Article Volumes and Market Characteristics †Free Samples to Students

Question: Discuss about the Article Volumes and Market Characteristics. Answer: Introduction: Sample size plays a vital role in the determination of the features of a population. A magnitude of proportion of the population is referred to as sample size. The decision of coming up with the exact sample size is essential in the collection of accurate information, this is according to (Khberger et al, 2014). As a result therefore, for the collected data to maintain its reliability, the accuracy of the sample size has to be considered. By applying the rightful formula for the determination of the sample size i.e. From the population of 69,000 Belgian bank workers, the size of the sample from this population by applying the stated formula was supposed to be 383 at the 95% confidence level with a marginal error pf 0.05. The preferred percentage pick in the calculation of the sample size was 50%. This percentage was chosen because it is conservative when it comes to calculation of a vast sample size. By taking all the factors into account i.e. percentage pick, marginal error and confidence level, out of the population of 69,000 bankers, 383 is the recommended sample size that ought to have been used by the two research institutions. Bearing this in mind therefore, we can conclude that choosing to work with the sample size of 15,000 bank workers being that it is beyond the recommended size, it is a large sample size. Working with large sample size normally has some advantages and disadvantages as will be discussed. Large sample size is importance in minimizing the Margie of error which further helps in boosting the accuracy of the obtained results from the sample, this is one among other advantages of large sample size. This increases the precision by which the population parameter fall within the range of the calculated point estimator (Clearly et al, 2014). Wide coverage by the large sample size (15,000 participants) in a population helps in acquiring more accurate information from the participants concerning the subject under study (i.e. stress in this case) as compared to when the smaller sample size would have been used. Furthermore, big and increased sample size raises the representativeness of the individuals in the population which further takes care and caters for the outliers that would be present in the population, this is according to (Belli et al, 2014). Disadvantages of sample size Collecting and covering a larger fraction of the population will require high expenses to be involved and incurred hence large samples are costly, this is according to (Goodman et al, 2013). In this case, reaching the targeted number of bank workers of 15,000 by the research institutions will make the Union of Belgian Banks to spend much in order for target to be achieved. On the same, being that the fraction is relatively large and the participants to participate in the data collection process are not in same geographical location since the banks are spread across the country, achieving the targeted sample size will be time consuming. Prior information obtained about the topic under study will be important in determining the sample size being that prior point estimates such as means and variances act as the reference to deal with variation could arise in the groups (Button et al, 2013). Cost is another factor that can be used to determine the size of the sample to be used in a survey. Depending on the risk value involved in the values that need to be collected, if the risk needs to be high, then small sample size can be used and if the risk involved is to be low, then that calls for large sample size. Sampling method is the process by which group characteristics are obtained from the population under study. Stratified sampling method was used by the research institutions in the selection of 15,000 bank workers who were to participate in the process. According to (Ye et al, 2013), stratified sampling method reduces sampling errors when used. The population is first divided into small groups called strata that are distributed to ensure that every element of a population is represented thereafter have characteristics from each stratum selected by simple random sampling in order to reduce selection bias. This method ensures that the population is highly represented in the sample. One of the problems with this method is the difficulty to identify means to be applied in subdividing the population into subgroups, this makes this method unpopular and rarely used by the researchers. Furthermore, a lot of time is involved in the determination of strata which will later require the selection of the sample from the available strata by simple random sampling method (Acharya et al, 2013). In our case, the research institutions were first to find the banks within the country, categorize the workers according their bank institutions which will form strata where workers will now be selected from to form a sample. Simple random sampling is involved in the selection of individuals from the banks to provide for equal chances to be in the sample that represent the population. I hereby recommend for the increased number of strata in order to improve the effectiveness of stratified sampling method that will increase the representativeness of the population as the marginal error is minimized. Research Design There are advantages and disadvantages associated with different research design used. Cross-sectional design is described as the tool researchers use to acquire definite point time information from gathered data. According to (Shen and Bjrk, 2015), incorporation of cross-sectional study in the cross-sectional design is important in determining the value of assumptions used in the study. The time involved in cross-sectional design is less compared to other research designs due to specific point time information from already existing information. This also helps in easy identification of the information of interest when cross-sectional design is used. Expense involved when using