examples. The case study research design is also useful for testing whether a specific theory and model actually applies to phenomena in the real world.
It is a useful design when not much is known about an issue or phenomenon. Approach excels at bringing us to an understanding of a complex issue through detailed contextual analysis of a limited number of events or conditions and their relationships. A researcher using a case study design can apply a variety of methodologies and rely on a variety of sources to investigate a research problem. Design can extend experience or add strength to what is already known through previous research.
Social scientists, in particular, make wide use of this research design to examine contemporary real-life situations and provide the basis for the application of concepts and theories and the extension of methodologies.
The design can provide detailed descriptions of specific and rare cases. A single or small number of cases offers little basis for establishing reliability or to generalize the findings to a wider population of people, places, or things.
Intense exposure to the study of a case may bias a researcher's interpretation of the findings. Design does not facilitate assessment of cause and effect relationships. Vital information may be missing, making the case hard to interpret. The case may not be representative or typical of the larger problem being investigated.
If the criteria for selecting a case is because it represents a very unusual or unique phenomenon or problem for study, then your intepretation of the findings can only apply to that particular case. Chapter 4, Flexible Methods: Columbia University Press, ; Gerring, John.
Past, Present and Future Challenges. Encyclopedia of Case Study Research. The Art of Case Study Research. Applied Social Research Methods Series, no. 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.
Statistical Language - Correlation and Causation
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.
Causality research designs assist researchers in understanding why the world works the way it does through the process of proving a causal link between variables and by the process of eliminating other possibilities. There is greater confidence the study has internal validity due to the systematic subject selection and equity of groups being compared. Not all relationships are casual! The possibility always exists that, by sheer coincidence, two unrelated events appear to be related [e.
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. If two variables are correlated, the cause must come before the effect.
Beach, Derek and Rasmus Brun Pedersen. Causal Case Study Methods: Foundations and Guidelines for Comparing, Matching, and Tracing. University of Michigan Press, ; Bachman, Ronet. Chapter 5, Causation and Research Designs. Sage,pp. Chapter 11, Nonexperimental Research: Cohort Design Definition and Purpose 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. 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. 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 upon cohort designs. Cohort analysis is highly flexible and can provide insight into effects over time and related to a variety of different types of changes [e. Either original data or secondary data can be used in this design. In cases where a comparative analysis of two cohorts is made [e.
These factors are known as confounding variables. 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. Due to the lack of randominization in the cohort design, its external validity is lower than that of study designs where the researcher randomly assigns participants. Healy P, Devane D.
Sage, ; Levin, Kate Ann. Evidence-Based Dentistry 7 Himmelfarb Health Sciences Library. Cross-Sectional Design Definition and Purpose Cross-sectional research designs have three distinctive features: The cross-sectional design can only measure differences between or from among a variety of people, subjects, or phenomena rather than a process of change.
As such, researchers using this design can only employ a relatively passive approach to making causal inferences based on findings. Cross-sectional studies provide a clear 'snapshot' of the outcome and the characteristics associated with it, at a specific point in time. Unlike an 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.
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. Groups identified for study are purposely selected based upon existing differences in the sample rather than seeking random sampling.
Cross-section studies are capable of using data from a large number of subjects and, unlike observational studies, is not geographically bound. Can estimate prevalence of an outcome of interest because the sample is usually taken from the whole population.
Because cross-sectional designs generally use survey techniques to gather data, they are relatively inexpensive and take up little time to conduct. Finding people, subjects, or phenomena to study that are very similar except in one specific variable can be difficult. Results are static and time bound and, therefore, give no indication of a sequence of events or reveal historical or temporal contexts.
Studies cannot be utilized to establish cause and effect relationships. This design only provides a snapshot of analysis so there is always the possibility that a study could have differing results if another time-frame had been chosen. There is no follow up to the findings. Descriptive Design Definition and Purpose Descriptive research designs help provide answers to the questions of who, what, when, where, and how associated with a particular research problem; a descriptive study cannot conclusively ascertain answers to why.
Descriptive research is used to obtain information concerning the current status of the phenomena and to describe "what exists" with respect to variables or conditions in a situation. The subject is being observed in a completely natural and unchanged natural environment. True experiments, whilst giving analyzable data, often adversely influence the normal behavior of the subject [a. Descriptive research is often used as a pre-cursor to more quantitative research designs with the general overview giving some valuable pointers as to what variables are worth testing quantitatively.
If the limitations are understood, they can be a useful tool in developing a more focused study. Descriptive studies can yield rich data that lead to important recommendations in practice. Appoach collects a large amount of data for detailed analysis.
The results from a descriptive research cannot be used to discover a definitive answer or to disprove a hypothesis. Because descriptive designs often utilize observational methods [as opposed to quantitative methods], the results cannot be replicated. The descriptive function of research is heavily dependent on instrumentation for measurement and observation.
Chapter 5, Flexible Methods: With this information, an organization can confidently decide whether it is worth the resources to use a variable, like adding better traffic signs, or attempt to eliminate a variable, like road rage.
Implementing Causal Research Effectively 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. Without a complete understanding of your research plan and what you are trying to prove, your findings can become unreliable and have high amounts of researcher bias.
Try using exploratory research or descriptive research as a tool to base your research plan on. The cause and effect relationship will be proved or disproved by the experiment. To make sure your study will have results one way or another, observe what your normal environment is and then crank up the frequency or power of the causal variable. You are clearly identifying which variables are being tested as independent causing effect and which are being tested as dependent being effected.
Because of this, it is essential to identify which will be tested as which prior to the experiment. Usually, the independent variable will be whatever you are adding to the environment.
For example, we hypothesize that increasing colour options for cars will increase sales. In this case, the number of colour options is the independent variable and the level of sales is our dependent variable. Your next step would be to measure the normal rate of sales at the car store, and then add a broader selection of car colours. After collecting the new sales numbers, compare the two data sets and study the effect on sales.
There are no external variables that can also be causing changes in your results. In the laboratory, scientists have the luxury of being able to create a completely neutral environment.
Unfortunately for the rest of us, we have to deal with the environment we are given. So the most important thing to do when creating your research plan is to ensure that your experiment occurs under the most similar possible conditions as when you measured your normal results.
Awesome idea, I know! It would be a bad idea to use your summertime sales as your normal data source and run your experiment in winter. Not only would that be cold for the clown, the weather would have a huge effect on ice cream sales. The goal of causal research is to give proof that a particular relationship exists. From a company standpoint, if you want to verify that a strategy will work or be confident when identifying sources of an issue, causal research is the way to go.
Most franchise chains conduct causal research experiments within their stores. In one case, a large auto-repair shop recently conducted an experiment where select shops enforced a policy that an employee would have a one-on-one with the client while their vehicle is being assessed.
This experiment was implemented because of an online survey that identified a lack of employee-client communication as being a barrier to repeat customers. After identifying two solutions to this issue facilitating discussion and increasing client understandingthe company used this experiment to learn just how effective these solutions would be in increasing customer retention.