Using Sequential Mixed Social Science Methods to Define and Measure Heritage Conservation Performance

There is no agreed-upon definition for heritage conservation performance, but it is possible to borrow ideas from the natural resource conservation field to inform this concept. Dimensions of performance can include economic, technical, and sociocultural and experiential indices. Because heritage conservation ostensibly benefits people as its primary goal, however, the values of most stakeholders ought to play a role in defining performance. Most of these values are subjective and represent sociocultural and personal meanings and tend to differ dramatically from the positivistic, fabric-centered value system of conservation experts. Measurement implies quantification, yet many sociocultural values are based on qualitative meanings that defy direct attempts at quantification. One solution for this predicament is to employ a sequential mixed-method approach where qualitative meanings are gathered from stakeholders and then these meanings are used to inform the development of a quantitative method, such as a survey instrument. In this way, while the qualitative meanings are not being directly “measured” as such, aspects of the phenomenon behind these meanings can be measured, quantified, and subjected to statistical techniques. A brief representative case study is presented as an example of how social science methodologies can help define and measure performance

Mixed methods - theory and practice. Sequential, explanatory approach

There appears to be a paucity of research undertaken in emotional intelligence in higher education suggesting a gap in which research can be undertaken that can provide new insight and add together with knowledge and understanding. This article discusses a study using sequential, explanatory, mixed methodology, which was undertaken on a sample of 533 academics (those employed by a university full time, part time, and hourly and who may be lecturers, tutors, instructors, researchers). The reason for collecting sequential quantitative and qualitative data into one study brings together two types of information providing greater understanding and insight into the research topics that may not have been obtained analysing and evaluating data separately. The findings from interviews helps explain the findings from quantitative data

An analysis of an industry sponsored governance development intervention : the case of Dairy NZ's Mark and Measure programme : a thesis presented in partial fulfilment of the requirements for the degree of Master of Business Studies in Management at Massey University, Palmerston North, New Zealand

Appendices 6 & 7 removed due to copyright reasonsA plethora of ‘good’ corporate governance prescriptions are ascribed to boards. Yet there is little research on the influence of corporate governance prescriptions on businesses. This research aims to investigate the impact of the DairyNZ Mark and Measure Farm Business Governance Programme (the Mark and Measure course). This research uses Kirkpatrick’s (1958) Four Level Model to evaluate the Mark and Measure course. The author employs an embedded case study with a sequential mixed methods design to examine the Mark and Measure course. The sequential mixed methods design included a document analysis, a quantitative comparative pre and post intervention analysis, and five qualitative semi-structured interviews. The participants in the research were made up of closely-held SME’s and family businesses. The results show inconsistencies in the conceptualisation of corporate governance in academia, the Mark and Measure course, and the responses of the interviewed participants. There was an increase in most of the four levels in Kirkpatrick’s model. However, caution needs to be applied to results as there are methodological issues with the employed survey instrument

Health concerns of Iranian adolescents: Protocol for a mixed methods study

Background: Adolescents have particular health and developmental needs that suggest they should neither be treated as older children nor younger adults. Objectives: The aim of this paper is to report the protocol for a mixed methods study that set out to investigate the health concerns of Iranian adolescents and their sources of health information with the goal of identifying suitable strategies to address their health concerns. Materials and Methods: This mixed methods study consists of an explanatory sequential design to be conducted in two phases. The first phase was a population-based cross-sectional survey of 915, 14-18 year old adolescents who were selected by stratified cluster random sampling method from the 22 main municipal sectors of Tehran, Iran. They completed a series of self-administered questionnaires which were analyzed using quantitative approaches. The second phase was a qualitative study in which adolescents were selected using purposeful sampling for individual in-depth semi-structured interviews on the basis of the quantitative findings from the first phase. These data, together with a literature review and data obtained through nominal group technique, would then be used to in the development of strategies to reduce adolescents' health concerns. Results: The findings of this mixed methods sequential explanatory study are expected to provide unique information about the health concerns of Iranian adolescents and their sources of information, which to date have received little attention. Conclusions: These data will provide a rich source of information that can be used by intervention programs, health professionals and policy makers in addressing the health concerns of adolescents, with the goal of facilitating a successful passage to adult life. © 2014, Iranian Red Crescent Medical Journal

A Sequential Procedure for Testing Unit Roots in the Presence of Structural Break in Time Series Data: An Application to Quarterly Data of Nepal, 1970-2003

