...Qualitative Data Analysis 1. Data reduction Data reduction is a term that applies to the business practice of accumulating, analyzing and ultimately transforming massive amounts of data into a series of summarized reports. The idea behind the data reduction process is to provide a complete though somewhat simplified format that can be utilized with relative ease in business settings. Several different approaches to the process may be used, with the selection of data reduction techniques and systems depending on the nature of the data and how those summary reports need to be structured in order to provide a full and comprehensive representation of that data. 2. Data Display Data display refers to computer output of data to a user, and assimilation of information from such outputs. Some kind of display output is needed for all information handling tasks. Data display is particularly critical in monitoring and control tasks. Data may be output on electronic displays, or hardcopy printouts, or other auxiliary displays and signaling devices including voice output, which may alert users to unusual conditions. 3. Reliability The ability of an apparatus, machine, or system to consistently perform its intended or required function or mission, on demand and without degradation or failure. 4. Validity Validity concerns the degree to which an account is accurate or truthful. In a broad sense, pertains to the relationship between an account and something external to it...
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...classification, analysis, and interpretation of numerical facts or data, and that, by use of mathematical theories of probability, imposes order and regularity on aggregates of more or less disparate elements. Quantitative vs. Quantities There are two general types of data. Quantitative data is information about quantities; that is, information that can be measured and written down with numbers. Some examples of quantitative data are your height, your shoe size, and the length of your fingernails. Speaking of which, it might be time to call Guinness. You've got to be close to breaking the record. Qualitative data is information about qualities; information that can't actually be measured. Some examples of qualitative data are the softness of your skin, the grace with which you run, and the color of your eyes. However, try telling Photoshop you can't measure color with numbers. Here's a quick look at the difference between qualitative and quantitative data. The age of your car. (Quantitative.) The number of hairs on your knuckle. (Quantitative.) The softness of a cat. (Qualitative.) The color of the sky. (Qualitative.) The number of pennies in your pocket. (Quantitative.) Remember, if we're measuring a quantity, we're making a statement about quantitative data. If we're describing qualities, we're making a statement about qualitative data. Keep your L's and N's together and it shouldn't be too tough to keep straight. There are a number of ways in which qualitative data can be displayed...
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...Data Collection Data Collection Ballard Integrated Managed Services Inc. (BMIS) located in New York city is a nationwide organization that specializes in providing services such as housekeeping and foodservices to 22 of Fortunes top 100 companies. Their clientele also consists of over 100 firms, 16 major universities, 14 medical centers, as well as 3 regional airports. BMIS is broken down into three divisions’ hospitality, food service, and physical plant maintenance, it also employees both full and part time workers. Recently, within the past four months general manger Barbara Tucker has noticed the turnover rate within her three divisions has reached over 64% annually as compared to the average rate between 55%and 60% (University of Phoenix, 2011, BIMS, Inc. Part I). The company moral at BMIS has reached an all time low and management has not improved its relationship with employee’s .Could this be one of the reason behind the increasing turnover rate? There has also been an increased usage of paid time off (PTO). The purpose of this research analysis is to find a solution to the increasing turnover rate as well as help improve company moral and get divisions at BMIS back on track. Instrument Design The data collection instrument used in this analysis was a survey. A survey can be defined as a tool used to collect information by asking questions recording responses on individual opinions and attitudes. BMIS administered a survey to allow...
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...Data Collection QNT/351 July 10, 2014 There are many times when companies have to collect data to come to a conclusion about an issue. The data may be collected from their employers, their competition or their consumers. BIMS saw that there had been an average turnover that was larger then what the company had seen in the past. Human Resources decided that they would conduct a survey to see what had changed in the company from the employee’s point of view. They attached each survey to the pay checks to ensure that each person received one, and waited for the response. Overview of the Situation The issue presented by BIMS employee turnover was a problem because when a company has a high turnover they put more money into the hiring and training process of new employees. The purpose was to understand what had happened in the last four months that had causes the increase in turnovers. The research questions were based on employee satisfaction within their departments. The data collected was going to be used to aide in the hypothesis that the turnover rate had increased because employee’s morals had decreased ("Develop A Research Proposal", 2014). The Types of Data and How They Were Collected The data that was conducted in the BIMS, Inc. was an employee evaluation, which consisted of both quantitative and qualitative statistics. By giving employees self-assessment will help them provide the necessary information...
