Peter Lyons and Howard J. Doueck
- Published in print:
- 2009
- Published Online:
- February 2010
- ISBN:
- 9780195373912
- eISBN:
- 9780199865604
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195373912.001.0001
- Subject:
- Social Work, Research and Evaluation
This book is intended to be read at any stage in the dissertation process, but will be particularly useful in the early stages of preparation for a social work dissertation, and as a reference ...
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This book is intended to be read at any stage in the dissertation process, but will be particularly useful in the early stages of preparation for a social work dissertation, and as a reference resource throughout. The book is a guide to successful dissertation completion. Content includes a brief history and overview of social work doctoral education in the United States, the importance of values in social work, and the relationship between personal, research, and social work values. Chapter 2 addresses issues in selecting and working with the dissertation supervisor and committee, as well as the role and tasks of all three parties in successful completion of the dissertation. In Chapter 3 strategies for researching, and evaluating the literature, as well as writing the literature review are discussed. In addition, the relevance of theory to social work research is examined. Chapter 4 describes ethical issues in social research and requirements for the protection of human subjects. In addition, an overview of both quantitative and qualitative research methods is provided. In Chapter 5 sample design and sample size are discussed in relation to both quantitative and qualitative research. The significance of the psychometric properties of measurement instruments is also discussed. Chapter 6 addresses issues in data collection, data management, and data analysis in qualitative and quantitative research. Finally Chapter 7 presents strategies for dissertation writing including structure and content, as well as data presentation.Less
This book is intended to be read at any stage in the dissertation process, but will be particularly useful in the early stages of preparation for a social work dissertation, and as a reference resource throughout. The book is a guide to successful dissertation completion. Content includes a brief history and overview of social work doctoral education in the United States, the importance of values in social work, and the relationship between personal, research, and social work values. Chapter 2 addresses issues in selecting and working with the dissertation supervisor and committee, as well as the role and tasks of all three parties in successful completion of the dissertation. In Chapter 3 strategies for researching, and evaluating the literature, as well as writing the literature review are discussed. In addition, the relevance of theory to social work research is examined. Chapter 4 describes ethical issues in social research and requirements for the protection of human subjects. In addition, an overview of both quantitative and qualitative research methods is provided. In Chapter 5 sample design and sample size are discussed in relation to both quantitative and qualitative research. The significance of the psychometric properties of measurement instruments is also discussed. Chapter 6 addresses issues in data collection, data management, and data analysis in qualitative and quantitative research. Finally Chapter 7 presents strategies for dissertation writing including structure and content, as well as data presentation.
Ray Chambers and Robert Clark
- Published in print:
- 2012
- Published Online:
- May 2012
- ISBN:
- 9780198566625
- eISBN:
- 9780191738449
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198566625.001.0001
- Subject:
- Mathematics, Probability / Statistics
This book is an introduction to the model-based approach to survey sampling. It consists of three parts, with Part I focusing on estimation of population totals. Chapters 1 and 2 introduce survey ...
