The use of the word "triangulation" in research is broad. Generally, it means that a decision is based upon three different sources of data. However, different triangulation techniques may be used depending on the types and sources of the data. The term is most commonly used in qualitative research. For example, if we wanted to determine the most popular painting being displayed at a gallery, we could survey the guests, use electronic means to measure the time spent looking at each painting, and even measure the wear (or scuffing) of the floor in front of each painting. If the same painting was identified as the most popular by all three techniques, we could say the results were varified by triangulating the data.

Within mixed methods research, what you describe is known as a sequential exploratory design (qual --> QUAN).

Also, the editors of the Journal of Mixed Methods Research have issued a statement that they will no longer accept articles that say they are based on triangulation, given the overly broad meanings that have been attached to that term.

Thanks alot Mr James E. McLean and Mr. David L Morgan for this explanation.

The term "triangulation" is popular in the qualitative case study method, wherein researchers gather multiple perspectives expressed in themes based on the case study questions. However, we need to be careful in defining "multiple sources" of information to ensure they are acceptable. I usually advise my students to gather information from at least three sources: interviews, document analysis, and observation. Of course, other sources of information—such as survey questionnaires, systematic reviews of similar phenomena, and secondary data—are also acceptable.

I think the approach you mentioned here pertains to the sequential exploratory mixed methods design, which starts with qualitative data that is then transformed into measurable questions. This process helps capture themes and develop generalizations based on the generated quantitative data.

Irving Larot Rio

yes, you are right. It is a sequential exploratory mixed methods design, which started with qualitative data that was transformed into a questionnaire to measure several questions. This process helps capture themes and develop generalizations based on the generated quantitative data. Then, I used factor analysis to reduce the number of items and to discover any latent variables to produce a new scale (5 to 7 factors). My questions are: Can I carry my analysis by using correlation and regression without assigning any hypotheses as it is an exploratory study, or what do you think is the best way to complete the research to find if number of factors affect the manager performance.

Regression requires a separation between independent and dependent variables, so that almost automatically implies a hypothesis. Of course, that hypothesis might be associated with assessing validity, such as convergent validity.