Understanding Business Research Terms and Concepts
Quantitative data collection instruments and random sampling methods produces research finding that are simple in terms of providing a summary, do comparison and provide for generalization. This kind of research method focuses on testing the hypothesis extracted from theory and estimates the size of the issue of interest. The researcher undertakes to collect data from the objects of study that possesses a characteristic that can be generalized to represent the entire populace (James and Watson, 2007).
Data collection instruments
Instruments that are used to collect data include experiment; a controlled study that aims at collecting data and understanding cause-effect relationships and controls how subjects are allotted to group and the dealing that each receive. For example, in clinical field this can be the clinical trials that seek to collect data from different groups of patients, especially in ascertaining the effect and behavior of a patient after administration of a drug. Situations when to use experiment research instrument is when the researcher is trying to learn some new phenomenon and when he/she want to provide an explanation as to why the phenomenon is actually occurring or happening.
Observation schedules and recording actions; as experiments they attempt to comprehend the cause-effect relationships though the researcher cannot control how the subjects receive treatment. What is observed would be noted down for example counting the number of people waiting to see a doctor in a healthcare facility. Situation when to use observation include when the researcher wants to identify relationships among the subjects, when there need to establish the interrelation between the subjects of study and when the researcher wants to be part of the subjects under study.
It is asserted that they have closed-ended queries to provoke answers (James and Watson, 2007). Sample survey would aim to study and get data from a section of the populace and then use it to generalize and approximate the entire populace physiognomies. Situation when to use administered survey includes when the researcher wants to collect a large magnitude of data and also when there is need to provide a broad perspective of the research study (Kevin, 2010).
As described qualitative research method would collect data from a section of the populace and then use to generalize for the entire population. To collect data from subjects sampling must be done as it provides guidance on how to go about data collection.
Simple random sampling
It selects respondents in unplanned manner, that is, each affiliate of the populace possesses an equivalent opportunity. It has several strengths in that it aims to reduce researcher bias in the selection of subjects for study in the sample thereby providing a sample that is fully representative of the population to study. The sample would in a position to represent the behaviors and attributes of the general populace. The weakness is that it is time consuming in case of reaching for new respondents since to avoid bias the proportion sample must be part and parcel of the research.
It arrives at the systematic group through calculating the anticipated selection segment. It uses a sampling frame to achieve this and it is very simple as it adds a degree of process to the random selection of target. The strengths include being very simple in usage, it is also economical and has the capability of checking for bias in the continuous selection of respondents. Just like simple random sampling, it is affected by several weaknesses with the possibility of losing fundamental information from the general populace, in cases where the population is small, it may be practically impossible to select the required sample size and it may not be appropriate for intermittent data.
It splits the populace into assemblies then a random sample of the assemblies is picked. Strengths include being cheap as the researcher can allocate the limited resources to a few randomly selected clusters instead of an entire population and it is also makes it easy to select subjects since they are more accessible. Major weakness is that with this technique a sample that is least representative of the general population can be used for the study; there is a tendency by the researcher to use an overrepresented or underrepresented cluster sample, which can tilt the results of the study.
Descriptive statistical approach/method
It provides for analysis of data by helping in describing, showing and summarizing data in a meaningful way to show patterns that are likely to emerge from the data. However, it does not allow the making of conclusions beyond the data analyzed regarding any hypotheses that might have been made (Kevin, 2010). This method is uniquely important as it enables presentation of data in a more meaningful way to allow easy interpretation. For example, a class that has 50 students may be interested in knowing the overall performance and marks distribution; this method would be the best in such a case.
The strength of this method is that it allows the use of specific forms of data collection like case studies, observation, and survey which offers advantages in understanding complicated data. In addition, it offers a unique means of collecting data that provide insightful life experiences in data collections. Confidentiality is a major weakness when using this method since at times the subjects may not be truthful in their responses, they may even refuse to provide responses that they look too personal.
Inferential statistical approach/method
It tries to attain conclusion that go beyond the available data. For example, inferential method would provide judgment that there is probability that whatever is observed in a group may have happened by chance on that particular study (Ballinger, 2008). Therefore, this method allows making conclusions from data to even more general condition unlike descriptive that only describes conclusions on the data.
The strength of this approach is that the researcher does not need data on the entire population to make inferences so long as the samples are accurate he/she can make an educated guess to reach the conclusions. The weakness that exists in this method is that the researcher is providing data about a population that actually has not been measured and so raising questions on the correctness of the statistics (Ballinger, 2008). In addition, the researcher must make an educated guess based on a theory that might also affect the validity of the statistics.
To reach to a valid conclusion about various situations both descriptive and inferential approaches can be used together to provide a full analysis of the data set and provide accurate conclusions. They can be used effectively in a small population to get accurate parameters then approximate those parameters to a much larger population.
Descriptive statistical method is appropriate in the cases where is need to conduct research, analyze and present epidemiological data. For example, the evidence of presence of diseases to human beings or animals can effectively be achieved using this method, which makes this method appropriate to the social sciences discipline and field.
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