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The instructions for the 1st part pf this problem set is attached in another document labeled “PS3”. The other 2 parts, you have to click on the hyperlinks for the problems to come up. Also, please ensure each section on each problem is cleary identifying which section and part is being answered. for example. for Problem Set #3: It has 3 sections. The 1st section is called "4RM – Data Collection" 9ATTACHED IN MICROFOST WORD DOCUMENT) and that section has (9) parts. Then the next section is called "5RM - Hypotheses and Hypothesis Testing" and that section has (4) parts, and so on. **Please keep in mind that all pasts do NOT neccessacy have a portion that have to be turned in. some of the parts are scenarios. you will need this FREE website in order to place the data into the system to get the calculations. YOu have to create a login. https://www.icpsr.umich.edu/rpxlogin SCHOLAR-PRACTITIONER RESEARCH PROBLEM SETS The research course series in this course focuses on developing the knowledge, skills, and habits of mind which are important for leaders to address complex problems as scholar-practitioners. This course uses a problem-based learning pedagogy consistent with self-directed learning characteristic of both scientific inquiry and professional practice-based learning. A problem set is a narration of the solution to a problem. A narration is a coherent story which describes a series of events from the beginning to end. As you solve, write short narrative comments between equations and sketches to transition the reader through your logic. A problem set solution requires making the audience believe that you know how to solve this problem. Problem sets in DEL 805 are opportunities to practice research thinking to analyze quantitative data. They require using the knowledge and skills from the readings in new ways. There are multiple correct solutions to problem sets. Responses to problem sets require making transparent one’s logical thought process and assumptions used to solve the problem. Communicating one’s logic requires citing the examples and principles from the readings which relate to the problem and present one’s argument for how they are applied to the problem set. The problem sets in this course come from the Social Science Research and Instructional Council's website. They use the Monitoring the Future 2015 survey of high school seniors as the data base for the exercises. This survey is freely available through the Inter-university Consortium for Political and Social Research. They also use SDA (Survey Documentation and Analysis) as the statistical package to analyze the data. SDA was developed by the Survey Methods Program at UC Berkeley and is freely available to anyone with internet access. There are notes on using SDA which you might find helpful. ***** Right click and open in new window***** Research Problem #3 RESEARCH_METHODS_4RM - Data Collection (Survey Research)** THIS LINK DOESN’T WORK*** CLICK ON THE WORD DOC FOR INFO · RESEARCH_METHODS_5RM - Hypotheses and Hypothesis Testing · RESEARCH_METHODS_6RM - Introduction to Data Analysis RESEARCH METHODS 4RM - Data Collection Author: Ed Nelson Department of Sociology M/S SS97 California State University, Fresno Fresno, CA 93740 Email: ednelson@csufresno.edu Note to the Instructor: This is the fourth in a series of 13 exercises that were written for an introductory research methods class. The first exercise focuses on the research design which is your plan of action that explains how you will try to answer your research questions. Exercises two through four focus on sampling, measurement, and data collection. The fifth exercise discusses hypotheses and hypothesis testing. The last eight exercises focus on data analysis. In these exercises we’re going to analyze data from one of the Monitoring the Future Surveys (i.e., the 2017 survey of high school seniors in the United States). This data set is part of the collection at the Inter-university Consortium for Political and Social Research at the University of Michigan. The data are freely available to the public and you do not have to be a member of the Consortium to use the data. We’re going to use SDA (Survey Documentation and Analysis) to analyze the data which is an online statistical package written by the Survey Methods Program at UC Berkeley and is available without cost wherever one has an internet connection. A weight variable is automatically applied to the data set so it better represents the population from which the sample was selected. You have permission to use this exercise and to revise it to fit your needs. Please send a copy of any revision to the author so I can see how people are using the exercises. Included with this exercise (as separate files) are more detailed notes to the instructors and the exercise itself. Please contact the author for additional information. This page in MS Word (.docx) format is attached. Goal of Exercise The goal of this exercise is to provide an introduction to data collection which is an integral part of any research design. In this exercise we’re going to focus on survey research as a method of data collection. The other elements of your research design are sampling, measurement, and data analysis which are discussed in other exercises. Part I—Inevitability of Error Error is inevitable in any research study. It’s impossible to eliminate all sources of error. What we do is to try to identify all sources of error and then minimize error to the extent possible. There are a number of different types of error. In this exercise we’ll discuss four types or sources of error in survey research – sampling, coverage, nonresponse, and measurement. Part II – Sampling Error No sample is ever a perfect representation of the population from which the sample is drawn. Some error is always introduced when you take a sample from a population and this is called sampling error. Imagine that your population is all high school seniors at a large urban high school that has 3,000 seniors. We’re interested in the percent of seniors that engage in binge drinking.