{"title":"Data Visualization","authors":"E. Sinar","doi":"10.1093/oso/9780190939717.003.0019","DOIUrl":"https://doi.org/10.1093/oso/9780190939717.003.0019","url":null,"abstract":"Data visualization—a set of approaches for applying graphical principles to represent quantitative information—is extremely well matched to the nature of survey data but often underleveraged for this purpose. Surveys produce data sets that are highly structured and comparative across groups and geographies, that often blend numerical and open-text information, and that are designed for repeated administration and analysis. Each of these characteristics aligns well with specific visualization types, use of which has the potential to—when paired with foundational, evidence-based tenets of high-quality graphical representations—substantially increase the impact and influence of data presentations given by survey researchers. This chapter recommends and provides guidance on data visualization techniques fit to purpose for survey researchers, while also describing key risks and missteps associated with these approaches.","PeriodicalId":192200,"journal":{"name":"Employee Surveys and Sensing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123069260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Focus Groups","authors":"Jeffrey M. Cucina, Ilene F. Gast","doi":"10.1093/oso/9780190939717.003.0007","DOIUrl":"https://doi.org/10.1093/oso/9780190939717.003.0007","url":null,"abstract":"When used in conjunction with employee surveys, focus groups can provide valuable qualitative data to support the employee survey process. Focus groups held prior to survey development and administration can uncover issues worthy of investigation and evaluate draft survey questions for sensitivity and readability. Post-survey focus groups can elucidate issues identified by the survey, solicit organizational members’ suggestions for resolving these issues, and gain management and employee feedback on possible programs to address identified issues. After describing how focus groups fit into the context of industrial–organizational psychology methodology, the authors outline steps for designing and conducting focus group studies and for analyzing the resulting data and reporting findings. The chapter concludes with an annotated list of additional resources for conducting focus groups.","PeriodicalId":192200,"journal":{"name":"Employee Surveys and Sensing","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125513503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Open-Ended Questions","authors":"Subhadra Dutta, Eric O’Rourke","doi":"10.1093/oso/9780190939717.003.0013","DOIUrl":"https://doi.org/10.1093/oso/9780190939717.003.0013","url":null,"abstract":"Natural language processing (NLP) is the field of decoding human written language. This chapter responds to the growing interest in using machine learning–based NLP approaches for analyzing open-ended employee survey responses. These techniques address scalability and the ability to provide real-time insights to make qualitative data collection equally or more desirable in organizations. The chapter walks through the evolution of text analytics in industrial–organizational psychology and discusses relevant supervised and unsupervised machine learning NLP methods for survey text data, such as latent Dirichlet allocation, latent semantic analysis, sentiment analysis, word relatedness methods, and so on. The chapter also lays out preprocessing techniques and the trade-offs of growing NLP capabilities internally versus externally, points the readers to available resources, and ends with discussing implications and future directions of these approaches.","PeriodicalId":192200,"journal":{"name":"Employee Surveys and Sensing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116284758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"How Did We Do?","authors":"Elizabeth A. McCune, Sarah R. Johnson","doi":"10.1093/oso/9780190939717.003.0011","DOIUrl":"https://doi.org/10.1093/oso/9780190939717.003.0011","url":null,"abstract":"The purpose of this chapter is to provide an overview of survey benchmark data, including where and how to access external survey benchmarks, what to consider when evaluating survey benchmarks, and a glimpse into the future of survey benchmarks. Practitioners are encouraged to evaluate the quality of benchmarks by considering both the generalizability of the sample used to generate the benchmarks as well as the comparability and relevance of benchmark items. Practitioners are also encouraged to consider how benchmarks can be used to drive action, where they might provide the most useful context, and the compromises and trade-offs required in the use of benchmarks.","PeriodicalId":192200,"journal":{"name":"Employee Surveys and Sensing","volume":"290 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116390883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Linkage Analysis","authors":"S. D. Duco, P. Hyland, D. Reeves, A. Caputo","doi":"10.1093/oso/9780190939717.003.0017","DOIUrl":"https://doi.org/10.1093/oso/9780190939717.003.0017","url":null,"abstract":"Linkage analysis is a framework for determining the impact that employee attitudes, as measured by organizational surveys, have on business outcomes. Linking employee attitudes to outcomes such as employee turnover and performance provides a compelling business case for executives to invest both emotionally and financially in employee surveys. The current chapter reviews the large body of research supporting the linkage analysis framework, as well as common approaches and challenges. Three case studies from the field are also presented, along with practical recommendations for translating linkage results into meaningful actions that organizations can take. The authors conclude by sharing the implications of linkage analysis in an era of big data.","PeriodicalId":192200,"journal":{"name":"Employee Surveys and Sensing","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123795778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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