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...Questions 1. A population is a collection of all individuals, objects, or measurements of interest. True False 2. A sample is a portion or part of the population of interest. True False 3. To infer something about a population, we usually take a sample from the population. True False 4. The techniques used to find out something about a population, such as their average weight, based on a sample are referred to as descriptive statistics. True False 5. There are four levels of measurement-qualitative, quantitative, discrete, and continuous. True False 6. The ordinal level of measurement is considered the "lowest" level of measurement. True False 7. A store asks shoppers for their zip code to identify market areas. Zip codes are an example of ratio data. True False 8. An ordinal level of measurement implies some sort of ranking. True False 9. Data that can only be classified into categories is measured with a nominal scale. True False 10. The terms descriptive statistics and inferential statistics can be used interchangeably. True False 11. A marketing research agency was hired to test a new DVD player. Consumers rated it outstanding, very good, fair or poor. The level of measurement for this experiment is ordinal. True False 12. The Union of Electrical Workers of America with 9,128 members polled 362 members about a new wage package that will...
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...collecting, organizing presenting, analyzing, and interpreting data to assist in making more efficient decisions" (Lind, Marchal & Wathen, 2011). This paper will summarize the types, levels, and the role of statistics in business decision making, followed by examples of statistics in action. Types, Levels, and the Role in Business Decision Making The two categories of statistics are descriptive and inferential. Descriptive statistics is the analysis of data that describes, or summarizes the data in a meaningful way (Laerd Statistics, 2013). Business leaders can organize large amounts of data into a comprehensive format utilizing descriptive statistics. But descriptive statistics do not make conclusions about the data. An inferential statistic is calculated by taking a sample of the data from the population, and drawing a conclusion about the whole based on the small amount (Lind, Marchal & Wathen). Decision makers move forward based on a conclusion drawn from statistical inference. The data gathered for statistics is classified into four levels of measurement: nominal, ordinal, interval and ratio (Lind, Marchal & Wathen). Data at the lowest level is nominal and has a qualitative variable divided into categories or outcomes. Ordinal data is qualitative and represented by sets of labels or names such as bad, good, excellent. The interval measurement is utilized to gather quantitative data classified by the amount of a particular characteristic such as...
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...QNT/351 Aug. 19, 2014 RANDALL ELKINS Schedule: Aug 08 - Sep 11, 2014 Use either the data one of your Learning Team members retained from RES/351 or the data from University of Phoenix Material: Ballard Integrated Managed Services, Inc., Part 1. Discuss with your team whether you have data from RES/351, and if your team would like to use one team member’s data for the Learning Team assignments in this course. If using data from RES/351: Resources: Data collected from RES/351 Prepare a 700- to 1,050-word written report along with a 5- to 7-slide Microsoft® PowerPoint® presentation for the senior management team or stakeholders of your RES/351 research project to present your findings. Address the following: • Present the chosen situation as an overview—problem, purpose, research questions, and hypotheses. • Describe the instrument used for data collection. • Identify types of data—quantitative, quantitative, or both—and how the data is collected. • Identify the level of measurement for each of the variables involved in the study. • Code the data if you have not done so. Describe how the data is coded and evaluate the procedure used. • Clean the data by eliminating the data input errors made. • Draw conclusions about appropriateness of the data to meet the purpose of the study. If you decide not to use your own data, you can use the Ballard Integrated Managed Services, Inc., case study overview: Resources: University...
