What are the strengths and weaknesses of triangulation?
Strengths and weaknesses of triangulation
| Main strengths | Main weaknesses |
|---|---|
| Potential to produce rich and productive data | Can produce large volumes of data |
| Facilitates a process of validation of results | Can result in superficial treatment of some data |
| Increases completeness in a study | Potential for discrepant findings |
What are strengths of triangulation?
Theoretical triangulation The primary strength of theoretical triangulation is its ability to look deeper and more broadly at findings. Specifically, using only one theory, perspective or hypothesis can decrease the number of al- ternative explanations for a situation or phenomenon.
What is the principal disadvantage of triangulated research?
The drawbacks of triangulation include the lack of a uniform methodology. Those who use triangulation often fail to explain their techniques adequately and use varying methods for combining results.
Why is triangulation important in qualitative research?
Triangulation facilitates validation of data through cross verification from more than two sources. It tests the consistency of findings obtained through different instruments and increases the chance to control, or at least assess, some of the threats or multiple causes influencing our results.
What is triangulation design in research?
Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.
How is triangulation used in research?
Triangulation is a technique to analyze results of the same study using different methods of data collection. It is used for three main purposes: to enhance validity, to create a more in-depth picture of a research problem, and to interrogate different ways of understanding a research problem.
How does triangulation improve validity?
Data triangulation is the use of a variety of data sources, including time, space and persons, in a study. Findings can be corroborated and any weaknesses in the data can be compensated for by the strengths of other data, thereby increasing the validity and reliability of the results.
Why triangulation method is important?
Abstract. Triangulation is a technique to analyze results of the same study using different methods of data collection. It is used for three main purposes: to enhance validity, to create a more in-depth picture of a research problem, and to interrogate different ways of understanding a research problem.
What is triangulation and why is it used in research?
Abstract. Triangulation refers to the use of multiple methods or data sources in qualitative research to develop a comprehensive understanding of phenomena (Patton, 1999). Triangulation also has been viewed as a qualitative research strategy to test validity through the convergence of information from different sources …
What are the advantages of triangulation designs?
The primary advantage of triangulation designs is the ability to find agreement and validation of results through various research methods. If different research methods come to the same conclusion, the researcher can be more confident in the results.
What is triangulation in sociology?
The Advantages and Disadvantages of Triangulation in Sociological Research Social Science Triangulation in sociological research is the use of 3 or more contrasting methods in a study to produce 3 different sets or even types of data. Its purpose is to reduce the weight given to any individual set of results.
What are the different types of triangulation methods?
There are various triangulation methods based on the techniques and people used. Data Triangulation requires use of space, time and people. Investigator triangulation requires multiple researchers in the same study. Theory triangulation requires use of more than one theoretical scheme to interpret the phenomenon.
What are the advantages of stratified sampling?
Using stratified sampling will allow you to obtain more precise (with lower variance) statistical estimates of whatever you are trying to measure. For example, say you want to investigate how income differs based on educational attainment, but you know that this relationship can vary based on race.