Scholarly Article Results For this Discussion Board, you will examine the results section of a scholarly article. Use the same article from Unit 7 and answer the following questions: How do the results relate to the predictions/hypotheses set out in the introduction (i.e., are they supported or not)? What differences did the authors find that support or refute the project hypotheses?Are the results reported and analyzed in an unbiased manner? The manner in which data are reported and analyzed could dramatically affect interpretation. For example, different types or formats of graphs can emphasize or de-emphasize the sizes of effects, and different inferential tests can yield different results.How does the methodology connect with the results section? For example, were the datasets of sufficient size for the statistical analysis? Although you have not had an inferential statistics course, you can still evaluate this section. Does it look like the researcher is trying too hypothes

Most people boast of having generated quality data without really understanding the meaning of quality data. Good data means quality data. There are several characteristics of data that make it qualify as quality or good data. First, good data is reliable; this means that the data collected is not similar and does not contradict (Blasius and Thiessen 99). For instance, when two sources of data contradict, both sources cannot be trusted. Secondly, good data relevant; therefore, it should make sense. The third characteristic of data is accuracy, which is the most important aspect of any data. Data must be correct as inaccuracy in data can result in severe consequences. Fourthly, good data is complete, providing comprehensive information. Incomplete data is not helpful. Lastly, good data is timely, meaning that it contains up to date information (Blasius and Thiessen 99). This allows one to make decisions based on the current information. The following is an analysis of an article’s data collection processes. Study Population and Sample The study population was Varanasi, a city in India with 90 wards. The study population comprised of people aged 25 to 64 years (Rajat and Bhide). The study sample used by the researchers was 640 after a non-response rate of 10% as considered. To generate these samples, multistage sampling was used. A multistage sampling involves taking smaller samples at different stages. To achieve this type of sampling, random sampling was first used to select five wards within which the research would be carried out. At each ward, systematic sampling was used to determine the households. From the selected households, one member within the targeted age group was selected. Since most households had more than one person, a lottery method was used to randomly select one individual. In case the selected houses had no eligible person, the adjacent household was selected. The sample size selected was sufficient because the researchers put several factors into consideration before concluding the sample size to use for their research (Rajat and Bhide). First, they considered the permissible error as 50%. Secondly, a non-response rate of 10% was used to ensure that the correct data was used. Instrumentation Data were collected through interview schedules to collect sociodemographic data. Other parameters included blood pressure and anthropometric assessment, such as measurement of weight using the Libra weighing machine, height using the steel anthropometry rod. To achieve the BMI, weight (kg) was divided by height in (M2). A measuring tape was used to measure the waist circumference and hip circumference (Rajat and Bhide). Dividing the waist circumference by hip circumference would result to a waist to hip ratio. The research project was quantitative research as the researchers tried to generate numerical data. The questions asked matched with the research question and hypothesis.