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This book is an introduction to the model-based approach to survey sampling. It consists of three parts, with Part I focusing on estimation of population totals. Chapters 1 and 2 introduce survey sampling, and the model-based approach, respectively. Chapter 3 considers the simplest possible model, the homogenous population model, which is then extended to stratified populations in Chapter 4. Chapter 5 discusses simple linear regression models for populations, and Chapter 6 considers clustered populations. The general linear population model is then used to integrate these results in Chapter 7. Part II of this book considers the properties of estimators based on incorrectly specified models. Chapter 8 develops robust sample designs that lead to unbiased predictors under model misspecification, and shows how flexible modelling methods like non-parametric regression can be used in survey sampling. Chapter 9 extends this development to misspecfication robust prediction variance estimators and Chapter 10 completes Part II of the book with an exploration of outlier robust sample survey estimation. Chapters 11 to 17 constitute Part III of the book and show how model-based methods can be used in a variety of problem areas of modern survey sampling. They cover (in order) prediction of non-linear population quantities, sub-sampling approaches to prediction variance estimation, design and estimation for multipurpose surveys, prediction for domains, small area estimation, efficient prediction of population distribution functions and the use of transformations in survey inference. The book is designed to be accessible to undergraduate and graduate level students with a good grounding in statistics and applied survey statisticians seeking an introduction to model-based survey design and estimation.Less
This book is an introduction to the model-based approach to survey sampling. It consists of three parts, with Part I focusing on estimation of population totals. Chapters 1 and 2 introduce survey sampling, and the model-based approach, respectively. Chapter 3 considers the simplest possible model, the homogenous population model, which is then extended to stratified populations in Chapter 4. Chapter 5 discusses simple linear regression models for populations, and Chapter 6 considers clustered populations. The general linear population model is then used to integrate these results in Chapter 7. Part II of this book considers the properties of estimators based on incorrectly specified models. Chapter 8 develops robust sample designs that lead to unbiased predictors under model misspecification, and shows how flexible modelling methods like non-parametric regression can be used in survey sampling. Chapter 9 extends this development to misspecfication robust prediction variance estimators and Chapter 10 completes Part II of the book with an exploration of outlier robust sample survey estimation. Chapters 11 to 17 constitute Part III of the book and show how model-based methods can be used in a variety of problem areas of modern survey sampling. They cover (in order) prediction of non-linear population quantities, sub-sampling approaches to prediction variance estimation, design and estimation for multipurpose surveys, prediction for domains, small area estimation, efficient prediction of population distribution functions and the use of transformations in survey inference. The book is designed to be accessible to undergraduate and graduate level students with a good grounding in statistics and applied survey statisticians seeking an introduction to model-based survey design and estimation.
Thomas J. Stohlgren
- Published in print:
- 2006
- Published Online:
- September 2007
- ISBN:
- 9780195172331
- eISBN:
- 9780199790395
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195172331.001.0001
- Subject:
- Biology, Plant Sciences and Forestry
This book provides sampling designs for measuring species richness and diversity, patterns of plant diversity, species-environment relationships, and species distributions in complex landscapes and ...
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This book provides sampling designs for measuring species richness and diversity, patterns of plant diversity, species-environment relationships, and species distributions in complex landscapes and natural ecosystems. Part I introduces the problem: plant diversity studies are difficult to design and conduct in part because of the history and baggage associated with the evolution of plant ecology into a quantitative science. Issues of scale, resolution, and extent must be effectively commandeered. Part II implores the practitioner to take an experimental approach to sampling plant diversity with a clear understanding of advantages and disadvantages of single-scale and multi-scale techniques. Part III focuses on scaling plant diversity measurements from plots to landscapes. Part IV provides a brief introduction to modeling plant diversity in relation to environmental factors. Examples of common non-spatial (correlative) and spatial analyses are explained. Part V introduces the concept of measuring temporal changes in plant diversity at landscape scales and follows with a case study designed to collect the necessary baseline data to monitor plant diversity. Part VI discusses research needed to understand better changes in plant diversity in space and time. Specific objectives are to: (1) provide a basic understanding of the history of design considerations in past and modern vegetation field studies; (2) demonstrate with real-life case studies the use of single-scale and multi-scale sampling methods, and statistical and spatial analysis techniques that may be particularly helpful in measuring plant diversity at landscape scales; and (3) address several sampling questions typically asked by students and field ecologists.Less
This book provides sampling designs for measuring species richness and diversity, patterns of plant diversity, species-environment relationships, and species distributions in complex landscapes and natural ecosystems. Part I introduces the problem: plant diversity studies are difficult to design and conduct in part because of the history and baggage associated with the evolution of plant ecology into a quantitative science. Issues of scale, resolution, and extent must be effectively commandeered. Part II implores the practitioner to take an experimental approach to sampling plant diversity with a clear understanding of advantages and disadvantages of single-scale and multi-scale techniques. Part III focuses on scaling plant diversity measurements from plots to landscapes. Part IV provides a brief introduction to modeling plant diversity in relation to environmental factors. Examples of common non-spatial (correlative) and spatial analyses are explained. Part V introduces the concept of measuring temporal changes in plant diversity at landscape scales and follows with a case study designed to collect the necessary baseline data to monitor plant diversity. Part VI discusses research needed to understand better changes in plant diversity in space and time. Specific objectives are to: (1) provide a basic understanding of the history of design considerations in past and modern vegetation field studies; (2) demonstrate with real-life case studies the use of single-scale and multi-scale sampling methods, and statistical and spatial analysis techniques that may be particularly helpful in measuring plant diversity at landscape scales; and (3) address several sampling questions typically asked by students and field ecologists.
