15 May 2023
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Developing an Innovation Leader Survey (ILS)
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.
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 available for developing an Innovation Leader Survey. Besides showing the numbers, colour illustration of certain indicators helps researchers to get an overview quickly.
Data Analytics with R: Code and Results
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 researcher’s choice of tools for the next steps towards the Innovation Leader Survey.
Data Analytics with R: Code and Results
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
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 of help.
Data Analytics with R using Lavaan and other free packages makes SEM a very easy step in developing the Innovation Leader Survey. 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
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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