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A mixed methods inquiry into the validity of data.

Kristensen E, Nielsen DB, Jensen LN, Vaarst M, Enevoldsen C - Acta Vet. Scand. (2008)

Bottom Line: By integrating quantitative and qualitative research methods in a mixed methods research approach, the researchers will improve their understanding of this potential bias of the observed data and farms, which will enable them to obtain more useful results of quantitative analyses.Studies where associations between specific herd health management routines and disease outcome variables are drawn based purely on quantitative observational studies may benefit greatly by adding a qualitative perspective to the quantitative approach as illustrated and discussed in this article.The combined approach requires, besides skills and interdisciplinary collaboration, also openness, reflection and scepticism from the involved scientists, but the benefits may be extended to various contexts both in advisory service and science.

View Article: PubMed Central - HTML - PubMed

Affiliation: StrateKo Aps, Gartnervaenget 2, DK-8680 Ry, Denmark. erling.kristensen@tdcadsl.dk

ABSTRACT

Background: Research in herd health management solely using a quantitative approach may present major challenges to the interpretation of the results, because the humans involved may have responded to their observations based on previous experiences and own beliefs. This challenge can be met through increased awareness and dialogue between researchers and farmers or other stakeholders about the background for data collection related to management and changes in management. By integrating quantitative and qualitative research methods in a mixed methods research approach, the researchers will improve their understanding of this potential bias of the observed data and farms, which will enable them to obtain more useful results of quantitative analyses.

Case description: An example is used to illustrate the potentials of combining quantitative and qualitative approaches to herd health related data analyses. The example is based on two studies on bovine metritis. The first study was a quantitative observational study of risk factors for metritis in Danish dairy cows based on data from the Danish Cattle Database. The other study was a semi-structured interview study involving 20 practicing veterinarians with the aim to gain insight into veterinarians' decision making when collecting and processing data related to metritis.

Discussion and evaluation: The relations between risk factors and metritis in the first project supported the findings in several other quantitative observational studies; however, the herd incidence risk was highly skewed. There may be simple practical reasons for this, e.g. underreporting and differences in the veterinarians' decision making. Additionally, the interviews in the second project identified several problems with correctness and validity of data regarding the occurrence of metritis because of differences regarding case definitions and thresholds for treatments between veterinarians.

Conclusion: Studies where associations between specific herd health management routines and disease outcome variables are drawn based purely on quantitative observational studies may benefit greatly by adding a qualitative perspective to the quantitative approach as illustrated and discussed in this article. The combined approach requires, besides skills and interdisciplinary collaboration, also openness, reflection and scepticism from the involved scientists, but the benefits may be extended to various contexts both in advisory service and science.

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Conceptual model of the iterative process of induction and deduction in Herd Health Management. Modified from [19] with inspiration from [2,31].
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Figure 1: Conceptual model of the iterative process of induction and deduction in Herd Health Management. Modified from [19] with inspiration from [2,31].

Mentions: Mixed Methods Research (MMR) is defined as an intellectual and practical synthesis based on the combination of qualitative and quantitative research methodologies and results [23]. It recognizes the importance of both quantitative and qualitative research methods but also offers a powerful third mixed research methodology that potentially will provide the most informative, complete, balanced, and useful research results. MMR aims at linking theory and practice [19,24] as illustrated in Figure 1. We believe that an appropriate and well-reflected integration of different scientific methods may contribute significantly to the understanding of any data potentially influenced by human action. In the following, it is suggested that scientists with a need to understand a certain field of human action and the consequences and background of these actions can reach far by implementing different methods in their research, and we point to three different methodologies [10,25]: a) supplementary validation; b) triangulation and c) knowledge generation.


A mixed methods inquiry into the validity of data.

Kristensen E, Nielsen DB, Jensen LN, Vaarst M, Enevoldsen C - Acta Vet. Scand. (2008)

Conceptual model of the iterative process of induction and deduction in Herd Health Management. Modified from [19] with inspiration from [2,31].
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2500011&req=5

Figure 1: Conceptual model of the iterative process of induction and deduction in Herd Health Management. Modified from [19] with inspiration from [2,31].
Mentions: Mixed Methods Research (MMR) is defined as an intellectual and practical synthesis based on the combination of qualitative and quantitative research methodologies and results [23]. It recognizes the importance of both quantitative and qualitative research methods but also offers a powerful third mixed research methodology that potentially will provide the most informative, complete, balanced, and useful research results. MMR aims at linking theory and practice [19,24] as illustrated in Figure 1. We believe that an appropriate and well-reflected integration of different scientific methods may contribute significantly to the understanding of any data potentially influenced by human action. In the following, it is suggested that scientists with a need to understand a certain field of human action and the consequences and background of these actions can reach far by implementing different methods in their research, and we point to three different methodologies [10,25]: a) supplementary validation; b) triangulation and c) knowledge generation.

Bottom Line: By integrating quantitative and qualitative research methods in a mixed methods research approach, the researchers will improve their understanding of this potential bias of the observed data and farms, which will enable them to obtain more useful results of quantitative analyses.Studies where associations between specific herd health management routines and disease outcome variables are drawn based purely on quantitative observational studies may benefit greatly by adding a qualitative perspective to the quantitative approach as illustrated and discussed in this article.The combined approach requires, besides skills and interdisciplinary collaboration, also openness, reflection and scepticism from the involved scientists, but the benefits may be extended to various contexts both in advisory service and science.

View Article: PubMed Central - HTML - PubMed

Affiliation: StrateKo Aps, Gartnervaenget 2, DK-8680 Ry, Denmark. erling.kristensen@tdcadsl.dk

ABSTRACT

Background: Research in herd health management solely using a quantitative approach may present major challenges to the interpretation of the results, because the humans involved may have responded to their observations based on previous experiences and own beliefs. This challenge can be met through increased awareness and dialogue between researchers and farmers or other stakeholders about the background for data collection related to management and changes in management. By integrating quantitative and qualitative research methods in a mixed methods research approach, the researchers will improve their understanding of this potential bias of the observed data and farms, which will enable them to obtain more useful results of quantitative analyses.

Case description: An example is used to illustrate the potentials of combining quantitative and qualitative approaches to herd health related data analyses. The example is based on two studies on bovine metritis. The first study was a quantitative observational study of risk factors for metritis in Danish dairy cows based on data from the Danish Cattle Database. The other study was a semi-structured interview study involving 20 practicing veterinarians with the aim to gain insight into veterinarians' decision making when collecting and processing data related to metritis.

Discussion and evaluation: The relations between risk factors and metritis in the first project supported the findings in several other quantitative observational studies; however, the herd incidence risk was highly skewed. There may be simple practical reasons for this, e.g. underreporting and differences in the veterinarians' decision making. Additionally, the interviews in the second project identified several problems with correctness and validity of data regarding the occurrence of metritis because of differences regarding case definitions and thresholds for treatments between veterinarians.

Conclusion: Studies where associations between specific herd health management routines and disease outcome variables are drawn based purely on quantitative observational studies may benefit greatly by adding a qualitative perspective to the quantitative approach as illustrated and discussed in this article. The combined approach requires, besides skills and interdisciplinary collaboration, also openness, reflection and scepticism from the involved scientists, but the benefits may be extended to various contexts both in advisory service and science.

Show MeSH
Related in: MedlinePlus