The Premixing method: prinicple, design, and construction

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This course is designed for those with a specific research question in mind — participants will be asked to design multi-method approaches to address a research question of their choice. By the end of this course, participants will have an overview of multi-method research that will enable them to design, understand, and evaluate multi-method approaches within a single project.

This intensive course will offer an introduction to history calendar data collection methods. The course will consider both the context of longitudinal research designs and pure retrospective designs. Methods will include approaches for the collection of dynamic measures of community, household and individual-level change over time. This course begins with an overview of principals of history calendar methods, integration of structured and semi structured methods of data collection techniques and types of data generated through the calendar methods.

In this course we will illustrate principals of calendar method with actual examples. The course focuses on how to develop and implement calendars as data collection tools and encourages participants to explore critical issues concerning the applications of these methods. Standardized multi-item scales are more common in some disciplines than others. It covers the psychometric principles of question development while adding in principles of general questionnaire design. Focusing first on Classical Measurement Theory, participants design their own multi-item scales.

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This is followed by a group discussion of existing multi-item scales. The course then introduces some basic statistical tools for assessing the reliability and dimensionality of multi-item scales and participants get to practice evaluating some existing scales in a computer lab session. The course finishes with an introduction to Item Response Theory. Prerequisite: There is no prerequisite, but a little knowledge about questionnaire design, multi-item scales and SPSS would be of value.

Probability and Non-probability Sampling Methods is a sampling course that differs from traditional sampling classes. First, this class gives an equal amount of attention to both probability and non-probability sampling methods as non-probability sampling cannot be discussed meaningfully without understanding probability sampling and these two methods offer distinctive advantages and disadvantages.

The course will start with examining probability sampling techniques and their properties, including simple random selection, systematic selection, cluster sampling, stratified sampling, and probability proportionate to size selection. Issues of weighting to compensate for unequal chances of selection and variance estimation for calculating confidence intervals are also examined. Then the wide variety of non-probability sampling methods are examined, from panel-based convenience samples, to river samples, quota samples, respondent-driven samples, and other techniques.

The properties of these samples are discussed, and assumptions needed to obtain estimates are examined. We will also examine these two approaches from the total survey error perspectives.

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The lab sessions to be held after each class will combine R programming and group discussions on the topics that need to be considered when implementing various sampling approaches. Hands-on examples of frame preparation, sample draws, post-survey adjustments and analysis specific to design will be provided and discussed. The course is not designed to provide the mastery of survey sampling. Rather, it provides materials that will accommodate participants to become familiar with advantages and disadvantages of the two methods and their implementation which will allow them to make informed design decisions.

This course will focus on semi-structured, or in-depth, interviewing. This methodology is often most helpful in understanding complex social processes. The course will examine the goals, assumptions, process, and uses of interviewing and compare these methods to other related qualitative and quantitative methods in order to develop research designs appropriate to research goals. The course will cover all aspects of interviewing, including how to decide who to interview, how to ask good interview questions, and how to conduct successful interviews.

Students will conduct interviews, and discuss the process and outcome of those interviews. We will examine the strengths and weaknesses of this methodology, particularly through discussion of some of the critiques of these methods. Below is the list of the individual courses offered as part of the RSD Program. RSD has financial support available to those who qualify. Responsive survey design RSD refers to a method for designing surveys that has been demonstrated to increase the quality and efficiency of survey data collection.

RSD uses evidence from early phases of data collection to make design decisions for later phases. Beginning in the Summer Institute, we will offer a series of eleven one-day short courses in RSD techniques. Survey Methodology for Randomized Controlled Trails does not have the remote participation option. Topics covered: Randomized Controlled Trials RCTs are an important tool for tests of internal validity of causal claims in both health and social sciences. In practice, however, inattention to crucial details of data collection methodology can compromise the internal validity test.

One crucial example is recruitment and retention of participants — though randomized to treatment, unequal reluctance to participate or unequal attrition from the RCT jeopardize the internal validity of comparisons within the RCT design. Another crucial example is the interaction of treatment and measurement — if the measures themselves change in response to the RCT treatment, then observed treatment and control differences may reflect these measurement differences rather than treatment differences.

In both cases, specific tools from survey methodology can be used to maximize the internal validity test in the RCT design. This course will focus on the survey methodology topics most important for maintaining the internal validity of RCT studies and feature specific examples of applications to RCTs.

