Titlebook: Quantitative Data Analysis; A Companion for Acco Willem Mertens,Amedeo Pugliese,Jan Recker Textbook 2017 Springer International Publishing

CRP743

嬉耍

How to Start Analyzing, Test Assumptions and Deal with that Pesky ,-Value,,” “skewness,” and “multivariate normality” make our heads spin and our plans seem impossible. However, we hope that, after you have gotten a kick from seeing first results with the method of your choice, you are now ready to learn about all the things you should have done first—the things that make your results credible.

悲觀

a collection process.Presents statistics without the math!.OThis book offers postgraduate and early career researchers in accounting and information systems a guide to choosing, executing and reporting appropriate data analysis methods to answer their research questions. It provides readers with a b

syncope

Invertebrate

Comparing Differences Across Groups,n in at least three ways: (1) You set up an experiment with two groups, one of which you make read a statistics book (a real cruelty), and then you make both groups analyze the same data. (2) You run an experiment with one group only, test their analysis skills, make them read a statistics book, and

使高興

Assessing (Innocuous) Relationships,ange of criteria, including originality, clarity, significance, and methodology. Have you ever wondered which of these criteria really affects whether a paper is accepted or rejected for presentation at the conference?

osculate

尖酸一點(diǎn)

Nested Data and Multilevel Models: Hierarchical Linear Modeling,s to certain levels and typically makes higher-level roles responsible for lower-level roles. At all levels of the organizational hierarchy, this approach translates into small clusters of managers and larger clusters of team members (which may include managers of lower-level teams). Such a hierarch

諷刺

Analyzing Longitudinal and Panel Data,a richer information set about observed units than would be possible with single observations and to map the evolution of the phenomenon over multiple periods for both individual units and overall as a trend. (For example, relationships between two variables may strengthen, weaken, or even disappear

憤怒事實(shí)

Causality: Endogeneity Biases and Possible Remedies,o establish whether a variation in X (the treatment) leads to a state change in Y (the effect). These studies go beyond an association between two phenomena (i.e., a correlation between variables in the empirical model) to find a true cause-effect relationship. Moving from a simple association to a