Career Perspectives from Workplace Leaders
Women Lead is an in-depth examination of women’s role in today’s workplace. Drawing on interviews with nearly 200 women leaders, and survey responses from more than 3000 male and female managers, the book explains 21st-century career trends and provides practical advice to help women excel in the new world of work. Readers will discover facts, figures, and real-life stories about leadership, education, and career planning, and learn how women are using negotiation, networking, and other collaborative practices to lead their organizations into the future.
Leadership Survey Methodology and Questions
As part of the comprehensive research for this book, Apollo Research Institute conducted a national survey to investigate how men and women of three generations perceive 21st-century leadership skills and attributes. Researchers surveyed more than 3,000 members of the Baby Boomer, Generation X, and Millennial cohorts who held management-level positions or higher in diverse industries. The survey sample was drawn from a panel of motivated respondents supplied by an online data collection company. The sample was stratified to enable comparisons across subgroups of genders and generations.
Online surveys have methodological challenges, particularly in obtaining a proportionally representative sample. To compensate for sampling limitations inherent in most online panels, the researchers used post-stratification weighting at a detailed level. Microdata from the American Community Survey (ACS) were used to construct the national counts of managers in each generational group. By knowing the prevalence of managers for each subpopulation, the researchers were able to estimate their proportion to the general population of managers in the United States. With this information, a weight was constructed to allow the researchers to correct for biases introduced in the data when some subpopulations were over-represented among respondents. ← 177 | 178 →
This method of post-stratification weighting corrects for bias in the data, but does not help in the accurate calculation of standard errors and measures derived from them, such as confidence intervals and levels of statistical significance. Accurate measures of these all require randomization or equal probability of selection, at least within...
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