Brief:
- Qualitative data analysis services offer different analytic techniques that help you to know what happened in the past.
- Data analysis plan for qualitative research helpsto predict what is most likely to happen in the future to solve the various business-related problem.
- Data collection is including information from a different source. Data collection technique include an interview, observation, questionaries, and various relevant information. The statistical analysis is the process defined transforming, modelling data to discover information to make a business decision.
Types of data analysis:
There are different types of data analysis techniques. The major types are:
Text Analysis
Text analysis referred to as data mining. This method discusses a pattern in large data sets using data mining tools. It generally used to transform raw data into business information that helps in the growth of the business.Textual data analysis services also have the availability of business intelligence tool in the market that used to make decisions on the strategic business.Sample qualitative data analysis plan offers to examine data deriving patterns and interpretation of the data.
Statistical Analysis:
Statistical analysis is the discipline that concerns the collection, organization and analysis and interpretation of data. It’s the science of collecting evaluating and exploring the extensive amount of data to discover underlying patterns and trends of the business. Statistics applied in everyday business, industry to become more relevant about the decision that made. There are two categories of Analysis, Descriptive Analysis and Inferential Analysis.
Descriptive Analysis
Descriptive analysis is an essential step for concluding statistical analysis. The sample size in Qualitative research analysis gives you an approach of the distribution of the data that helps you to detect typos and enable you to identify associates among the variables to conduct future statistical analysis. Statistical analyses complete data of a sample summarized numerical data. It shows the mean and deviation from continuous data, whereas percentage and frequency for categorical data.
Inferential Analysis
It used to generalize the result obtained from the random population. This analysis is only applicable when a sample is drawn from a random procedure when there is a high response rate—it analysis sample from complete data sets. In the inferential analysis, you can find different conclusions from the same data by selecting othermodels.
Diagnostic Analysis
It is the form of Advanced data analytics that analysis data or content to answer additional questions. Its characterized technique such as data discovery, data mining, correlations. The diagnostic analysis focuses on past performance to determine what happened and why it happened. The diagnostic research shows the data of “Why did it happen?” by finding the cause and effect from the insight found in Statistical Analysis.Sample size calculation for secondary data analysis suses a technique that is useful to identify the behavioural pattern. If a new problem related to business arrives then you can have a look at this analysis and find similar ways to solve the problem. And it may have similar information on the occurrence of new issues.
Predictive Analysis
It is the type used to Predictive analysis that used to recommend one or more course of action while doing the research. It used to predict future outcomes. The essence of predictive analysis is a devise model such that existing data is understood to extrapolate future occurrence. Hence predictive analysis includes building and validation of the models to get accurate predictions—this analysis relies on machine learning algorithms—the most famous tool used for predictive analysis in python.
Qualitative data collection tools help in the prediction of future data depends on the already existing data as it can not obtain. It goes step ahead of standard BI in giving accurate predictions.
Prescriptive Analysis
This analysis takes predictive analysis to the next step. To know what will likely happen in the future. It suggestsa various course of action and outlines what potential implication will occur in the future.The prescriptive analysis combines the insight from all previous research to determine which actsin a current problem. Most data-driven companies are utilizing Prescriptive analysis because the predictive and descriptive analysis is not enough to improve data performance. Based on everyday situations and questions, they analyze the data and make decisions.
Conclusion:
Analytical Tools for Qualitative Research makes predictions about future outcomes based on current existing data. Its accuracy based on how much detailed information you provide and how much you dig in it.
References:
- Mkansi, M., & Acheampong, E. A. (2012). Research philosophy debates and classifications: students’ dilemma. Electronic Journal of Business Research Methods, 10(2), 132-140.
- Bonebright, T. L., Miner, N. E., Goldsmith, T. E., &Caudell, T. P. (2005). Data collection and analysis techniques for evaluating the perceptual qualities of auditory stimuli. ACM Transactions on Applied Perception (TAP), 2(4), 505-516.
- Tsien, C. L., & Fackler, J. C. (1997, August). A data collection system to support intelligent analysis of intensive care unit data. In International Symposium on Intelligent Data Analysis (pp. 111-121). Springer, Berlin, Heidelberg.