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Qualitative Data is meant for the novice researcher who needs guidance on what specifically to do when faced with a sea of information. It takes readers through the qualitative research process, beginning with an examination of the basic philosophy of qualitative research, and ending with planning and carrying out a qualitative research study. It provides an explicit, step-by-step procedure that will take the researcher from the raw text of interview data through data analysis and theory construction to the creation of a publishable work. The volume provides actual examples based on the authors' own work, including two published pieces in the appendix, so that readers can follow examples for each step of the process, from the project's inception to its finished product. The volume also includes an appendix explaining how to implement these data analysis procedures using NVIVO, a qualitative data analysis program.

In the realm of health care, privacy protections are needed to preserve patients' dignity and prevent possible harms. Ten years ago, to address these concerns as well as set guidelines for ethical health research, Congress called for a set of federal standards now known as the HIPAA Privacy Rule. In its 2009 report, Beyond the HIPAA Privacy Rule: Enhancing Privacy, Improving Health Through Research, the Institute of Medicine's Committee on Health Research and the Privacy of Health Information concludes that the HIPAA Privacy Rule does not protect privacy as well as it should, and that it impedes important health research.

The first practical guide to research methods in memory studies. This book provides expert appraisals of a range of techniques and approaches in memory studies, and focuses on methods and methodology as a way to help bring unity and coherence to this new field of study.

We present a procedure that divides a set of experimental units into two groups that are similar on a prespecified set of covariates and are almost as random as with a complete randomization. Under complete randomization, the difference in the standardized average between treatment and control is O p (n -1/2), which may be material in small samples. We present an algorithm that reduces imbalance to O p (n⁻³) for one covariate and O p {n -(1+2/p)} for p covariates, but whose assignments are, strictly speaking, nonrandom. In addition to the metric of maximum eigenvalue of allocation variance, we introduce two metrics that capture departures from randomization and show that our assignments are nearly as random as complete randomization in terms of all measures. Simulations illustrate the results, and inference is discussed. An R package to generate designs according to our algorithm and other popular designs is available.

Research Design and Statistical Analysis provides comprehensive coverage of the design principles and statistical concepts necessary to make sense of real data. The book's goal is to provide a strong conceptual foundation to enable readers to generalize concepts to new research situations. Emphasis is placed on the underlying logic and assumptions of the analysis and what it tells the researcher, the limitations of the analysis, and the consequences of violating assumptions. Sampling, design efficiency, and statistical models are emphasized throughout. As per APA recommendations, emphasis is also placed on data exploration, effect size measures, confidence intervals, and using power analyses to determine sample size. \"\"Real-world\"\" data sets are used to illustrate data exploration, analysis, and interpretation. The book offers a rare blend of the underlying statistical assumptions, the consequences of their violations, and practical advice on dealing with them. Changes in the New Edition: Each section of the book concludes with a chapter that provides an integrated example of how to apply the concepts and procedures covered in the chapters of the section. In addition, the advantages and disadvantages of alternative designs are discussed. A new chapter (1) reviews the major steps in planning and executing a study, and the implications of those decisions for subsequent analyses and interpretations. A new chapter (13) compares experimental designs to reinforce the connection between design and analysis and to help readers achieve the most efficient research study. A new chapter (27) on common errors in data analysis and interpretation. Increased emphasis on power analyses to determine sample size using the G*Power 3 program. Many new data sets and problems. More examples of the use of SPSS (PASW) Version 17, although the analyses exemplified are readily carrie.

A boosted photocatalytic activity was observed for the CdS-AgBr nanocomposite in the degradation of methylene blue (MB). The experimental design method based on the response surface methodology (RSM) approach used to study the simultaneous interaction effects between the influencing variables. Analysis of variance (ANOVA) of the results confirmed a significant model for processing the data because an F value of 32.34 for the suggested model was higher than that of the critical value of F 0.05, 14, 13  = 2.55 at 95% confidence interval. This analysis also showed a non-significant lack of fit (LOF) (as a measure of the randomness of the deviations around the obtained data) because the LOF F value of 8.27 was smaller than that of the critical value of F 0.05, 10, 3  = 8.79. R 2 values near to unity were achieved (the multiple correlation coefficients R 2 ( R 2  = 0.9627), adjusted R 2 (adj- R 2  = 0.9226), and predicted R 2 (pred- R 2  = 0.7423)). Six center points suggested by the model included the following conditions: pH, 6.1; C MB , 3.5 mg/L; a dose of the catalyst, 0.68 g/L; and irradiation time, 40.5 min. During the center point runs, the degradation efficiencies were obtained in the range of 38 to 43%. The optimal run included pH, 9; catalyst dosage, 1 g/L; irradiation time, 60 min; and C MB , 2 mg/L, and the best removal efficiency of 98% was achieved during these conditions.

