By programming in a syntactic language, such as R or Python (Van Rossum & Drake, 1995), analysts can apply complex methods that are not easy to parameterize with spreadsheet or graphical, menu-driven software. Both of these advantages allow software like SPSS and Tableau (► ) to facilitate communication between stakeholders and conduct data exploration interactively in meetings.Ĭomputational analytics using programming syntax has been present since the earliest days of computing, but has recently gained new popularity from the advent of big data analytics, artificial intelligence, and the larger data science movement. Graphical, menu-driven software has also become popular during recent decades, since it is easier to learn and process for many users and provides rich visualizations. Spreadsheets are advantageous for smaller datasets, since they make it easy for users to manipulate data in tabular form and obtain quick results in the same interface as their data. Spreadsheet software has long been of value to business researchers, since a familiar ledger or balance book metaphor is adopted that predates computers. We expect that many quantitative researchers are already familiar with certain types of software to analyze data: spreadsheet software, such as Apache OpenOffice Calc (► ) or Microsoft Excel (► ), and more graphical, menu-driven software like IBM SPSS (► ) and Statistica (► ). Thus, researchers who learn and adopt computational methods will have the advantage of being able to apply and adapt the latest techniques to their research, while also being competent and conversant with industry trends. Similarly, academic research is rapidly adopting computational methods, involving the implementation of analytic techniques for inferential analysis and machine learning into computer programs (Hair & Sarstedt, 2021). Additionally, we will provide examples of R code throughout this textbook, so we start by looking at the syntax and formatting that we will use to distinguish code from regular text.ĭata analytics using computationally intensive methods is becoming an increasingly important, strategic capability for companies to transform the data collected during business activities into information that can assist effective decision-making and policy creation. This textbook will not serve as a comprehensive resource for learning R, so we will share further resources for learning this programming language and helpful documentation on the Internet. We then offer a basic introduction to writing analytic scripts in R. We will explain how to download and install the software required, how to interact with the software, and how to store your data and code. We will then introduce the RStudio (RStudio Team, 2021) application, which is an integrated development environment that enables you to easily and productively conduct computational analyses using the R language. First, we introduce the R statistical computing language (R Core Team, 2021), which is the software language we will use to import and clean data as well as create and analyze PLS path models. Software packages are a series of software functions and features with a similar purpose bundled into a single set. This chapter introduces the two software packages that will be used throughout this textbook.
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