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Data Analytics With R

Data Analytics becomes more and more important for any organisation over all industries worldwide. This trend has to do with two developments: Firstly, more and more high-quality data is available to describe any kind of business-related activities, consumer behaviour and workforce matters. Secondly, potent hard- and software is at hand that can handle, analyse and save huge amounts of data.

One of the popular analysis packages is R. Data analytics with R and RStudio is an easy way to overcome high costs for powerful analysis tools. In the following, we offer some examples of data analytics with R.

Study: Understanding the Drivers for Innovative Work Behaviour

The current global economic landscape is forcing all organizational sectors to remain relevant by innovating services, products and work processes. Therefore, more than before, organisational leaders must enable innovative behaviour of their employees. We conducted a survey study among 406 employees from six public and private sector service organisations in Singapore to test the relationship between transformational leadership and employee innovative work behaviour. The findings might be useful to design and implement effective human resource and organisational development interventions within Asian service organisations.

This case shows a comprehensive example of Data Analytics with R.

Descriptive Statistics

Any data analysis starts with an inspection of the descriptive statistics that includes mean, standard deviation, minimum, median, maximum, quartiles and kurtosis as well as skewness. Kurtosis and skewness give hints for normality of the data.

Additionally, a series of graphical displays should be part of descriptive statistics. Time plots, histograms, box plots, dot plots, violin plots or a combination of these are helpful to illustrate characteristics of data.

Data Analytics with R: Code and Results

Data Analytics with R - Violin Plot

Correlation Analysis

In research, one of the first steps when searching for relationships between items or factors is correlation analysis. This very common analysis determines whether there is a relationship between two variables. This relationship can show as -1 … 1, i.e. can be positive or negative, causal or not. In research it is a simple and important step to calculate and show this relationship.

A correlation matrix displays the correlation between many different variables in a one to one relationship as seen in the figure. This way, clusters of high correlations can be detected that may lead to common factors later. This means, correlation analysis is a prerequisite for factor analysis and structural equation modelling and many other approaches.

Data Analytics with R: Code and Results

Data Analytics with R - Correlation Matrix

Factor Analysis (EFA, CFA)

Especially when analysing survey data, understanding clusters of rating results, i.e., clusters of similar behaviours belong to the same factor. Finding these factors is an important part of research either to explore new models (Exploratory Factor Analysis – EFA) or to confirm whether the new data fit an existing model (Confirmatory Factor Analysis – CFA).

R is well equipped to support these and many other research tasks with Lavaan and other free packages.

Data Analytics with R: Code and Results

Data Analytics with R - Scree Plot

Structural Equation Modelling (SEM)

In order to understand the working mechanism of different factors such as Leadership Style (TFL), Support for Innovation (SFI), Individual Innovation Readiness (IIR) and their effect on Innovative Work Behaviour (IWB), structural equation modelling (SEM) can be deployed.

In the past, SEM was only open for a few researchers who had powerful hardware and potent software to deal with the statistics needed to conduct SEM. Now thanks to developments in both fields, nearly every researcher has access to SEM.

Data Analytics with R: Code and Results

Data Analytics with R - SEM

This analysis package is part of a research project with the University of Twente, The Netherlands. Read more about the study on Innovative Work Behaviour that includes the Data Analytics with R shown above.

More examples of Data Analytics with R can be found here.

Study: Developing an Innovation Leader Survey

Our Study “Understanding the Drivers for Innovative Work Behaviour” brought a few interesting insights. One of these is the apparent importance of leaders behaviour to drive the ultimate objective, the Innovative Work Behaviour of their employees.

Therefore, we intended to identify which leader behaviours, in particular, drive such behaviour of their followers and design a survey to measure that. These survey items can serve as part of a more comprehensive survey for leadership development or can form an Innovation Leader Survey.

Descriptive Statistics

As usual, comprehensive descriptive statistics give first insights into the data. Besides showing the numbers, colour illustration of certain indicators helps researchers to get an overview quickly.

Data Analytics with R: Code and Results

Data Analytics with R - Descriptive Statistics

Graphical Data Analysis

Every statistical analysis must be accompanied with the appropriate graphs. Whilst graphs do not drive decisions, they always add information not available in descriptive statistics of any kind.

Knowing the distribution of the data, for example, guides the researchers choice of tools for the next steps.

Data Analytics with R: Code and Results

Data Analytics with R - Histograms

Scale Reliability

Scale reliability is measured using Cronbach’s Alpha, for example. This shows whether items that are clustered in one factor belong together based on their pattern. As such, it measures internal consistency of items we have put in the same group.

Data Analytics with R: Code and Results

Data Analytics with R - Cronbach's Alpha

Structural Equation Modelling (SEM)

Lastly, we needed to prove that our new survey instrument measures behaviour of leaders that in turn instils Innovative Work Behaviour (IWB) in their followers. The appropriate instrument to perform exactly this test , structural equation modelling (SEM) can be help.

Data Analytics with R using Lavaan and other free packages makes SEM a very easy step. Lavaan helps produce necessary fit indices like Chi Square, p-values, CFI, TLI, RMR/SRMR, RMSEA and others to evaluate the suitability of the model.

Data Analytics with R: Code and Results

Data Analytics with R - SEM

This analysis package is part of a research project with the University of Twente, The Netherlands. Read more about the study on Innovative Work Behaviour that includes the Data Analytics with R shown above.

More examples of Data Analytics with R can be found here.

Conclusion

Data analytics with R and RStudio is an easy way to overcome high costs for powerful analysis tools. We have performed rather complex analysis with R as shown above. There are practically no limitations to your research when you choose this environment.

And, whenever you have questions or look for a workshop to implement Data Analytics with R, talk to us (+65 6100 0263). Our partners, SUTD and SMU will help to design a package that suits your needs.

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