Patrick Dattalo
- Published in print:
- 2009
- Published Online:
- February 2010
- ISBN:
- 9780195378351
- eISBN:
- 9780199864645
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195378351.003.0002
- Subject:
- Social Work, Research and Evaluation
This chapter begins with a discussion of external validity and sampling bias. Next, the rationale and limitations of RS as a way to maximize external validity and minimize sampling bias are ...
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This chapter begins with a discussion of external validity and sampling bias. Next, the rationale and limitations of RS as a way to maximize external validity and minimize sampling bias are presented. Then, the following alternatives and supplements to RS will be presented in terms of their assumptions, implementations, strengths, and weaknesses: (1) deliberate sampling for diversity and typicalness; and (2) sequential sampling. Deliberate sampling for diversity and typicalness and sequential sampling are methodological alternatives for RS.Less
This chapter begins with a discussion of external validity and sampling bias. Next, the rationale and limitations of RS as a way to maximize external validity and minimize sampling bias are presented. Then, the following alternatives and supplements to RS will be presented in terms of their assumptions, implementations, strengths, and weaknesses: (1) deliberate sampling for diversity and typicalness; and (2) sequential sampling. Deliberate sampling for diversity and typicalness and sequential sampling are methodological alternatives for RS.
Peter Lyons and Howard J. Doueck
- Published in print:
- 2009
- Published Online:
- February 2010
- ISBN:
- 9780195373912
- eISBN:
- 9780199865604
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195373912.003.0005
- Subject:
- Social Work, Research and Evaluation
This chapter examines participant selection, sampling design, sample size and sampling error; as well as the importance of statistical power, effect size, confidence levels, and confidence intervals. ...
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This chapter examines participant selection, sampling design, sample size and sampling error; as well as the importance of statistical power, effect size, confidence levels, and confidence intervals. Types of sampling, including probability and nonprobability sampling methods, are discussed in relation to both quantitative and qualitative research designs. The measurement properties of instruments including requirements of validity and reliability as well as issues in measurement with human measures (credibility, inquiry audits, and triangulation) are presented.Less
This chapter examines participant selection, sampling design, sample size and sampling error; as well as the importance of statistical power, effect size, confidence levels, and confidence intervals. Types of sampling, including probability and nonprobability sampling methods, are discussed in relation to both quantitative and qualitative research designs. The measurement properties of instruments including requirements of validity and reliability as well as issues in measurement with human measures (credibility, inquiry audits, and triangulation) are presented.
Kyeongae Choe, William R. Parke, and Dale Whittington
- Published in print:
- 2001
- Published Online:
- November 2003
- ISBN:
- 9780199248919
- eISBN:
- 9780191595950
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/0199248915.003.0010
- Subject:
- Economics and Finance, Development, Growth, and Environmental
In many settings, a random sample may not be cost‐inefficient, so that a two‐stage stratified random sample is adopted in contingent valuation (CV) studies. The first stage comprises identifying a ...