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...Statistics-the language of data Statistic is to data as French is to language of the French. A solid understanding of statistics provides you with tools to react intelligently to information that you read or hear. Finally, an important ingredient of a well-executed statistical analysis is to clearly communicate numerical information into written language. Descriptive statistics refers to the summary of important aspects of a data set. This includes collecting data, organizing the data, and then presenting the data in the forms of charts and tables. In addition, we often calculate numerical measures that summarize, for instance, the data’s typical value and the data’s variability. Descriptive statistics is summary/describing data. Inferential statistics is drawing conclusions/assumptions. The phenomenal growth in statistics is mainly in the field called inferential statistics. Generally, inferential statistics refers to drawing conclusions about a large set of data— called a population —based on a smaller set of sample data. A population is defined as all members of a specified group (not necessarily people), whereas a sample is a subset of that particular population. In most statistical applications we must rely on sample data in order to make inferences about various characteristics of the population. For example, a 2010 survey of 1,208 registered voters by a USA TODAY/Gallup Poll found that President Obama’s job performance was viewed favorably by only 41%...
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...Services, Inc. (BIMS) has created a study to gain better understanding of the high turnover rate at the Douglas Medical Center (DMC). This report will provide recommendations for Barbara Tucker, general manager, and will help with workplace improvement. This report was created by Team C consultants and includes data and recommendations to further understand and reduce the causes of the high turnover rate. The turnover rate for DMC has been at the industry standards of 55 to 60%. However, in the last four months it has risen to over 64%. Use of sick time has increased and a large number of workers waste their time every day. Debbie Horner, HR manager, has created a survey for workers to express their opinion about the work environment. This survey asked morale and demographic questions to help with the coding and analysis of the data. The survey will help find the reason for the high turnover rate. The instrument used for data collection. The instrument that was used within Ballard Integrated Managed Services (BIMS) to find out why their moral was down was the sample method. According to "Sampling" (2012), “The sample method is a process used in statistical analysis in which a predetermined number of observations will be taken from a larger population”. The way this method was used was that the Human Resource Manager Debbie Horner handed out surveys to 449 employees asking fourteen questions. Debbie Horner decided that the upper management did not have to take the survey...
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...statistics that is relied upon to find solutions to the problems and help determine what possibilities are truly feasible. Statistics may be best defined as the means by which data is gathered, analyzed, and interpreted to better understand a problem or situation. There are two types of data that can be analyzed in statistics. These are quantitative and qualitative data. Quantitative data deals with actual numbers and percentages, things that can be measured. Qualitative data deals with things which cannot be measured, but only perceived, such as color, texture, or odor. For example, one may say that a painting’s dimensions are 15” by 20”. This is quantitative data. One may then say that the painting’s primary color used is yellow. This is qualitative data. Quantitative data deals with quantity, where qualitative data deals with quality. There are four levels of measuring data in statistics. These levels are, in ascending order of specificity: nominal, ordinal, interval, and ratio. Nominal levels of measurement deal with names and categories. Ordinal levels deal with ordering the data, but no distinguishing observations can be made. On the interval level, data can be ordered in a way that does make sense and make a difference in perception. At the ratio level, meaningful ratios can be made to represent the data at hand. For example, distance works on a ratio level. In business, statistics plays a vital role in the decision making process. Statistics help see the big picture, ensure...
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...Types of Data Collection and Levels of Measurement There are two types of data collection and they are referred to as qualitative and quantitative. Either type of data assortments hassome advantages to it and some restraints that may apply. The type of data you gather depends on the question you want to answer and your available resources. Each may be appropriate for contrasting settings, evaluation designs, and assessing questions.Qualitative data consist of words and narratives. The analysis of qualitative data can come in many forms including highlighting key words, drawing out themes, and amplifying main concepts to get a better understanding of your statistics. Qualitative data can be conducted a focus group with Ballard Integrated Managed Services, Inc. (BIMS) employees participating in a work morale satisfaction program to understand participant perceptions and behavior. If this route is chosen then the data that we collect would most likely be narrative in form and therefore we would use qualitative techniques to analyze the transcripts looking for content and themes relevant to the work moralesatisfaction program. “Quantitative data are measurements that are recorded on a naturally occurringnumerical scale”(McClave, Benson, Sincich p 13). In other words, quantitative data is numerical information which includes an analysis that involves statistical techniques, and the type of data we collect guides the examination process. For our circumstance here with Ballard Integrated...
