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 point of truth b

In the developing domain of big data, the role of a data miner is pivotal; your business may see the value in harnessing data. The key lies in knowing how to mine data for business success. Australian statisticians from Statswork offer some suggestions to learn the programming languages for data mining. First, let us define data mining.  Data mining   is the course of habitually sorting over large data sets to identify patterns or trends. Data mining transcends the average data analysis that do manually. Data mining can rapidly rid the guesswork out of data by observing automated predictions of behavior. Hence, use data mining to harness and interpret large amounts of data. That implies data mining has many merits you can assimilate into your knowledge discovery phases to collate information that affects your business revenue; say for instance, the banking and financial sector could use it to look at historical data and new trends to determine what type of stocks to inves

There is no “mantra” to simplify statistics and data analysis   for learning; however; statisticians at Statswork offer some keys to circumvent pitfalls and ensure smooth flow of your research work.  Here are some top tips to ease your research phase. First Review Descriptive Stats: Many folks do things the other way round; for example, they do complex analysis prior to analyzing the data.  Most of the time, descriptive statistics provide pivotal background for advanced analysis thereby providing clarity of interpretation . Second, Prune Data Before Analysis: Doing this helps you focus on analysis; you can manually delete unwanted variables after taking a backup or leverage the “Define Variable Sets” feature. Third, Refrain Analyzing The Master File: Ensure you work on a copy of the data. Normally, things may not go wrong but better be safe than sorry. You never know what can happen; hence, backup your master copy of data. Fourth, Anchor Hypothesis On Theory: Don’t

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 known as t-testing, h

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