Testing for unit roots has special significance in terms of both economic theory and the interpretation of estimation results. as there are several methods available, researchers face method selection problem while conducting the unit root test on time series data in the presence of structural break. this paper proposes a sequential search procedure to determine the best test method for each time series. different test methods or models may be appropriate for different time series. therefore, instead of sticking to one particular test method for all the time series under consideration, selection of a set of mixed methods is recommended for obtaining better results.time series, stationarity, unit root test, structural break, sequential procedure

Innovation behaviour at farm level: Selection and identification

Using a squential logit model and a mixed-effects logistic regression approach this empirical study investigates factors for the adoption of automatic milking technology (AMS) at the farm level accounting for problems of sequential sample selection and behaviour identification. The results suggest the importance of the farmer’s risk perception, significant effects of peer-group behaviour, and a positive impact of previous innovation experiences.squential logit model, automatic milking technology (AMS), Livestock Production/Industries, Research Methods/ Statistical Methods, Risk and Uncertainty,

Generalized fiducial inference for normal linear mixed models

While linear mixed modeling methods are foundational concepts introduced in any statistical education, adequate general methods for interval estimation involving models with more than a few variance components are lacking, especially in the unbalanced setting. Generalized fiducial inference provides a possible framework that accommodates this absence of methodology. Under the fabric of generalized fiducial inference along with sequential Monte Carlo methods, we present an approach for interval estimation for both balanced and unbalanced Gaussian linear mixed models. We compare the proposed method to classical and Bayesian results in the literature in a simulation study of two-fold nested models and two-factor crossed designs with an interaction term. The proposed method is found to be competitive or better when evaluated based on frequentist criteria of empirical coverage and average length of confidence intervals for small sample sizes. A MATLAB implementation of the proposed algorithm is available from the authors.Comment: Published in at http://dx.doi.org/10.1214/12-AOS1030 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

An Exploratory Sequential Mixed Methods Approach to Understanding Researchers’ Data Management Practices at UVM: Findings from the Qualitative Phase

The objective of this article is to report on the first qualitative phase of an exploratory sequential mixed methods research design focused on researcher data management practices and related institutional research data services. The aim of this study is to understand data management behaviors of faculty at the University of Vermont (UVM), a higher-research activity Research University, in order to guide the development of campus research data management services. The population of study was all faculty who received National Science Foundation (NSF) grants between 2011 and 2014 who were required to submit a data management plan (DMP); qualitative data was collected in two forms: (1) semi-structured interviews and (2) document analysis of data management plans. From a population of 47 researchers, six were included in the interview sample, representing a broad range of disciplines and NSF Directorates, and 35 data management plans were analyzed. Three major themes were identified through triangulation of qualitative data sources: data management activities, including data dissemination and data sharing; institutional research support and infrastructure barriers; and perceptions of data management plans and attitudes towards data management planning. The themes articulated in this article will be used to design a survey for the second quantitative phase of the study, which will aim to more broadly generalize data management activities at UVM across all disciplines

An Exploratory Sequential Mixed Methods Approach to Understanding Researchers’ Data Management Practices at UVM: Findings from the Quantitative Phase

This article reports on the second quantitative phase of an exploratory sequential mixed methods research design focused on researcher data management practices and related institutional support and services. The study aims to understand data management activities and challenges of faculty at the University of Vermont (UVM), a higher research activity Research University, in order to develop appropriate research data services (RDS). Data was collected via a survey, built on themes from the initial qualitative data analysis from the first phase of this study. The survey was distributed to a nonrandom census sample of full-time UVM faculty and researchers (P=1,190); from this population, a total of 319 participants completed the survey for a 26.8% response rate. The survey collected information on five dimensions of data management: data management activities; data management plans; data management challenges; data management support; and attitudes and behaviors towards data management planning. Frequencies, cross tabulations, and chi-square tests of independence were calculated using demographic variables including gender, rank, college, and discipline. Results from the analysis provide a snapshot of research data management activities at UVM, including types of data collected, use of metadata, short- and long-term storage of data, and data sharing practices. The survey identified key challenges to data management, including data description (metadata) and sharing data with others; this latter challenge is particular impacted by confidentiality issues and lack of time, personnel, and infrastructure to make data available. Faculty also provided insight to RDS that they think UVM should support, as well as RDS they were personally interested in. Data from this study will be integrated with data from the first qualitative phase of the research project and analyzed for meta-inferences to help determine future research data services at UVM