The small-spotted catshark is one of the most abundant elasmobranchs in the Northeastern Atlantic Ocean. Although its landings are devoted for human consumption, in general this species has low commercial value with high discard rates, reaching 100% in some European fisheries. The reduction of post-harvest losses (discards and by-products) by promotion of a full use of fishing captures is one of the main goals of EU fishing policies. As marine collagens are increasingly used as alternatives to mammalian collagens for cosmetics, tissue engineering, etc., fish skins represent an excellent and abundant source for obtaining this biomolecule. The aim of this study was to analyze the influence of chemical treatment concentration, temperature and time on the extractability of skin collagen from this species. Two experimental designs, one for each of the main stages of the process, were performed by means of Response Surface Methodology (RSM). The combined effect of NaOH concentration, time and temperature on the amount of collagen recovered in the first stage of the collagen extraction procedure was studied. Then, skins treated under optimal NaOH conditions were subjected to a second experimental design, to study the combined effect of AcOH concentration, time and temperature on the collagen recovery by means of yield, amino acid content and SDS-PAGE characterization. Values of independent variables maximizing collagen recovery were 4 °C, 2 h and 0.1 M NaOH (pre-treatment) and 25 °C, 34 h and 1 M AcOH (collagen extraction).

The tools and technique used in the Design of Experiments (DOE) have been proved successful in meeting the challenge of continuous improvement over the last 15 years. However, research has shown that applications of these techniques in small and medium-sized manufacturing companies are limited due to a lack of statistical knowledge required for their effective implementation. Although many books have been written in this subject, they are mainly by statisticians, for statisticians and not appropriate for engineers.Design of Experiments for Engineers and Scientists overcomes the problem of statistics by taking a unique approach using graphical tools. The same outcomes and conclusions are reached as by those using statistical methods and readers will find the concepts in this book both familiar and easy to understand. The book treats Planning, Communication, Engineering, Teamwork and Statistical Skills in separate chapters and then combines these skills through the use of many industrial case studies. Design of Experiments forms part of the suite of tools used in Six Sigma.Key features:* Provides essential DOE techniques for process improvement initiatives* Introduces simple graphical techniques as an alternative to advanced statistical methods - reducing time taken to design and develop prototypes, reducing time to reach the market* Case studies place DOE techniques in the context of different industry sectors* An excellent resource for the Six Sigma training programThis book will be useful to engineers and scientists from all disciplines tackling all kinds of manufacturing, product and process quality problems and will be an ideal resource for students of this topic.Dr Jiju Anthony is Senior Teaching Fellow at the International Manufacturing Unit at Warwick University. He is also a trainer and consultant in DOE and has worked as such for a number of companies including Motorola, Vickers, Procter and Gamble, Nokia, Bosch and a large number of SMEs.

The explanation and implementation of statistical methods for the medical researcher or statistician remains an integral part of modern medical research. This book explains the use of experimental and analytical biostatistics systems. Its accessible style allows it to be used by the non-mathematician as a fundamental component of successful research. Since the third edition, there have been many developments in statistical techniques. The fourth edition provides the medical statistician with an accessible guide to these techniques and to reflect the extent of their usage in medical research. The new edition takes a much more comprehensive approach to its subject. There has been a radical reorganization of the text to improve the continuity and cohesion of the presentation and to extend the scope by covering many new ideas now being introduced into the analysis of medical research data. The authors have tried to maintain the modest level of mathematical exposition that characterized the earlier editions, essentially confining the mathematics to the statement of algebraic formulae rather than pursuing mathematical proofs. Received the Highly Commended Certificate in the Public Health Category of the 2002 BMA Books Competition.

In this paper, microporous Zn‐based zeolitic imidazolate framework with the sodalite cage structure (SOD‐ZIF‐8) was synthesized by the solvothermal method. Powder X‐ray diffraction (PXRD), scanning electron microscopy (SEM) and N2 adsorption were employed to characterize the synthesized material. An ultra‐sensitive electrochemical sensor based on highly dispersed bimetallic Ni−Pt nanoparticles immobilized on zeolitic metal–organic framework ZIF‐8 for dopamine quantification is introduced for the first time. The as‐prepared Ni−Pt@ZIF‐8 composite was deposited onto a glassy carbon electrode (GCE), serving as a sensor that exhibits superior properties for the detection of dopamine (DA). A Box‐Behnken design was employed, and response surface methodology (RSM) was applied to investigate the impact of various experimental parameters on dopamine detection. The parameters optimized in this study included pH, drying time (hours), drop volume for deposition (μL), and accumulation time (minutes). The Box‐Behnken experimental design enabled the systematic optimization of these factors to enhance the sensor‘s performance. Benefiting from the synergy of ZIF‐8 and Ni−Pt bimetallic nanoparticles, the Ni−Pt@ZIF‐8 composite exhibited high sensitivity towards dopamine, achieving a low detection limit of 1.0 nM. The sensor‘s linear response to dopamine (1 nM to 10 μM), resistance to interference, and high recovery in human serum, coupled with its simple fabrication, make it a promising tool for real‐world dopamine detection. “This study presents the sensor design benefits from the interaction between the ZIF‐8 framework and the Ni−Pt bimetallic nanoparticles. The resulting Ni−Pt@ZIF‐8 sensor demonstrated remarkable electrochemical properties, achieving a low detection limit of 1.0 nM and a wide linear response range from 1 nM to 10 μM. Additionally, the sensor exhibited strong resistance to interference from other substances and showed high recovery rates when tested in human serum samples, indicating its robustness and reliability for real‐world applications.”