A Quasi-Experimental Study Measuring the Effect of Dimensional Analysis on Undergraduate Nurses' Level of Self-Efficacy in Medication Calculations
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Those Missing Values in Questionnaires
Those Missing Values in Questionnaires John R. Gerlach, Maxim Group, Plymouth Meeting, PA Cindy Garra, IMS HEALTH; Plymouth Meeting, PA Abstract alternative values are not bona fide SAS missing values; consequently, a SAS procedure, expectedly, will include Questionnaires are notorious for containing these alternative values, thereby producing bogus results. responses that later become missing values during processing and analyses. Contrary to a non-response that Typically, you can re-code the original data, so results in a typical, bona fide, missing value, questions that the missing values become ordinary missing values, might allow several alternative responses, necessarily thus allowing SAS to process only appropriate values. Of outside the usual range of appropriate responses. course, there's a loss of information since the original Whether a question represents continuous or categorical missing values did not constitute a non-response. Also, data, a good questionnaire offers meaningful alternatives, such pre-processing might include a myriad of IF THEN / such as: "Refused to Answer" and, of course, the ELSE statements, which can be very tedious and time- quintessential "Don't Know." Traditionally, these consuming to write, and difficult to maintain. Thus, given alternative responses have numeric values such as 97, a questionnaire that has over a hundred variables with 998, or 9999 and, therefore, pose problems when trying varying levels of missing values, the task of re-coding to distinguish them from normal responses, especially these variables becomes very time consuming at best. when multiple missing values exist. This paper discusses Even worse, the permanent re-coding of alternative missing values in SAS and techniques that facilitate the responses to ordinary missing numeric values in SAS process of dealing with multi-valued, meaningful missing precludes categorical analysis that requires the inclusion values often found in questionnaires. -
10 Questions Opinion Polls
Questions you might have on 10opinion polls 1. What is an opinion poll? An opinion poll is a survey carried out to measure views on a certain topic within a specific group of people. For example, the topic may relate to who Kenyans support in the presidential race, in which case, the group of people interviewed will be registered voters. 2. How are interviewees for an opinion poll selected? The group of people interviewed for an opinion poll is called a sample. As the name suggests, a sample is a group of people that represents the total population whose opinion is being surveyed. In a scientific opinion poll, everyone has an equal chance of being interviewed. 3. So how come I have never been interviewed for an opinion poll? You have the same chance of being polled as anyone else living in Kenya. However, chances of this are very small and are estimated at about 1 in 14,000. This is because there are approximately 14 million registered voters in Kenya and, for practical and cost reasons, usually only between 1,000 and 2,000 people are interviewed for each survey carried out. 4. How can such a small group be representative of the entire population? In order to ensure that the sample/survey group is representative of the population, the surveyors must ensure that the group reflects the characteristics of the whole. For instance, to get a general idea of who might win the Kenyan presidential election, only the views of registered voters in Kenya will be surveyed as these are the people who will be able to influence the election. -
Handbook of Recommended Practices for Questionnaire Development and Testing in the European Statistical System
Handbook of Recommended Practices for Questionnaire Development and Testing in the European Statistical System Release year: 2006 Authors: G. Brancato, S. Macchia, M. Murgia, M. Signore, G. Simeoni - Italian National Institute of Statistics, ISTAT K. Blanke, T. Körner, A. Nimmergut - Federal Statistical Office Germany, FSO P. Lima, R. Paulino - National Statistical Institute of Portugal, INE J.H.P. Hoffmeyer-Zlotnik - German Center for Survey Research and Methodology, ZUMA Version 1 Acknowledgements We are grateful to the experts from the network countries who supported us in all relevant stages of the work: Anja Ahola, Dirkjan Beukenhorst, Trine Dale, Gustav Haraldsen. We also thank all colleagues from European and overseas NSIs who helped us in understanding the current practices and in the review of the draft version of the handbook. Executive summary Executive Summary Questionnaires constitute the basis of every survey-based statistical measurement. They are by far the most important measurement instruments statisticians use to grasp the phenomena to be measured. Errors due to an insufficient questionnaire can hardly be compensated at later stages of the data collection process. Therefore, having systematic questionnaire design and testing procedures in place is vital for data quality, particularly for a minimisation of the measurement error. Against this background, the Directors General of the members of the European Statistical System (ESS) stressed the importance of questionnaire design and testing in the European Statistics Code of Practice, endorsed in February 2005. Principle 8 of the Code states that “appropriate statistical procedures, implemented from data collection to data validation, must underpin quality statistics.” One of the indicators referring to this principle requires that “questionnaires are systematically tested prior to the data collection.” Taking the Code of Practice as a starting point, this Recommended Practice Manual aims at further specifying the requirements of the Code of Practice. -
