Table 1 - uploaded by Jerry J. Vaske
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Source publication
Human dimensions survey data are commonly stored in flat files where the rows correspond to individuals and the columns are variables. As the number of variables increases (e.g., 1,000+) or when compressed variables are used, the complexity of understanding the data increases substantially. This article illustrates how data can be restructured into...
Contexts in source publication
Context 1
... types of data structures are considered in this article: (a) flat files and (b) relational databases. A flat file structure is illustrated with variables for storing information about arti- cles published in Human Dimensions of Wildlife (HDW) ( Table 1). Each row of Table 1 rep- resents a journal article published in HDW. ...
Context 2
... flat file structure is illustrated with variables for storing information about arti- cles published in Human Dimensions of Wildlife (HDW) ( Table 1). Each row of Table 1 rep- resents a journal article published in HDW. Each column is a variable characterizing a given article. ...
Context 3
... flat file data structure for HDW articles resulted in multiple columns with similar information and numerous empty cells. For example, because the article by Diefenbach et al. (2005) had seven co-authors (last row, Table 1), seven columns (variables) were devoted to author information. Because 19 of the 26 articles had only one or two authors, more than 67% of the author fields were blank. ...
Context 4
... is referred to as a "many to one" relation. For example, article number 2059 (Table 1) would occur in three rows in R1. Each row is for one of the three authors of article 2059. ...
Citations
... Below we outline some key characteristics distinguishing each survey (see Table 1). Response rates for each survey were calculated as the percentage of completed surveys per total number of eligible respondents (Beaman and Vaske 2008). Significant differences in response rates were assessed with chi-square tests. ...
To improve the economic and environmental sustainability of agriculture , information is needed on how to target research, teaching, and outreach programs. However, conducting survey research in general, and with agricultural producers specifically, is increasingly challenging given issues such as declining response rates and limited resources. While studies examining the best practices for promoting higher response rates exist, few focus explicitly on agricultural producers. In three separate surveys conducted with agricultural producers in South Dakota in 2018 and 2019, we included experiments testing how token pre-incentives, a research partnership, and response mode options impacted response rates. We also examined how sample source and email augmentations influence survey responses. The study findings indicate that providing pre-incentives and multiple simultaneous response options can increase response rates with agricultural producers. On the other hand, email augmentation to mail surveys, sample source, and identification of select institutional research partnerships appear to have minimal effects. ARTICLE HISTORY
... Response rates were calculated as the percentage of completed surveys per total number of eligible respondents (Beaman and Vaske 2008). Completed surveys were defined as any survey returned with at least one question answered with usable data. ...
Response rates to mail-based surveys have declined in recent decades, and survey response rates for farmers tend to be low overall. Maintaining high response rates is necessary to prevent non-response bias. Historically, incentives have been an effective tool to increase response rates with general populations. However, the effect of incentives on farmers has not been well tested. In this study, we experimentally manipulated the use of a $2 incentive in two surveys targeted at farmers. We tested both the use of the incentive and the timing of incentive distribution in the survey process. We found the incentive significantly increased response rates with farmers but there was no significant effect of when the incentive was distributed. Additionally, we evaluated the cost-effectiveness of using the incentive. While the incentive increased response rate, the cost per survey response also increased and the cost of the incentive was not offset by the increased response rate.
... Furthermore, the sample selection model was applied on the 2006 FHWAR, which was the most recent data when the study was conducted. 1 Beaman and Vaske (2008) used the 2006 FHWAR data set as an example to demonstrate how to work on the data set efficiently but did not address any specific economic issue. Hussain et al. (2012) used a general equilibrium model and the 2006 FHWAR data set to evaluate the aggregate impact of wildlife-associated recreation in the southeastern United States. ...
Wildlife watching has become more popular in recent years. By use of data from the 2006 National Survey of Fishing, Hunting, and Wildlife-Associated Recreation, the demand for nonresidential wildlife watching and associated consumer surplus in the United States were assessed at the national scale. Participation and trip frequency were jointly examined through a sample selection model. The binary probit and negative binomial models identified several significant factors, including demographic characteristics, resource availability, and costs of relevant activities. Furthermore, wildlife watching was found to have a varying relation with hunting and fishing, depending on the type of decision being made. When an individual made a decision whether to participate in wildlife watching, hunting and fishing were a substitute for wildlife watching. Once the participation choice was made, however, the relation became complementary. Total consumer surplus of nonresidential wildlife watching in the United States was up to $217 billion in 2006. These findings can help policymakers design better programs to promote wildlife watching and assist land managers to improve resource management.
Using diagrams of data structure, or conceptual models, is important in businesses. Survey research often has if-then data structure, but discussion of diagramming survey data structure is rare. This study uses the U.S. Fishing, Hunting and Wildlife-Associated Recreation (FHWAR) survey data and a Taiwan survey in the analysis of benefits of using data structure diagrams in survey research. Examples of data structure diagram use show how diagramming can support consistent and logical data collection, as well as improved data storage and analysis. Analysis also shows how storing if-then (conditional) data in entities/tables allows simple and intuitively meaningful unconditional variable names and can facilitate consideration of conditions that should/can affect analysis. A general conclusion is that the time has come for tourism and business survey researchers to benefit from using diagrams of data structure in planning data accumulation and to benefit from using modern systems in data collection, storage and analysis.
This issue of JBR is the consequence of a call for a special issue on Work, Leisure, and Tourism in the Pacific Rim. Who the special issue editors are presumably prompted submissions related to tourism or hospitality. The articles in the issue relate to the business aspect of tourism, and they show the diversity in tourism research. The study of entertainment in Macao's future presents stakeholder views. The wine tourism paper is not on demand or tourism behavior but addresses supply side issues. The articles on employees' work–family conflict moderating life and job satisfaction and telepresence affecting the demand for tourism have business ramifications. The paper on empowerment pursues applying Western views in applying a concept in the East. The study of tourists' perceptions on safety broaches some important analysis issues. The paper on using diagrams to improve survey research has broad ramifications for survey research, particularly regarding getting and using complex data. The final article addresses effectiveness and offers ideas that apply to application of DEA in business research.
