All articles by Priya Chetty

How to perform unit root test?

Unit root indicates a stochastic trend in the time series. Sometimes it is known as “random walk with drift”. A time series dataset will show a systematic unpredictable pattern if it has the unit root. If a time series dataset has the unit root, the regression result will be unreasonable and provide a spurious result (in which there is large r-squared value even if the data is uncorrelated) and errant behavior (in which t-rations will not follow at- distributions). Therefore it is important to perform unit root test. Read more »

How to perform Johansen cointegration test?

If a series is nonstationary in time series without a constant mean and constant variance, the regression results will be spurious. But regression results can be reliable when a linear combination of non-stationary series (dependent and independent) removes the stochastic trend and produces stationary residuals. Therefore, it is implied that variables are co-integrated. Co-integrated also assumes that there is the occurrence of stochastic non-stationary series, underlying two or more process (p). Read more »

Importance of Granger causality test

Granger causality is a method to examine the causality between two variables in a time series. “Causality” is related to cause and effect notion, although it is not exactly the same. It is a statistical concept which is based on the prediction. If X variable’s Granger causes Y, then past values of X should contain information that helps in predicting Y. Read more »

Understanding random operating curves or ROC analysis

Previous articles in this module on logistic regression and discriminant analysis explained how to know the classification of a group of observations based on some selected variables. In results, the articles predicted a binary classification (in the case of logistic regression) and classified the observations (like student hired or not hired). Receiver Operating Curve (ROC) is an extension of such classifications. Performance of binary classifier system in the case of ROC analysis can be tested. Read more »

Demographic data representation in Nvivo

The previous article explained how to generate queries within the data processed. This article explains the succeeding step of data visualization of results generated for the case research. Data visualization is a technique for data representation in the form of tables, charts and diagrams. This article explains representation of demographic information of the participants (e.g. teachers) based on nodes in Nvivo. Read more »

Data analysis by generating Nvivo coding query

The next step after processing data through coding and creating memos and classifications is analysis. Nvivo coding query eases the understanding of nodes and their interconnections. Given the vast array of nodes generated, researchers find it difficult to connect two nodes. Therefore examining elements and checking if such connection is possible is also challenging. Read more »

Creating and managing Nvivo memo

Memos are notes which can be linked on a project item in Nvivo. They are used to record and maintain elements of a project. They are a part of ‘Sources’ folder and are saved in ‘Memos’ folder. A memo can be made on the entire project or a single part of it. For example, memo can be created for recording notes about a particular interview. Read more »

Generating Nvivo matrix coding query

The previous articles, explained how to carry out data analysis by generating Nvivo word frequency coding query. Queries can be generated in Nvivo by 3 ways; words, content and matrix coding. Not only words but also by number of nodes or classification in different nodes and attributes. For that purpose, Nvivo matrix coding query is useful. In the case research, two different interviewees’ responses about ‘preference towards teaching’, based on their ‘years of experience’ is examined. This article explains the process for generating queries in coding matrix. Read more »

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