Education Quarterly Reviews
Education Quarterly Reviews Allanson, Patricia E., and Notar, Charles E. (2020), Statistics as Measurement: 4 Scales/Levels of Measurement. In: Education Quarterly Reviews, Vol.3, No.3, 375-385. ISSN 2621-5799 DOI: 10.31014/aior.1993.03.03.146 The online version of this article can be found at: https://www.asianinstituteofresearch.org/ Published by: The Asian Institute of Research The Education Quarterly Reviews is an Open Access publication. It May be read, copied, and distributed free of charge according to the conditions of the Creative ComMons Attribution 4.0 International license. The Asian Institute of Research Education Quarterly Reviews is a peer-reviewed International Journal. The journal covers scholarly articles in the fields of education, linguistics, literature, educational theory, research, and methodologies, curriculum, elementary and secondary education, higher education, foreign language education, teaching and learning, teacher education, education of special groups, and other fields of study related to education. As the journal is Open Access, it ensures high visibility and the increase of citations for all research articles published. The Education Quarterly Reviews aiMs to facilitate scholarly work on recent theoretical and practical aspects of education. The Asian Institute of Research Education Quarterly Reviews Vol.3, No.3, 2020: 375-385 ISSN 2621-5799 Copyright © The Author(s). All Rights Reserved DOI: 10.31014/aior.1993.03.03.146 Statistics as Measurement: 4 Scales/Levels of Measurement 1 2 Patricia E. Allanson , Charles E. Notar 1 Liberty University 2 Jacksonville State University. EMail: [email protected] (EMeritus) Abstract This article discusses the basics of the “4 scales of MeasureMent” and how they are applicable to research or everyday tools of life. -
Meta-Analysis of the Relationships Between Different Leadership Practices and Organizational, Teaming, Leader, and Employee Outcomes*
Journal of International Education and Leadership Volume 8 Issue 2 Fall 2018 http://www.jielusa.org/ ISSN: 2161-7252 Meta-Analysis of the Relationships Between Different Leadership Practices and Organizational, Teaming, Leader, and Employee Outcomes* Carl J. Dunst Orelena Hawks Puckett Institute Mary Beth Bruder University of Connecticut Health Center Deborah W. Hamby, Robin Howse, and Helen Wilkie Orelena Hawks Puckett Institute * This study was supported, in part, by funding from the U.S. Department of Education, Office of Special Education Programs (No. 325B120004) for the Early Childhood Personnel Center, University of Connecticut Health Center. The contents and opinions expressed, however, are those of the authors and do not necessarily reflect the policy or official position of either the Department or Office and no endorsement should be inferred or implied. The authors report no conflicts of interest. The meta-analysis described in this paper evaluated the relationships between 11 types of leadership practices and 7 organizational, teaming, leader, and employee outcomes. A main focus of analysis was whether the leadership practices were differentially related to the study outcomes. Studies were eligible for inclusion if the correlations between leadership subscale measures (rather than global measures of leadership) and outcomes of interest were reported. The random effects weighted average correlations between the independent and dependent measures were used as the sizes of effects for evaluating the influences of the leadership practices on the outcome measures. One hundred and twelve studies met the inclusion criteria and included 39,433 participants. The studies were conducted in 31 countries in different kinds of programs, organizations, companies, and businesses. -
Survey Experiments
IU Workshop in Methods – 2019 Survey Experiments Testing Causality in Diverse Samples Trenton D. Mize Department of Sociology & Advanced Methodologies (AMAP) Purdue University Survey Experiments Page 1 Survey Experiments Page 2 Contents INTRODUCTION ............................................................................................................................................................................ 8 Overview .............................................................................................................................................................................. 8 What is a survey experiment? .................................................................................................................................... 9 What is an experiment?.............................................................................................................................................. 10 Independent and dependent variables ................................................................................................................. 11 Experimental Conditions ............................................................................................................................................. 12 WHY CONDUCT A SURVEY EXPERIMENT? ........................................................................................................................... 13 Internal, external, and construct validity .......................................................................................................... -