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In many settings, a random sample may not be cost‐inefficient, so that a two‐stage stratified random sample is adopted in contingent valuation (CV) studies. The first stage comprises identifying a number of enumeration areas from which to sample households in the second stage. The Monte Carlo simulation, from a CV case study, suggests that increasing the second‐stage sample size within a limited number of enumeration areas selected at the first stage of sampling will result in a greater return in statistical and sampling design efficiency than will increasing the number of enumeration areas with a limited second‐stage sampling size. In both approaches, the marginal return diminishes as the number of sampling units increases.Less
In many settings, a random sample may not be cost‐inefficient, so that a two‐stage stratified random sample is adopted in contingent valuation (CV) studies. The first stage comprises identifying a number of enumeration areas from which to sample households in the second stage. The Monte Carlo simulation, from a CV case study, suggests that increasing the second‐stage sample size within a limited number of enumeration areas selected at the first stage of sampling will result in a greater return in statistical and sampling design efficiency than will increasing the number of enumeration areas with a limited second‐stage sampling size. In both approaches, the marginal return diminishes as the number of sampling units increases.
Curtis L. Meinert and Susan Tonascia
- Published in print:
- 1986
- Published Online:
- September 2009
- ISBN:
- 9780195035681
- eISBN:
- 9780199864478
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195035681.003.0009
- Subject:
- Public Health and Epidemiology, Public Health, Epidemiology
This chapter discusses the role of sample size and power estimates in planning a trial. It details the methods for making such calculations in trials involving fixed sample designs.
This chapter discusses the role of sample size and power estimates in planning a trial. It details the methods for making such calculations in trials involving fixed sample designs.
Raymond L. Chambers and Robert G. Clark
- Published in print:
- 2012
- Published Online:
- May 2012
- ISBN:
- 9780198566625
- eISBN:
- 9780191738449
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198566625.003.0005
- Subject:
- Mathematics, Probability / Statistics
When there is a single continuous auxiliary variable, it is often reasonable to assume a simple linear regression model relating this variable to the variable of interest. This chapter describes the ...
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When there is a single continuous auxiliary variable, it is often reasonable to assume a simple linear regression model relating this variable to the variable of interest. This chapter describes the use of regression population models in sample surveys. Proportional models, where the intercept is assumed to be zero, have a long history in survey sampling and are discussed first. Empirical best and best linear unbiased predictors are derived. The ratio model is a special case of the proportional model, and this leads to the well known ratio estimator. Models with intercepts are then discussed, including best estimators of totals. Sample designs are developed. Under the ratio model, the optimal design is to select only the units with the largest values of the auxiliary variable. However this would not be robust to departures from the ratio model. The problem of robust design is discussed in Chapter 8. Optimal design is also discussed for the linear model with intercept. The combination of regression and stratification is discussed. It is possible to assume the same regression or ratio relationship in every stratum, or to allow different coefficients in each stratum. Data from an agriculture survey are used to illustrate this choice.Less
When there is a single continuous auxiliary variable, it is often reasonable to assume a simple linear regression model relating this variable to the variable of interest. This chapter describes the use of regression population models in sample surveys. Proportional models, where the intercept is assumed to be zero, have a long history in survey sampling and are discussed first. Empirical best and best linear unbiased predictors are derived. The ratio model is a special case of the proportional model, and this leads to the well known ratio estimator. Models with intercepts are then discussed, including best estimators of totals. Sample designs are developed. Under the ratio model, the optimal design is to select only the units with the largest values of the auxiliary variable. However this would not be robust to departures from the ratio model. The problem of robust design is discussed in Chapter 8. Optimal design is also discussed for the linear model with intercept. The combination of regression and stratification is discussed. It is possible to assume the same regression or ratio relationship in every stratum, or to allow different coefficients in each stratum. Data from an agriculture survey are used to illustrate this choice.
Luigi Boitani, Paolo Ciucci, and Alessio Mortelliti
- Published in print:
- 2012
- Published Online:
- December 2013
- ISBN:
- 9780199558520
- eISBN:
- 9780191774546
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199558520.003.0002
- Subject:
- Biology, Biodiversity / Conservation Biology, Ecology
This chapter examines the challenges associated with planning and implementing a field survey on carnivores. Following the definition of the term ‘survey’, the main issues associated with the several ...