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One set of tools will focus on maximizing participation and minimizing attrition of participants. Core survey methodology tools for encouraging participation in both pre-treatment measurement and the treatment itself as well as tools for minimizing the loss of participants to follow-up measures will be featured. These tools include incentives, tailoring refusal conversion, switching modes, and tracking strategies.

Links to RSD will also be made. A second set of tools will focus on measurement construction to reduce chances of interaction with treatment. These tools include mode options, questionnaire design issues, and special instruments such as life history calendars to minimize reporting error. Each portion of the course will feature examples applying each specific tool to RCT studies. This course will provide participants with an overview of the primary concepts underlying RSD. This will include discussion of the uncertainty in survey design, the role of paradata , or data describing the data collection process, in informing decisions, and potential RSD interventions.

These interventions include timing and sequence of modes, techniques for efficiently deploying incentives, and combining two-phase sampling with other design changes. Interventions appropriate for face-to-face, telephone, web, mail and mixed-mode surveys will be discussed. Using the Total Survey Error TSE framework, the main concepts behind these designs will be explained with a focus on how these principles are designed to simultaneously control survey errors and survey costs. Examples of RSD in both large and small studies will be provided as motivation.

Small group exercises will help participants to think through some of the common questions that need to be answered when employing RSD. This course will explore several well-developed examples of RSD. The instructors will then provide independent examples of the implementation of RSD in different international surveys. All case studies will be supplemented with discussions of issues regarding the development and implementation of RSD. This variety of case studies will reflect a diversity of survey conditions. The NSFG West is a cross-sectional survey that is run on a continuous basis with in-person interviewing.

The RDSL Axinn is a panel survey that employed a mixed-mode approach to collecting weekly journal data from a panel of young women. The UMCC survey is a web survey of students at UM that employed multiple modes of contact across the phases of the design. The Netherlands Survey of Consumer Satisfaction Schouten is a mixed-mode survey combining web and mail survey data collection with telephone interviewing. The focus of the course will be on practical tools for implementing RSD in a variety of conditions, including small-scale surveys. Topics covered: Web surveys can be an inexpensive method for collecting data.

This is especially true for designs that repeat measurement over several time periods. However, these relatively low-cost data collections may result in reduced data quality if the problem of nonresponse is ignored. This course will examine methods for using RSD to effectively deploy scarce resources in order to minimize the risk of nonresponse bias.

These surveys are defined by phased designs and multiple modes of contact. This approach produced relatively high response rates and used alternative contact methods in later phases to recruit sample members from subgroups that were less likely to respond in earlier phases. In the case of the UM-CCS all of this was accomplished on a very small budget and with a small management team. Lessons from these experiences can be directly applied in many similar settings.

We will begin with a detailed discussion of how to design dashboards from an RSD perspective. This will include concrete discussions of how relevant data may be collected and summarized across a variety of production environments. We will also discuss how these dashboards can be used to implement RSD interventions on an ongoing basis. We will demonstrate these points using examples from actual dashboards. We will briefly explore methods for modeling incoming paradata in order to detect outliers.

In the afternoon, we will consider practical issues associated with the development of dashboards, including software alternatives. Finally, we will demonstrate how to update dashboards using data reflecting the results of ongoing fieldwork. Students will be provided with template spreadsheet dashboards as discussed earlier. Instructor: Barry Schouten and Natalie Shlomo. Topics covered: The response rate has been shown to be a poor indicator for data quality with respect to nonresponse bias.

Several alternatives have been proposed — the fraction of missing information FMI , R-Indicators, subgroup response rates, etc. This course will explore the use of these indicators as guides for data collection when working within an RSD framework. We also explore optimization techniques that may be useful when designing a survey to maximize these alternative indicators. The consequences of optimizing a survey to other indicators will be explored. We will also consider how the response rate fits into this approach.

We will end with a brief discussion of methods for post data collection evaluation of data quality. This course will cover issues associated with implementation of RSD to manage field work. Instructors will provide concrete instruction on active monitoring of key indicators across a variety of environments — small-scale surveys, large-scale surveys, and web, telephone, face-to-face and mixed-mode surveys.

Methods for implementing RSD interventions in a diversity of production environments will be discussed. RSD will be presented within the framework of the principles of project management, with a particular focus on risk management. A checklist of steps for implementing RSD will be discussed in detail. This course will draw upon a semester-long graduate course in survey management, which includes sections on RSD.