MRS Guidance on How to Read Opinion Polls
What are opinion polls? MRS guidance on how to read opinion polls June 2016 1 June 2016 www.mrs.org.uk MRS Guidance Note: How to read opinion polls MRS has produced this Guidance Note to help individuals evaluate, understand and interpret Opinion Polls. This guidance is primarily for non-researchers who commission and/or use opinion polls. Researchers can use this guidance to support their understanding of the reporting rules contained within the MRS Code of Conduct. Opinion Polls – The Essential Points What is an Opinion Poll? An opinion poll is a survey of public opinion obtained by questioning a representative sample of individuals selected from a clearly defined target audience or population. For example, it may be a survey of c. 1,000 UK adults aged 16 years and over. When conducted appropriately, opinion polls can add value to the national debate on topics of interest, including voting intentions. Typically, individuals or organisations commission a research organisation to undertake an opinion poll. The results to an opinion poll are either carried out for private use or for publication. What is sampling? Opinion polls are carried out among a sub-set of a given target audience or population and this sub-set is called a sample. Whilst the number included in a sample may differ, opinion poll samples are typically between c. 1,000 and 2,000 participants. When a sample is selected from a given target audience or population, the possibility of a sampling error is introduced. This is because the demographic profile of the sub-sample selected may not be identical to the profile of the target audience / population. -
Levels of Measurement and Types of Variables
01-Warner-45165.qxd 8/13/2007 4:52 PM Page 6 6—— CHAPTER 1 evaluate the potential generalizability of research results based on convenience samples. It is possible to imagine a hypothetical population—that is, a larger group of people that is similar in many ways to the participants who were included in the convenience sample—and to make cautious inferences about this hypothetical population based on the responses of the sample. Campbell suggested that researchers evaluate the degree of similarity between a sample and hypothetical populations of interest and limit general- izations to hypothetical populations that are similar to the sample of participants actually included in the study. If the convenience sample consists of 50 Corinth College students who are between the ages of 18 and 22 and mostly of Northern European family back- ground, it might be reasonable to argue (cautiously, of course) that the results of this study potentially apply to a hypothetical broader population of 18- to 22-year-old U.S.col- lege students who come from similar ethnic or cultural backgrounds. This hypothetical population—all U.S.college students between 18 and 22 years from a Northern European family background—has a composition fairly similar to the composition of the conve- nience sample. It would be questionable to generalize about response to caffeine for pop- ulations that have drastically different characteristics from the members of the sample (such as persons who are older than age 50 or who have health problems that members of the convenience sample do not have). Generalization of results beyond a sample to make inferences about a broader popula- tion is always risky, so researchers should be cautious in making generalizations. -
Chapter 1 Getting Started Section 1.1
Chapter 1 Getting Started Section 1.1 1. (a) The variable is the response regarding frequency of eating at fast-food restaurants. (b) The variable is qualitative. The categories are the number of times one eats in fast-food restaurants. (c) The implied population is responses for all adults in the U.S. 3. (a) The variable is student fees. (b) The variable is quantitative because arithmetic operations can be applied to the fee values. (c) The implied population is student fees at all colleges and universities in the U.S. 5. (a) The variable is the time interval between check arrival and clearance. (b) The variable is quantitative because arithmetic operations can be applied to the time intervals. (c) The implied population is the time interval between check arrival and clearance for all companies in the five-state region. 7. (a) Length of time to complete an exam is a ratio level of measurement. The data may be arranged in order, differences and ratios are meaningful, and a time of 0 is the starting point for all measurements. (b) Time of first class is an interval level of measurement. The data may be arranged in order and differences are meaningful. (c) Class categories is a nominal level of measurement. The data consists of names only. (d) Course evaluation scale is an ordinal level of measurement. The data may be arranged in order. (e) Score on last exam is a ratio level of measurement. The data may be arranged in order, differences and ratios are meaningful, and a score of 0 is the starting point for all measurements. -
