Category: Learning modules »

Performing wordlist comparing, KWIC and text profile in Hamlet II

In the previous article, the steps of performing Wordlist in Hamlet II software were presented. In this article, the steps of comparing wordlist, keyword in-context or KWIC and text profiling have been presented. Read more »

How to use an instrumental variable?

Instrumental variable is a third variable that estimates causal relationships in the regression analysis when an endogenous variable is present. Instrumental variables are useful when the independent variable in the regression model correlates with the error term in the model. A major complication in econometrics is the possibility of inconsistent parameter estimation due to endogenous regressors. Read more »

Prediction and forecasting using ARIMA in STATA

After performing Autoregressive Integrated Moving Average (ARIMA) modelling in the previous article: ARIMA modeling for time series analysis in STATA, the time series GDP can be modelled through ARIMA (9, 2, 1) equation as below:

Figure 1: ARIMA Results in STATA

Figure 1: ARIMA Results in STATA

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Understanding effect size in CMA

In the previous article: Data entry formats in CMA, different data entry formats were shown. In the discussion surrounding meta-analysis, effect size holds the prime importance, when it comes to analysis. An effect size is the magnitude or size of an effect resulting from a clinical treatment. Thus, in Comprehensive Meta Analysis (CMA), it assumes the reference of “treatment effect”. As different studies generate different datasets of variable nature, hence the effect size for a given study cannot be constrained to one particular index or measure (“measures” will be used in the article). Read more »

Understanding the various data entry formats in CMA

The software interface was discussed in the previous articleIntroducing Comprehensive Meta analysis (CMA) software. To calculate the effect size, one needs to enter the data into specific data entry formats. This article will acquaint the user with the procedure of entering the data into the software. Read more »

How to perform LASSO regression test?

In statistics, to increase the prediction accuracy and interpret-ability of the model, Least Absolute Shrinkage and Selection Operator (LASSO) is extremely popular. It is a regression procedure that involves selection and regularisation and was developed in 1989. LASSO regression is an extension of linear regression that uses shrinkage. The LASSO imposes a constraint on the sum of the absolute values of the model parameters. Here the sum has a specific constant as an upper bound. This constraint causes regression coefficients for some variables to shrink towards zero, i.e. ‘shrinkage’. The LASSO regression is easy when there is automatic feature or variable selection. It is also useful when dealing with predictors with high correlation, where standard regression will usually have large regression coefficients. Read more »

ARIMA modeling for time series analysis in STATA

In the previous article, all possibilities for performing Autoregressive Integrated Moving Average (ARIMA) modeling for the time series GDP were identified as under.

S. No ARIMA
1 (1,1,1)
2 (1,1,2)
3 (1,1,3)
4 (1,1,4)
5 (1,1,5)
6 (1,1,6)
7 (1,2,1)
8 (4,2,1)
9 (9,2,1)

 Table 1: ARIMA models as per ACF and PACF graphs.

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How to apply missing data imputation?

Missing data is one of the most common problems in almost all statistical analyses. If the data is not available for all the observations of variables in the model, then it is a case of ‘missing data’. Missing data are part of almost all researches. They are also a common problem in most scientific research domains such as biology and medicine. If missing values are not treated well then complications arise in handling and analyzing the data. Read more »

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