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This chapter examines the challenges associated with planning and implementing a field survey on carnivores. Following the definition of the term ‘survey’, the main issues associated with the several phases of a carnivore survey, such as the planning phase, the establishment of goals and objectives, and the optimal allocation of sampling effort, are discussed. Throughout the chapter, the focus is on the most relevant aspects of studying carnivores in the field, such as dealing with false absences through occupancy modelling, defining the target population and spatial extent of the survey, and implementing a probabilistic sampling scheme under the constraints of field methods commonly used to study carnivores.Less
This chapter examines the challenges associated with planning and implementing a field survey on carnivores. Following the definition of the term ‘survey’, the main issues associated with the several phases of a carnivore survey, such as the planning phase, the establishment of goals and objectives, and the optimal allocation of sampling effort, are discussed. Throughout the chapter, the focus is on the most relevant aspects of studying carnivores in the field, such as dealing with false absences through occupancy modelling, defining the target population and spatial extent of the survey, and implementing a probabilistic sampling scheme under the constraints of field methods commonly used to study carnivores.
Raymond L. Chambers and Robert G. Clark
- Published in print:
- 2012
- Published Online:
- May 2012
- ISBN:
- 9780198566625
- eISBN:
- 9780191738449
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198566625.003.0007
- Subject:
- Mathematics, Probability / Statistics
All of the models discussed in chapters 3 through 6 are special cases of the correlated general linear population model. This chapter describes the general linear population model, and the correlated ...
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All of the models discussed in chapters 3 through 6 are special cases of the correlated general linear population model. This chapter describes the general linear population model, and the correlated general linear population model. These models allow for very general relationships between the variable of interest and a set of auxiliary variables, where auxiliary variables can consist of an arbitrary number of continuous and categorical variables. Empirical best and best linear unbiased predictors are described, and the predictors from earlier chapters are shown to be special cases. Questions of model choice and efficient sample design are also considered.Less
All of the models discussed in chapters 3 through 6 are special cases of the correlated general linear population model. This chapter describes the general linear population model, and the correlated general linear population model. These models allow for very general relationships between the variable of interest and a set of auxiliary variables, where auxiliary variables can consist of an arbitrary number of continuous and categorical variables. Empirical best and best linear unbiased predictors are described, and the predictors from earlier chapters are shown to be special cases. Questions of model choice and efficient sample design are also considered.
David G. Hankin, Michael S. Mohr, and Ken B. Newman
- Published in print:
- 2019
- Published Online:
- December 2019
- ISBN:
- 9780198815792
- eISBN:
- 9780191853463
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/oso/9780198815792.003.0002
- Subject:
- Biology, Biomathematics / Statistics and Data Analysis / Complexity Studies, Ecology
This chapter provides a conceptual, visual and non-quantitative presentation of the basic principles of sampling theory which are developed in formal quantitative fashion in subsequent chapters. ...
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This chapter provides a conceptual, visual and non-quantitative presentation of the basic principles of sampling theory which are developed in formal quantitative fashion in subsequent chapters. Included are summaries of (a) basic terminology used throughout the text (population, sample, estimator, estimate), (b) components of a sampling strategy (sampling frame, sampling design, estimator), (c) properties of estimators (bias, sampling variance, mean square error), and (d) sampling distribution of an estimator. Simple or familiar settings are used to illustrate the differences between simple frames (listings of population units from which a sample of units is selected) and complex frames (sampling units consist of groupings of population units), and to illustrate the different components of a sampling strategy. A bullseye target with associated dart throws is used to distinguish the important estimator properties of bias, sampling variance, and mean square error. The performances of randomized sampling procedures and purposive or judgment selection of “representative samples” are contrasted using two examples: (1) an historical contrast of estimated abundance of Oregon coastal coho salmon (Oncorhynchus kisutch) based on purposive representative reach surveys and on stratified random surveys, and (2) a repeatable classroom exercise pitting judgment sampling against simple random sampling for estimation of mean weight in a population of agates collected from northern California beaches.Less
This chapter provides a conceptual, visual and non-quantitative presentation of the basic principles of sampling theory which are developed in formal quantitative fashion in subsequent chapters. Included are summaries of (a) basic terminology used throughout the text (population, sample, estimator, estimate), (b) components of a sampling strategy (sampling frame, sampling design, estimator), (c) properties of estimators (bias, sampling variance, mean square error), and (d) sampling distribution of an estimator. Simple or familiar settings are used to illustrate the differences between simple frames (listings of population units from which a sample of units is selected) and complex frames (sampling units consist of groupings of population units), and to illustrate the different components of a sampling strategy. A bullseye target with associated dart throws is used to distinguish the important estimator properties of bias, sampling variance, and mean square error. The performances of randomized sampling procedures and purposive or judgment selection of “representative samples” are contrasted using two examples: (1) an historical contrast of estimated abundance of Oregon coastal coho salmon (Oncorhynchus kisutch) based on purposive representative reach surveys and on stratified random surveys, and (2) a repeatable classroom exercise pitting judgment sampling against simple random sampling for estimation of mean weight in a population of agates collected from northern California beaches.