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...achievement. The researcher sought to answer two research questions “Does differentiated instruction have an impact on student achievement?” and “Are there components of differentiated instruction that have a greater impact on student achievement than others?” The study followed a mixed method design and consisted of two parts. First, a quantitative analysis of test scores from the Michigan Education Assessment Program (MEAP) and teacher and student survey results were analyzed as a means to outline broad relationships from the data. Results from the quantitative findings directed the researcher on how to frame the qualitative design. Second, a qualitative analysis of classroom observations and interviews with teachers was conducted. The qualitative portion of this study followed a social interactionism orientation adopted by social interactionism theorist (Blumer, 1969). This approach allowed the researcher to analyze relationships between the differentiation variables. The quantitative data methods of surveys and test scores, qualitative techniques of classroom observations, and teacher interviews were triangulated. Triangulation of data was used to support research findings through independent measures to point to the same conclusions (Webb et al., 1965). The conceptual framework (Hall, 2004) served as the foundation in the identification of the differentiation variables to be studied. The research findings supported the work of learning styles theorists (Dunn, Griggs, Olsen, Beasley...
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... |The collection and analysis of information about consumers, | | | |market niches, and the effectiveness of marketing programs. | | |Primary research |Primary research (also called field research) involves the | | | |collection of data that does not already exist, which is | | | |research to collect original data. Primary Research is often| | | |undertaken after the researcher has gained some insight into| | | |the issue by collecting secondary data. | | |Secondary research |Secondary research (also known as desk research) involves | | | |the summary, collation and/or synthesis of existing research| | | |rather than primary research, where data is collected from, | ...
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...picked for this assignment is gender for the qualitative data and intrinsic for quantitative data. The data selected will be discussed and analysis until a conclusion is made. For qualitative data I picked gender. I put gender because I felt that is was the best option of the two. The gender I picked was female. I picked female because I’m female. The quantitative data I picked was Intrinsic. I picked intrinsic because Of what the definition said it was which was belonging naturally. A qualitative variable is a variable without a sense of order. A qualitative variable is measured on a nominal scale. A nominal scale is a scale with no order but has some numeric values are nominal. An example nominal scale would be the data showed on the excel sheet for this assignment where male and female are showed as, 1-male and 2-female. Quantitative variables are numerical. An example of quantitative variables would be the number of people in a town which is a measurable attribute. Population is a quantitative variable. An easy way to remember the two variables is when you think of qualitative variables think or something being categorical and when you think of quantitative think of numerical. Qualitative variable The data above use qualitative variable. As stated before a qualitative variable is measured on a nominal scale. A nominal scale is a scale with no order but has some numeric values. An example nominal scale would be the data showed on the excel sheet for this assignment...
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...judge how well we like a song, intelligence, achievement, attitude, perception regarding quality. We, thus, measure physical object as well as abstract concepts. Measurement is a relatively complex & demanding task, especially so when it concerns qualitative or abstract phenomena. So, technically speaking, measurement is a process of mapping aspects of a domain onto other aspects of a range according to some rule of correspondence. It is a collection of quantitative data. TYPES OF MEASUREMENT: 1) Nominal measurement: All qualitative measurements are nominal, regardless of whether the categories differ from one another only in names. Nominal measurements represent the most elementary level of measurement in which values are assigned to an object for identification or classification purposes only. In nominal level of measurement, the categories differ from one another only in names. In other words, one category of a characteristic is not necessary higher or lower, greater or smaller than other category. Sex (male & female), religious (Muslim, Hindu, christen etc.) are the example of the nominal measurement. In measure scale, nominal level is the lowest or weakest level of measurement & the resulting data are nominal data. This makes it impossible to conduct standard mathematical operations such as...
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