[1] We decide to select a simple random sample of 300 seniors from this population and ask each of these 300 seniors if they ever had five or more drinks in a row.[2] The percent of the sample that has engaged in binge drinking is our estimate of the population percent. Now imagine selecting a second simple random sample of 300 seniors from this same population and asking them the same question. You can immediately see that our two estimates of the percent of the population that binge drink would not be identical because the two samples would consist of different high school seniors.[3] Assuming that we are using probability sampling, sampling error depends on three factors. · Size of the sample. The larger the sample, the less the sampling error. That’s why we prefer a large sample to a small sample. But there is a point of diminishing returns. Once we have a sample of between 1,000 and 2,000 there isn’t much of a reduction in sampling error when we further increase the size of our sample. For example, election polls rarely use a sample much larger than 1,500 to estimate the percent of the population that intend to vote for a particular candidate. · Amount of variability in the population. The more variability there is in the population, the more the sampling error. If the entire population intended to vote for the same candidate, there wouldn’t be any sampling error. If the population was evenly divided as to whether they were going to vote for candidate A or candidate B that would represent the maximum amount of population variability and we would have more sampling error. · The way we select the sample. In exercise 2RM we discussed stratification. When we stratify our sample, sampling error decreases. This assumes that our stratification variable is related to whatever we are trying to estimate. (See exercise 2RM for a fuller discussion of stratification.) Part III – Coverage Error Coverage error occurs when the list of the population from which we select our sample does not perfectly match the population. Think about the example from part 2 where we selected a sample of 300 high school seniors from the population of 3,000 seniors at a large urban high school. We would expect the high school to have an accurate list of all seniors from which we could select our sample. In this case the list of the population would perfectly match the population and there would be no coverage error.[4] Here are some examples of coverage error. · The General Social Survey is a large national probability sample of adults in the United States conducted biannually by the National Opinion Research Center at the University of Chicago. Prior to 2006 the sample consisted of adults living in non-institutionalized settings who spoke English. Starting in 2006, Spanish-speaking adults were included in the sample. While the exclusion of non-institutionalized adults and those who don’t speak English or Spanish introduces two sources of coverage error, both represent small proportions of the adult population. The exclusion of these two groups offers both cost savings and greater ease of survey administration. Since the coverage error is relatively small, the advantages outweigh the small increase in sampling error. However, as more non-English and non-Spanish speaking individuals immigrate to the U.S., coverage error might increase in the future. · The Monitoring the Future Survey of high school seniors excludes seniors in Alaska and Hawaii for both cost reasons and ease of administration. This introduces a small amount of coverage error. Since the survey is administered in the spring of each year, it also excludes seniors who dropped out prior to the survey administration. This too introduces a small amount of coverage error. These examples demonstrate that small amounts of coverage error may be tolerated for cost reasons and the ease of survey administration. But sometimes coverage error can be quite large as you will see in the next part of this exercise. Part IV – Now It’s Your Turn to Consider Coverage Error We’re going to consider two hypothetical surveys and think about the types of coverage error that might occur. · Our research center has been asked to do a survey of adults in our community regarding quality of life. Specifically, we want to determine how satisfied respondents are with different areas of their local community including the local economy, level of crime, road conditions, health services, and education. We decide to do a phone survey of households in our community. · Suppose we select a sample of phone numbers from the local phone directory. What types of coverage error would that introduce? Do you think the amount of coverage error would be fairly small or quite large? Why? · Someone points out to us that published phone directories do not typically include cell phones or people with unlisted numbers so we contact a reputable research service that provides samples of both cell phone and landline numbers as well as people with unlisted numbers. What would that do to our coverage error? Would there be any remaining sources of coverage error? What would they