Introduction
All types of businesses undertake extensive research to improve and grow. The success of a start-up, medium sized business or even an established business depends on cost-effective and efficient research undertaken. The research methods undertaken by organizations enable them to conduct an in-depth study regarding several internal and external factors that influence the market share as well as profitability. Organizations can make appropriate decisions based on the information obtained from research. For instance, research is crucial for managerial decision making. Most strategic business areas are always analyzed and evaluated after which techniques for more efficient procedures are created . Usually, businesses have many ways of doing an activity. Proper research facilitates identification and implementation of the most effective, profitable and productive approach. Research can be applied to human resources, finance or even production. Therefore, research acts as a guiding light for organizations.
Descriptive statistics, inferential statistics and trend analysis can help in summarizing and analyzing research data to obtain meaningful information. The various types of descriptive statistics include measures of frequency, measures of central tendency, measures of dispersion and measures of position. The various types of inferential statistics include t-tests, analysis of variance and regression. The identified business problem is employee turnover while the identified variable is leader-follower relationships. Research can help to establish the influence of leader-follower relationships on employee turnover at Amazon.
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Descriptive Statistics
Descriptive statistics help in describing the basic features of data in a particular study. As such, descriptive statistics provide simple summaries regarding the sample as well as the measures. Descriptive statistics include measures of frequency, measures of central tendency, measures of dispersion and measures of position. Such measures help the researchers to establish patterns from the data (Cavana et al., 2001). However, descriptive statistics do not allow researchers to make conclusions beyond the data analyzed or even reach conclusions regarding some of the hypotheses that might have been made. Therefore, descriptive statistics are simply a way of describing data. Descriptive statistics assist in presenting the data in a more meaningful way.
Measures of frequency can be important in summarizing the data collected from the employee's sample at Amazon. The measures of frequency include count, percentages, and frequency. The measures show how often something occurs. Therefore, they can be used to measure the number or percentage of employees who leave the organization per year. Frequency can be used to present the ages of employees in the organization. Additionally, the frequency can also be used to show the years the employees have worked in the organization. Measures of frequency can also help to show how often a response is given by employees of the organization. Percentages and frequency are some of the measures that will help in summarizing the data obtain from the study sample in the organization. This is because the percentages will help in grouping employees in terms of their demographics and responses. Frequencies will also help in summarizing the characteristics of employees in terms of age and years worked.
Measures of central tendency include mean, median and mode. The measures of central tendency help in locating the distribution of data by various points. Therefore, they can be used to show an average or the most commonly indicated response in some sets of data. Mean can be helpful in determining the mean age and the number of years worked by employees. Mode can also be used to determine the most occurring age or length of service of the Amazon employees.
Measures of dispersion include range variance and standard deviation. Measures of dispersion include variance and standard deviation. They complement the measures of central tendency by attempting to describe the spread of the data values in a data set (Thiem, 2014). The most common measures of dispersion include the range and the standard deviation. The range represents the crudest way of estimating the spread of data values. However, the range is an adequate estimate for a data set of no more than five values. In Amazon’s case, the range will not be applicable because the sample consists of more than five values. Therefore, the standard deviation can be used to estimate the distribution of data. Additionally, the standard deviation is less susceptible to outliers. It will help to estimate the spread of employees’ age and length of service. They help in determining whether the data is spread out that it can affect the mean (Burg & Romme, 2014). This will help in establishing the impact of positive leader-follower relationship on employee turnover rate.
Inferential Statistics
Inferential statistics help in reaching conclusions that extend beyond the immediate data alone. The inferential statistics attempt to infer from the sample data what the population thinks. Researchers use inferential statistics to make judgments of the probability that an observed difference between groups is a dependable one or one that might have occurred by chance in a particular study (Burg & Romme, 2014). Thus, inferential statistics are used to make inferences from the data to more general conditions. Some of the major inferential statistics include t-tests, analysis of variance and regression analysis. T-Tests can be used to compare means in the sample of employees selected at Amazon. For instance, one sample t-test can be used in this case to compare the identified to some known population. To determine whether employees who experience positive leader-follower relationship stay for long in the organization, the mean length of service in years is compared to the mean length of service in years for all the employees.
The analysis of variance refers to a statistical test that is also used to compare means. Unlike t-test analysis of variance can be used to compare more than two means. Because the research may involve comparison of more than two means, analysis of variance can be suitable as an inferential statistic in the study.
Regression analysis refers to a statistical procedure that allows a researcher to make a prediction about a particular outcome variable based on some knowledge of a predictor. To create a regression model, the researcher will need to collect data on both variables just the same it is done when conducting a correlation. Thereafter, the researcher determines the contribution of the predictor variable to the outcome variable. For instance, if the researcher wants to predict whether an employee will stay at Amazon based on whether they experience positive leader-follower relationship or not. Therefore, employees who are at risk of leaving the organization can be identified.
Role of Trend Analysis
Trend analysis refers to the process of comparing business data over time to identify any consistent trends. This can enable an organization to devise a strategy to respond to such trends in line with the business goals. Trend analysis helps executives to understand how the business has performed and predict where the current business operations and practices will take the organization (Burg & Romme, 2014). If trend analysis is conducted well, it will provide important ideas about how things might be changed to move the organization in the right direction. If the organization establishes that lack of positive leader-follower relationships contributes to high rate of employee turnover, it may take the necessary steps to improve leader-follower relationships in the organization.
Conclusion
In conclusion, it is clear that research plays a significant role in the process of business decision making. Research undertaken by organizations enables them to conduct an in-depth study regarding several internal and external factors that influence the market share as well as profitability. Organizations can make appropriate decisions based on the information obtained from research. Most strategic business areas are always analyzed and evaluated after which techniques for more efficient procedures are created. Descriptive and inferential statistics facilitate effective data analysis. There are used by organizations to obtain meaningful information from collected data. Additionally, trend analysis enables organizations to devise strategies to respond to established trends in line with the business goals.
References
Burg, E., & Romme, A. G. L. (2014). Creating the future together: Toward a framework for research synthesis in entrepreneurship. Entrepreneurship Theory and Practice , 38 (2), 369-397.
Cavana, R. Y., Delahaye, B. L., & Sekaran, U. (2001). Applied business research: Qualitative and quantitative methods . John Wiley & Sons Australia.
Thiem, A. (2014). Membership function sensitivity of descriptive statistics in fuzzy-set relations. International Journal of Social Research Methodology , 17 (6), 625-642.
