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Posted by u/statswork just now What Approach Should I Take: Qualitative Or Quantitative – Statswork In practice, Data collection and Data Analysis involves two approaches: Qualitative and Quantitative approaches. Each approach has diverse kinds of objectives and statistical methodology.  Quantitative research Methodology  is useful in testing the assumptions and theories which are already Qualitative research is useful in understanding the concepts and formulating the theories.  Experimental research and surveys  are examples of quantitative research. Quantitative Research It focuses on testing the hypothesis we claim about the problem. Expressed by numbers, tables, and graphs Needs many observations The questionnaire should be in closed form i.e. with multiple-choice questions Qualitative Research It focuses on developing and formulating a hypothesis Expressed by words or text Needs a few observations The qu...

Harry is a pizza shop owner, and he’s perplexed to prefer a better location among two locations that he already shortlisted. He created up his mind to conduct a study to opt for the quintessential area. Location A is narrow, and he notices that there’s a high school two blocks away, some business offices accessible, and a Laundromat not far away. Location B is more extensive and next to a market with some business offices scattered around the space, amidst many vacant tons. If you were Harry, which might you choose? Mark Twain once quoted, “FACTS ARE STUBBORN THINGS, BUT  STATISTICS  ARE PLIABLE.” You are a  statistician  in many ways. Statistics is that the technique of conducting a study of a couple of specific topics by aggregation, organizing, decoding, and eventually presenting data. Statistics are used to analyze what’s happening within the world around us. In this data-driven world, all activities of ours are monitored by someone else every time. Statist...

The clustering technique in  Statistical Analysis  is used to determine the subsets as clusters in the data using the specified distance measure. However, this technique cannot be applied easily for longitudinal or time-series data. In this blog, I will discuss some of the methods used for modeling longitudinal or panel data using the  Clustering Analysis  technique as explained in Schmatter (2011). Longitudinal data is actually a sample of observations which are measured repeatedly over time. And, nowadays, longitudinal/repeated measure data or panel data exists in all areas of  Applied statistics  such as finance, psychology, economics, and social sciences. Most studies deals with analyzing homogeneity in such  Time series data  (Diggle et al 2002), however, there are few researchers’ shows interest in analyzing the heterogeneity in such data and they proposed different modeling technique for the same. Let us now discuss the applicabilit...

Professional statistical  Data Analysis Services  are able to meet the requirements of any customer. We'll help  Statistics Services  you to collect, analyze, interpret all the data you need. Methodology can be defined as the rationale for the methods used in each study.“ Methodologies ” refer to the overall approach to the research process, from the theoretical underpinning to the collection and analysis of data.  Statswork  i s a pioneer statistical consulting company providing full assistance to researchers and scholars. Statswork offers expert consulting assistance and enhancing researchers by our distinct statistical process and communication throughout the research process with us. Contact Us: Website: www.statswork.com Email: info@statswork.com UnitedKingdom: +44-1143520021 India: +91-4448137070 WhatsApp: +91-8754446690

COMPARISON OF MULTILEVEL MODEL AND ITS STATISTICAL DIAGNOSTICS Diagnostics in Statistical Analysis is atmost important because there may be few influential observations which may distort the inference of the problem statement at hand. It is to be noted that all influential observations are not outliers, but some outliers are influential. In this blog, I will point out few standard statistical diagnostics in multilevel data. Multilevel data and its diagnostics Multi-level models are the statistical models of parameters (like in usual linear regression model) that vary at more than one level. It is also referred with many terms, namely, mixed-effect models, random effect model, hierarchical models and many more. In recent times, with the advent of statistical software and computations, multi-level or hierarchical models are widely used for longitudinal repeated measures analysis and in many meta data applications. Multi-level models could also applicable for non-linear case too ...