Topics covered: The effective treatment and management of a wide variety of health disorders often requires individualized, sequential decision making whereby treatment is adapted over time based on the changing disease state or specific circumstances of the patient. There has been a huge surge of scientific interest in constructing adaptive interventions via the sequential multiple assignment randomized trial SMART design. SMART is a type of multi-stage randomized trial design, developed specifically for the purpose of collecting high-quality data for building optimal adaptive interventions.

SMARTs are still new to the great majority of behavioral and social science investigators. In this course, we will introduce adaptive interventions, SMART including simple design principle, cutting-edge analytic methods e. Topics covered: This course will discuss a variety of potential RSD interventions. Many of these have been implemented experimentally, and the course will include evaluations of those experiments. The importance of experimental evaluations in early phases of RSD will be discussed. Methods for implementing interventions will also be discussed, including implementation of experiments aimed at evaluating new interventions.

Strategies for implementing these interventions with both interviewer-mediated and self-administered e.

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Methods for the evaluation of the results of the interventions experimental and otherwise will be considered. These evaluations will include measures of both costs and errors. Testing your survey questionnaire is absolutely essential for ensuring a high quality survey. There has been a proliferation of question testing methods both new methods and variations of existing methods. T his course covers 20 different question testing techniques and combines information from the survey methodological literature with practical advice and hands on practice.

The course covers methods for testing quantitative interview-based, web and mail questionnaires looking both at standard methods as well as new and unusual techniques. Instructors: Mick Couper , University of Michigan. The course focuses on the design of Web survey instruments and procedures. The course covers all aspects of instrument design for Web surveys, including the appropriate use of various input tools e. The course draws on empirical results from experiments on alternative design approaches as well as practical experience in the design and implementation of a wide variety of Web surveys.

The course also covers the challenges of designing Web surveys for mobile devices smartphones and tablets , and the collection and use of paradata to evaluate Web survey instruments. Microstructural studies were also performed to understand the reactions of the mixtures.

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The results showed that the compressive behavior of CSM panel changes significantly by increasing the mixing depth. Moreover the influence of curing pressure equivalent to the depth of CSM panels on increasing unconfined compressive strength of specimens is partially more than the effect of additives and considerably more than the effect of curing time. The microstructural studies also showed that the main hydration products are cementitious products such as calcium silicate hydrates C-S-H and the addition of alkali-activated slag in a treated clay results in production of the cementitious products in a faster rate.

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Advertisement Hide. Geotechnical Engineering First Online: 28 August This is a preview of subscription content, log in to check access. Arslan, H. CrossRef Google Scholar. Arulrajah, A. ASTM Google Scholar. Bakharev, T. Process description manual: Cutter soil mixing process and equipment , No. Bellato, D. Benhelal, E. Bhadriraju, V. Bjergea, L. Bruce, D. Bruce, M.

The Premixing Method: Principle, Design and Construction - CRC Press Book

Federal highway administration design manual: Deep mixing for embankment and foundation support , No. Brunner, W. Building Research Establishment Watford, UK, p. CDIT The deep mixing method: Principle, design and construction 1st Ed. Chai, J. Deformation analysis in soft ground improvement , Springer, Heidelberg, Germany p. Chaparro, W. Chen, W. Consoli, N. Correia, A. Denies, N. Faraone, N. Farouk, A. Frikhan, W. Gerressen, F. Ghiabi, H. Characteristics and uses of steel slag in building construction , Elsevier, Amsterdam, Netherlands, p.

Han, J. Imbabi, M. Indraratna, B. Ground improvement case histories chemical, electrokinetic, thermal and bioengineering , Elsevier, Amsterdam, Netherlands, p.

The Premixing method: prinicple, design, and construction The Premixing method: prinicple, design, and construction
The Premixing method: prinicple, design, and construction The Premixing method: prinicple, design, and construction
The Premixing method: prinicple, design, and construction The Premixing method: prinicple, design, and construction
The Premixing method: prinicple, design, and construction The Premixing method: prinicple, design, and construction
The Premixing method: prinicple, design, and construction The Premixing method: prinicple, design, and construction
The Premixing method: prinicple, design, and construction The Premixing method: prinicple, design, and construction

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