cross-sectional design in the identification of point time information is less hence making it inexpensive. In order to obtain a pattern displayed by variables in a certain period of time, longitudinal design is used. Lack of reliability by cross-sectional design to predict the relationship between findings is as a result of having no tie element being that it is only capable of measuring point time information. This becomes one of the major disadvantages of cross-sectional design. Having consistent results in the extended period of time that could be less important or less serious and exist for a relatively lengthy time. Longitudinal research design on the other hand covers a long period of time and as a result can be more expensive. Being that longitudinal research design can come up with a pattern for over a period of time, it consumes a lot of time. Furthermore, the longitudinal research design cannot be used or rather becomes less efficient where less outcomes are expected (Shen and Bjrk, 2015). Questionnaires were used by the research institutions to collect data from the bank workers. The questionnaires were structured with questions that required the respondents to provide their responses on the spaces provided. The use of this procedure in collecting data has some of the problems as discussed below; Having so many open ended questions in a questionnaire will result to some negative impact at the time of data analysis. It becomes so difficult to analyze such kind of data from such questions since people have different opinions, they will tend to provide their varied opinions in response to the open ended questions making them difficult to analyze since too much data will be available more than they can be handled and analyzed being that they cannot be coded. To combat this problem, when constructing the questionnaire, questions are supposed to be closed ended which will provide multiple choices that can be coded thus making the analysis easy since it is simpler to work with coded data. Dishonesty from the side of the respondents is another major problem associated with using questionnaires to collect data. In some of the cases, respondents can decided to conceal the truth when they are participating in the process (Chernik et al, 2011). Such problems are always rampant when the participants feel that the public can get to know their identities. As a result of this therefore, reliability and accuracy of the data collected from the process is tampered with which will further affect the results of the research. Researchers in this case are supposed to exterminate this menace by assuring the participants who participate in the process that the researcher value and give their identities priority that they will be kept secret and private from the reach of unauthorized people. Giving such assurance to the respondents will reduce the level of dishonesty. One of the effective means of communication is face to face communication where emotions of the people involved in the communication can be read through their facial expressions. Using questionnaires make it difficult to capture emotional responses that would develop in the process of responding to the questions especially when the questionnaires are not administered by the researcher. Such problems can be eradicated and taken care of by constructing a Likert scale questions that would be applied to gauge the feelings, attitude and emotions of the participants. Questionnaires also face serious problems when some of the questions remain unanswered. A number of the respondents normally tend not to respond to some of the questions provided in the questionnaire due to their own reasons. Some may fail to respond to some of the questions because the questions are not clear or understandable and tend to skip them for a while to finish up with other questions and end up collecting the questionnaires with the questions not answered. This is a problems that needs to be dealt with to reduce its effects in the future research. For instance, online surveys do tend to solve this problem by making all the questions required such that they all need to be answered without which there is no proceeding to the next step of the survey. With the questionnaires therefore, this problem can be put to the past by constructing questions that are uncomplicated that are easy to understand and also making the survey as short as possible. Questionnaires are supposed to be made accessible to all the people irrespective of whether you are disabled or not. This has been one of the major problems questionnaires have since people of different forms of physical disability are not taken care of for example people with impairment or visual problems who cannot be able to read what is on the questionnaires. This problem can be eliminated by using questionnaires that are having their accessibility options built in. Secondary Data These are data that are obtained from the database archives from the previously conducted research. All the data to be retrieved from databases for a certain study must be relevant to the subject of study. According to (Piwowar and Vision, 2013), competency and accuracy of data in regard to the subject of study are one of the first things to be considered when checking for the representativeness of the sample using secondary data. Secondary data helps in saving time by providing a clear picture of what the researcher expects. Unlike primary data, secondary data are easier and cheaper to obtain since they are only retrieved from the databases where they are stored (Irwin, 2013). The government funded projects do involve collection of large data samples which in most cases will cover a wider subset of a population. The researcher is supposed to do anything possible in order to understand the stored data before they are put to use in checking for representativeness. Probabilistic sampli ng design is to be applied in case of large sample size where stratified sampling methods are appropriate because of its wider coverage of the population. 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