Masashi Sugiyama and Motoaki Kawanabe
- Published in print:
- 2012
- Published Online:
- September 2013
- ISBN:
- 9780262017091
- eISBN:
- 9780262301220
- Item type:
- chapter
- Publisher:
- The MIT Press
- DOI:
- 10.7551/mitpress/9780262017091.003.0010
- Subject:
- Computer Science, Machine Learning
This chapter describes real-world applications of active learning techniques: sampling policy design in reinforcement learning and wafer alignment in semiconductor exposure apparatus.
This chapter describes real-world applications of active learning techniques: sampling policy design in reinforcement learning and wafer alignment in semiconductor exposure apparatus.
Pierre Taberlet, Aurélie Bonin, Lucie Zinger, and Eric Coissac
- Published in print:
- 2018
- Published Online:
- March 2018
- ISBN:
- 9780198767220
- eISBN:
- 9780191821387
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/oso/9780198767220.001.0001
- Subject:
- Biology, Biodiversity / Conservation Biology, Evolutionary Biology / Genetics
Environmental DNA (eDNA), i.e. DNA released in the environment by any living form, represents a formidable opportunity to gather high-throughput and standard information on the distribution or ...
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Environmental DNA (eDNA), i.e. DNA released in the environment by any living form, represents a formidable opportunity to gather high-throughput and standard information on the distribution or feeding habits of species. It has therefore great potential for applications in ecology and biodiversity management. However, this research field is fast-moving, involves different areas of expertise and currently lacks standard approaches, which calls for an up-to-date and comprehensive synthesis. Environmental DNA for biodiversity research and monitoring covers current methods based on eDNA, with a particular focus on “eDNA metabarcoding”. Intended for scientists and managers, it provides the background information to allow the design of sound experiments. It revisits all steps necessary to produce high-quality metabarcoding data such as sampling, metabarcode design, optimization of PCR and sequencing protocols, as well as analysis of large sequencing datasets. All these different steps are presented by discussing the potential and current challenges of eDNA-based approaches to infer parameters on biodiversity or ecological processes. The last chapters of this book review how DNA metabarcoding has been used so far to unravel novel patterns of diversity in space and time, to detect particular species, and to answer new ecological questions in various ecosystems and for various organisms. Environmental DNA for biodiversity research and monitoring constitutes an essential reading for all graduate students, researchers and practitioners who do not have a strong background in molecular genetics and who are willing to use eDNA approaches in ecology and biomonitoring.Less
Environmental DNA (eDNA), i.e. DNA released in the environment by any living form, represents a formidable opportunity to gather high-throughput and standard information on the distribution or feeding habits of species. It has therefore great potential for applications in ecology and biodiversity management. However, this research field is fast-moving, involves different areas of expertise and currently lacks standard approaches, which calls for an up-to-date and comprehensive synthesis. Environmental DNA for biodiversity research and monitoring covers current methods based on eDNA, with a particular focus on “eDNA metabarcoding”. Intended for scientists and managers, it provides the background information to allow the design of sound experiments. It revisits all steps necessary to produce high-quality metabarcoding data such as sampling, metabarcode design, optimization of PCR and sequencing protocols, as well as analysis of large sequencing datasets. All these different steps are presented by discussing the potential and current challenges of eDNA-based approaches to infer parameters on biodiversity or ecological processes. The last chapters of this book review how DNA metabarcoding has been used so far to unravel novel patterns of diversity in space and time, to detect particular species, and to answer new ecological questions in various ecosystems and for various organisms. Environmental DNA for biodiversity research and monitoring constitutes an essential reading for all graduate students, researchers and practitioners who do not have a strong background in molecular genetics and who are willing to use eDNA approaches in ecology and biomonitoring.