Development and Factor Analysis of a Questionnaire to Measure Patient Satisfaction with Injected and Inhaled Insulin for Type 1 Diabetes
Epidemiology/Health Services/Psychosocial Research ORIGINAL ARTICLE Development and Factor Analysis of a Questionnaire to Measure Patient Satisfaction With Injected and Inhaled Insulin for Type 1 Diabetes JOSEPH C. CAPPELLERI, PHD, MPH IONE A. KOURIDES, MD system that permits noninvasive delivery of ROBERT A. GERBER, PHARMD, MA ROBERT A. GELFAND, MD rapid-acting insulin was developed (Inhale Therapeutics Systems, San Carlos, CA) that offers an effective and well-tolerated alternative to preprandial insulin injections in type 1 diabetes (3). Inhaled insulin, OBJECTIVE — To develop a self-administered questionnaire to address alternative deliv- intended for use in a preprandial therapeu- ery routes of insulin and to investigate aspects of patient satisfaction that may be useful for tic regimen, is the first practical alternative subsequent assessment and comparison of an inhaled insulin regimen and a subcutaneous to injections for therapeutic administration insulin regimen. of insulin. However, measures of treatment RESEARCH DESIGN AND METHODS — Attributes of patient treatment satisfaction satisfaction in diabetes have not directly with both inhaled and injected insulin therapy were derived from five qualitative research stud- examined delivery routes for insulin other ies to arrive at a 15-item questionnaire. Each item was analyzed on a five-point Likert scale so than by injection (e.g., syringe, pen, or that higher item scores indicated a more favorable attitude. There were 69 subjects with type 1 pump) and were developed at a time when diabetes previously taking injected insulin therapy who were enrolled in a phase II clinical trial. only injectable forms of insulin were readily Their baseline responses on the questionnaire were evaluated and subjected to an exploratory available in clinical practice (4–7). -
Appropriate Statistical Analysis for Two Independent Groups of Likert
APPROPRIATE STATISTICAL ANALYSIS FOR TWO INDEPENDENT GROUPS OF LIKERT-TYPE DATA By Boonyasit Warachan Submitted to the Faculty of the College of Arts and Sciences of American University in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy In Mathematics Education Chair: Mary Gray, Ph.D. Monica Jackson, Ph.D. Jun Lu, Ph.D. Dean of the College of Arts and Sciences Date 2011 American University Washington, D.C. 20016 © COPYRIGHT by Boonyasit Warachan 2011 ALL RIGHTS RESERVED DEDICATION This dissertation is dedicated to my beloved grandfather, Sighto Chankomkhai. APPROPRIATE STATISTICAL ANALYSIS FOR TWO INDEPENDENT GROUPS OF LIKERT-TYPE DATA BY Boonyasit Warachan ABSTRACT The objective of this research was to determine the robustness and statistical power of three different methods for testing the hypothesis that ordinal samples of five and seven Likert categories come from equal populations. The three methods are the two sample t-test with equal variances, the Mann-Whitney test, and the Kolmogorov-Smirnov test. In additional, these methods were employed over a wide range of scenarios with respect to sample size, significance level, effect size, population distribution, and the number of categories of response scale. The data simulations and statistical analyses were performed by using R programming language version 2.13.2. To assess the robustness and power, samples were generated from known distributions and compared. According to returned p-values at different nominal significance levels, empirical error rates and power were computed from the rejection of null hypotheses. Results indicate that the two sample t-test and the Mann-Whitney test were robust for Likert-type data. -
Guidelines for Stated Preference Experiment Design
Guidelines for Stated Preference Experiment Design (Professional Company Project in Association with RAND Europe) A dissertation submitted for the degree of Master of Business Administration. Project Period: Aug. 1, 2001 – Nov. 30, 2001 November 23, 2001 © Nobuhiro Sanko 2001 All rights reserved School of International Management Ecole Nationale des Ponts et Chaussées (Class 2000/01) Nobuhiro SANKO Nobuhiro Sanko, 2001 ii Abstract The purpose of stated preference design is how to collect data for efficient model estimation with as little bias as possible. Full factorial or fractional factorial designs have been frequently used just in order to keep orthogonality and to avoid multi-colinearity between the attributes. However, these factorial designs have a lot of practical problems. Although many methods are introduced to solve some of these problems, there is no powerful way which solves all problems at once. Therefore, we need to combine some existing methods in the experiment design. So far, several textbooks about stated preference techniques have been published, but most of them just introduced some existing methods for experimental design and gave less guidance how to combine them. In this paper, we build a framework which brings an easier guideline to build SP design. In each step of the framework, we show a problem to be considered and methods to solve it. For each method, the advantage, disadvantage and the criteria are explained. Based on this framework, we believe even the beginner can build a reasonable design. Of course for advanced researchers, this paper will be a useful guidebook to understand stated preference design from different viewpoint.