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4582 results in Management: general interest

Page 58 of 230

  • 6 - R

  • from Part II - Tools for Data Science
  • Chirag Shah, University of Washington
  • Book:
    A Hands-On Introduction to Data Science
    Published online:
    01 February 2020
    Print publication:
    02 April 2020, pp 161-186

  • Summary

    While a versatile programming language such as Python can provide a framework to work with data and logic effectively, often we want to stay focused on data analysis. In other words, we could use a programming environment that is designed for handling data and is not concerned with programming so much. There are several such environments or packages available – SPSS, Stata, and Matlab. But nothing can beat R for a free, open-source, and yet a very powerful data analytics platform.

    And just because R is free, do not think even for a second that it is somehow inferior. R can do it all – from simple math manipulations to advanced visualization. In fact, R has become one of the most-used tools in data science and not just because of its price.

Power Grab
  • Political Survival through Extractive Resource Nationalization
  • Paasha Mahdavi
  • Published online:
    31 March 2020
    Print publication:
    02 April 2020
  • For rulers whose territories are blessed with extractive resources - such as petroleum, metals, and minerals that will power the clean energy transition - converting natural wealth into fiscal wealth is key. Squandering the opportunity to secure these revenues will guarantee short tenures, while capitalizing on windfalls and managing the resulting wealth will fortify the foundations of enduring rule. This book argues that leaders nationalize extractive resources to extend the duration of their power. By taking control of the means of production and establishing state-owned enterprises, leaders capture revenues that might otherwise flow to private firms, and use this increased capital to secure political support. Using a combination of case studies and cross-national statistical analysis with novel techniques, Mahdavi sketches the contours of a crucial political gamble: nationalize and reap immediate gains while risking future prosperity, or maintain private operations, thereby passing on revenue windfalls but securing long-term fiscal streams.

  • Chapter 6 - Measurements

  • from Part II - The Research Process
  • Pervez Ghauri, University of Birmingham, Kjell Grønhaug, Norwegian School of Economics and Business Administration, Bergen-Sandviken, Roger Strange, University of Sussex
  • Book:
    Research Methods in Business Studies
    Published online:
    17 January 2020
    Print publication:
    12 March 2020, pp 78-92

  • Summary

    Empirical research requires the collection and analysis of data and other information. The quality of the research (and the conclusions derived therefrom) depend upon the collection of appropriate data, the quality of the data collected, and on how well the data are analysed. Quantitative research requires the measurement and enumeration of the variables to be used in the analysis. In this chapter, we first explain the process of operationalization, by which researchers decide how to measure the theoretical concepts they use. The second section considers different scales of measurement, and highlights some of the implications for empirical analysis. The third section focuses on the measurement of multi-dimensional variables, and the generation of latent constructs. The fourth section addresses how to assess the reliability and validity of variables and multi-dimensional constructs. The fifth section offers some practical suggestions for improving the measurement of the variables used in quantitative research, whilst the final section is concerned with measurements in qualitative research.

Page 58 of 230