Shylashri Shankar and Raghav Gaiha
- Published in print:
- 2013
- Published Online:
- September 2013
- ISBN:
- 9780198085003
- eISBN:
- 9780199082476
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198085003.003.0002
- Subject:
- Political Science, Indian Politics
It first discusses NREGA’s goals and the reviews of the scheme by policy makers and scholars. NREGA is a targeted program where instead of relying on an administrator to choose the beneficiaries, ...
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It first discusses NREGA’s goals and the reviews of the scheme by policy makers and scholars. NREGA is a targeted program where instead of relying on an administrator to choose the beneficiaries, NREGA expects beneficiaries to select themselves by creating incentives so that only the poor will participate in the scheme. In practice, however, the self- selection mechanism has been weakened in areas where NREGA wage was higher than the prevailing market wage, and by the low awareness among the beneficiaries of the scheme’s components. It then discusses the sample design and data collection The analysis draws upon five datasets—household (and a subset), ethnographic, panel, worksite, and individual beneficiary scheme—designed and collected by the authors from Rajasthan, Andhra Pradesh (AP), Madhya Pradesh (MP), and Tamil Nadu (TN). These four were chosen to ensure geographic and economic diversity.Less
It first discusses NREGA’s goals and the reviews of the scheme by policy makers and scholars. NREGA is a targeted program where instead of relying on an administrator to choose the beneficiaries, NREGA expects beneficiaries to select themselves by creating incentives so that only the poor will participate in the scheme. In practice, however, the self- selection mechanism has been weakened in areas where NREGA wage was higher than the prevailing market wage, and by the low awareness among the beneficiaries of the scheme’s components. It then discusses the sample design and data collection The analysis draws upon five datasets—household (and a subset), ethnographic, panel, worksite, and individual beneficiary scheme—designed and collected by the authors from Rajasthan, Andhra Pradesh (AP), Madhya Pradesh (MP), and Tamil Nadu (TN). These four were chosen to ensure geographic and economic diversity.
Gordon A. Fox, Simoneta Negrete-Yankelevich, and Vinicio J. Sosa (eds)
- Published in print:
- 2015
- Published Online:
- April 2015
- ISBN:
- 9780199672547
- eISBN:
- 9780191796487
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199672547.001.0001
- Subject:
- Biology, Biomathematics / Statistics and Data Analysis / Complexity Studies, Ecology
This book discusses the change in use of statistics in ecology—especially the increased use (over the last two decades) of more sophisticated statistical and computational methods. This book also ...
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This book discusses the change in use of statistics in ecology—especially the increased use (over the last two decades) of more sophisticated statistical and computational methods. This book also highlights the contribution of statistical modeling to knowledge acquisition as an important way of abstracting ecological questions into mathematical models, and its role in the research cycle currently used by most ecologists. The book reviews briefly the logic and key parts of statistical linear models, as they form the conceptual foundation of most of the methods discussed in the book. Finally, it explains the book’s organization, the background required for readers, and strategies for getting the most out of this intermediate-level book.Less
This book discusses the change in use of statistics in ecology—especially the increased use (over the last two decades) of more sophisticated statistical and computational methods. This book also highlights the contribution of statistical modeling to knowledge acquisition as an important way of abstracting ecological questions into mathematical models, and its role in the research cycle currently used by most ecologists. The book reviews briefly the logic and key parts of statistical linear models, as they form the conceptual foundation of most of the methods discussed in the book. Finally, it explains the book’s organization, the background required for readers, and strategies for getting the most out of this intermediate-level book.
