Systematic review and meta-analysis are often confused and used interchangeably albeit having distinct differences between them. Both are reviews of high quality evidence that form a part of literature review.  What is a Systematic review?  A systemic review seeks to find answer to a defined question by collecting and synthesizing high quality evidence from different studies. It is an exhaustive summary of all scientific evidence substantiated to a particular question. Systematic reviews came to be as a way to improve the quality of literature review. Systematic review applies to all types of research, both qualitative and quantitative. Systematic review evolved in the health care industry and clinical trials, but has since found its application in variety of fields like social sciences, astronomy, etc. What is a Meta-analysis?   Meta-analysis integrates statistically similar studies using statistical principles to achieve an estimate of common po...

Reacquaint yourself with simple yet important data analysis tools to transition from abstract numbers to quantifiable success. In today’s digital era, we are inundated with information—it’s overwhelming. Leaders at the top helm make decisive decisions daily. Sorting through avalanche of data to assist management’s decision making is the key. Objective is to sort and  interpret  and report findings. Toward this end, tools or techniques for statistical data analyses achieve this objective. Buzzwords we hear today are big data, hadoop, and AI; however, rudimentary tools need to be deployed to their maximum capability, which is not the case in most firms. Propel your  data analysis   program by mastering the following 5 basic techniques. Review these mundane statistical techniques, yet so vital. Techniques Merits Demerits 1. Hypothesis Testing Hypothesis tests are used in multiple disciplines such as science, research, economics, etc. Popularly kn...

Data Analysis Plan for Quantitative and Qualitative Research: Data Analytics is the quest of excavating an understanding or inference from unprocessed data via dedicated computer applications. These applications alter, shape, and model the data to infer deductions and ascertain patterns. Although Data Analytics can be uncomplicated, nowadays the term is most frequently used to define the study of huge capacities of data ( quantitative and qualitative data ) and/or high-speed data, which offers unique mathematical and data-juggling obstacles. Proficient data analytics pros who essentially possess a strong know-how in business statistics, are known as data scientists. Data Analytics is a generic word for any type of data manipulation that considers historical data over a time frame; however, as the volume of organizational data increases, the concept of data analytics is developing to support big data-capable systems. The dawn of big data dramatically altered the need f...

With more and more companies using big data, demand for professional data analysts has witnessed exponential growth in the recent years. “Learners of data analysis and interpretation need to have an in-depth understanding of the subject along with the statistical acumen and working knowledge of tool sets to make significant progress in the field of data analytics” – Dr. Venkatesh, Statistician. To gain good expertise in Data analysis and interpretation, the beginner learner requires good foundation in basic statistics, and better skills of how to input, process and analyze data by employing a range of data analytical and visualization tools. The objectives of data analysis and interpretation are to manipulate the data, both qualitative as well as quantitative, to obtain utilizable information that is critical to making informed decisions. It involves the usage of many different methods and procedures that can, Describe and summarize data Evaluate and enhance qua...

Statistics is an integral part of all research studies, a discipline that all researchers and scientists commonly use to analyze their data and arrive at the desired research findings and effectively present meaningful conclusions. Statistical techniques , presently, play a central part in the majority of scientific disciplines and are frequently utilized to support hypotheses in order to provide credibility to research methodology and conclusions arrived from these studies. Thus, many researchers and scholars are keenly interested In Statistics and its various methods and analytical techniques to ascertain the credibility and usefulness of the data and information collected from their respective research studies. This short guide provides essential information relating to important statistical concepts like sampling methods, the role of statistics in scientific research to facilitate them to conduct a well-designed statistical research. What is Statistics? Statistics...

Tremendous amount of data has been collected by organizations all around the world thanks primarily to the World Wide Web! This collection is useless unless it is analyzed to see a pattern. It is important for decision makers, researchers and policy makers throughout industry, academia and Governments to integrate the huge collected data to suit their needs.  A good software  provider will seek to understand your organization’s unique needs and work with you to find the most cost-effective solution. How does one decide the best statistical software to use? There are questions of price, time available to learn to use it and most importantly the nature of data. Excel is quite a powerful instrument often used in biomedical research to combine large volumes of data. It does contain some  basic statistical analysis  and could serve for simple inferences. Data can be exported into an excel spreadsheet, making it easy to test or determine the dispersion...

Statistics are handled efficiently in myriad ways in the field of data analysis. Statistical analysis is a module of data analytics. A representative selection from a total population is drawn and the data collected is scrutinized so that the sample data speak for a set of items dealt with, in the framework of business intelligence. It involves a description of the nature of data that is to be analysed, drawing a parallel between the sample data selected and the underlying population, coming up with a model that recapitulates the understanding of how the sample data is in regard to the total population, substantiating the standpoint that the model is reliable, and making use of predictive analytics in order to enlist possible scenarios that make the study relevant, paving the way for further research. Demography and statistics are yet another link that is often explored, making utmost use of statistical data analysis services for the population study, research, administratio...