Cotton Farming in Pakistan

 Reliability and Validity of Data

In quantitative research, the use of validity and reliability has been widespread and is being reviewed in the paradigm of qualitative research. Since reliability and validity are founded on a positivist viewpoint, therefore, naturalistic usage should be redefined. Just as reliability and validity as in quantitative research are employed, the triangulation used for the evaluation of reliability and validity may provide springboards for the examination of what these two words imply in the qualitative research paradigm. Consequently, if they are important research terms, especially qualitative concepts, reliability, validity and triangulation must be reformulated such that the many different methods of establishing truth are reflected.

Reliability Test

A reliability test is required to verify that the measuring equipment is consistent, whether the findings stay consistent when the measurement is repeated. The statement items in the questionnaire are not trustworthy, they are not consistent for measured outcomes. Cronbach Alpha is used for the reliability test extensively utilised in research. In the reliability test, all items are valid. Therefore the total score is not included for invalid items evaluated and not included. Each variable additionally conducted reliability testing. Reference for Cotton farming affecting factors testing using Iqbal, Ping, Abid, Kazmi, and Rizwan (2016), as follows:

A.    Cronbach's Alpha may be approved under the condition of alpha coefficient of Cronbach <0.6> (construct reliable).

Validity criteria

The convergent validity of an instrument is the degree to which the values of this instrument are comparable to those of another instrument that must measure the same concept [17,18]. A moderate to a high correlation between the two instruments should thus be anticipated. The convergent validity criteria were deemed acceptable in this research at 0.50 or above or a sub-scale correlation range at a minimum of 0.50.

The discriminating validity is defined as the degree to which the variable score of cotton farming varies from that of an interconnected, but distinct concept variable. The study investigated three different characteristics of discriminating validity. First of all, the capacity to differentiate between a similar instrument and a distinct idea. A correlation of 0.50 or less was identified as criterion for an acceptable degree of discrimination. If there was no information on this connection, the mutual distinction of the sub-scales was investigated (correlation coefficient of, at most, 50). When tools meet the requirements of reliability and validity, their responsiveness has been examined. This was because the instrument first has to be valid throughout the time before its reaction can be validly evaluated. Studies were evaluated to show that the tools could quantify changes following intervention in cotton farms. If following an intervention changes in variables influencing the cotton cultivation were demonstrated, the instrument used was deemed responsive.

There are several techniques used to remove the biases of the data. To verify our results from other sources is also the technique to remove the biases of the data the same technique has been used by this research. To recheck the outcomes of my research, I examine the results of other authors and their researches and found the appropriate links with the outcomes of my findings. Such technique has also been used by many authors (Abid, Schilling, Scheffran, & Zulfiqar, 2016; Cheema et al., 2016; Memon et al., 2019)

To conduct this research work I gathered the data from 28 respondents. With the help of thematic analysis, I analyse the factors affecting cotton farming in the Multan region. But there is the major issue in these outcomes is the validity and reliability of the data. However to minimise these issues I used authentic sources to avoid biases. The same type of techniques has also been used in many studies such as Abid et al. (2016) researched to examine the Climate change vulnerability, adaptation and perceptions of risk at the farm level in Punjab. After removing the biasness from the data authors get the appropriate outcomes from the research work. Another study has been conducted by Memon et al. (2019) where after imposing validity and reliability of the data and removing all biases authors examined the health issued face by farmers in cotton fields because of pesticides.

References

Abid, M., Schilling, J., Scheffran, J., & Zulfiqar, F. (2016). Climate change vulnerability, adaptation and risk perceptions at farm level in Punjab, Pakistan. Science of the Total Environment, 547, 447-460.

Cheema, H., Khan, A., Khan, M., Aslam, U., Rana, I., & Khan, I. (2016). Assessment of Bt cotton genotypes for the Cry1Ac transgene and its expression. The Journal of Agricultural Science, 154(1), 109-117.

Iqbal, M. A., Ping, Q., Abid, M., Kazmi, S. M. M., & Rizwan, M. (2016). Assessing risk perceptions and attitude among cotton farmers: A case of Punjab province, Pakistan. International Journal of Disaster Risk Reduction, 16, 68-74.

Memon, Q. U. A., Wagan, S. A., Chunyu, D., Shuangxi, X., Jingdong, L., & Damalas, C. A. (2019). Health problems from pesticide exposure and personal protective measures among women cotton workers in southern Pakistan. Science of the Total Environment, 685, 659-666.