Quantitative techniques & data interpretation
Data tative matory techniques discussed in this section are classical s as opposed to eda techniques. For example, the analysis can start with some cal techniques such as the 4-plot followed by the matory methods discussed herein to provide more rigorous the conclusions. Often this is an indication that some of tions of the classical techniques are of the quantitative techniques fall into two broad categories:It is common in statistics to estimate a a sample of data. The value of the parameter using all of le data, not just the sample data, is called the ter or true value of the parameter. However, instead of providing an interval, esis test attempts to refute a specific claim about tion parameter based on the sample data. In these cases, the es a method for empirically determining an appropriate of the more common classical quantitative techniques are .
Related slideshares at tative techniques introduction 19 hed on jul 14, you sure you want message goes you sure you want message goes you sure you want message goes tell us how to download it??? General manager at africa dataedge dataedge tative techniques introduction 19 quantitative techniques abs-bangalore quantitative techniques - rvmreddy - abs july 14, 2010. Venkatamuni reddy associate professor
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- introduction
- definitions
- evolution
- classification
- role of quantitative techniques in business and industry
- quantitative techniques and business management
- advantages and limitations
- a person managing a production unit, where it is a farm, factory, or domestic kitchen, has to coordinate men, machines, and money against several constraints like that of time, cost and space, in order to achieve the organizations objectives in an efficient and effective manner. Li>
- the manager has to analyze the situation on a continuous basis, determine the objectives, identify the best options from the set of available alternatives, implement, coordinate, evaluate and control the situation continuously to achieve these objectives
- quantitative techniques are those statistical and programming techniques, which help decision makers solve many problems, especially those concerning business and industry
- quantitative techniques are those techniques that provide the decision makers with systematic and powerful means of analysis, based on quantitative data, for achieving predetermined goals
- the utility of quantitative techniques has been realized long ago and the science of mathematics is probably as old as the human society
- the evolution of industrial engineering, scientific methodologies the were prominent earlier in the natural sciences, were found applicable to management functions-planning, organizing and controlling of operations
- they can broadly be put under two groups
- 1) statistical techniques: which are used in conducting the statistical inquiry concerning a certain phenomenon
- it includes all the statistical methods beginning from the collection of data till the task of interpretation of the collected data.
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- collection, classification, summarizing, analyzing , interpretation of the data
- first designed to tackle defense and military problems and are now being used to solve business problems
- it includes variety of techniques like linear programming, games theory, simulation, network analysis, queuing theory, and so on
- system under consideration are defined in mathematical language: variable (factors which are controlled), coefficients (factors which are not controlled)
- appropriate mathematical expressions are formulated which describes inter-relations of all variables and coefficients. It describes the technology and the economics of a business through a set of simultaneous equations and inequalities
- an optimum solutions is determined (maximizing profit and minimizing cost)
- quantitative techniques specially operation research techniques have gained increasing importance since world war ii in the technology of business administration. These techniques greatly help in tackling the intricate and complex problems of modern business and industry
- they provide a tool for scientific analysis
- they provide solutions for various business problems
- they enable proper deployment of resources
- they help in minimizing waiting and servicing costs
- they enable the management to decide when to buy and how much to buy
- they assist in choosing an optimum strategy
- they render great help in optimum resource allocation
- they facilitate the process of decision making
- through various quantitative techniques management can know the reaction of integrated business systems
- it helps the directing authority in optimum allocation of various limited resources viz.
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- the inherent limitation concerning mathematical expressions
- high costs are involved in the use of quantitative techniques
- quantitative techniques do not take into consideration the intangible factors ie non-measurable human factors. Li>
- quantitative techniques are just the tools of analysis and not the complete decision making process
London: sage publications, teristics of quantitative goal in conducting quantitative research study is to determine the relationship between one thing [an independent variable] and another [a dependent or outcome variable] within a population. Quantitative research designs are either descriptive [subjects usually measured once] or experimental [subjects measured before and after a treatment]. Quantitative research focuses on numeric and unchanging data and detailed, convergent reasoning rather than divergent reasoning [i. Main characteristics are:The data is usually gathered using structured research results are based on larger sample sizes that are representative of the research study can usually be replicated or repeated, given its high cher has a clearly defined research question to which objective answers are aspects of the study are carefully designed before data is are in the form of numbers and statistics, often arranged in tables, charts, figures, or other non-textual t can be used to generalize concepts more widely, predict future results, or investigate causal cher uses tools, such as questionnaires or computer software, to collect numerical overarching aim of a quantitative research study is to classify features, count them, and construct statistical models in an attempt to explain what is to keep in mind when reporting the results of a study using quantitative methods:Explain the data collected and their statistical treatment as well as all relevant results in relation to the research problem you are investigating. Interpretation of results is not appropriate in this unanticipated events that occurred during your data collection. Explain your handling of missing data and why any missing data does not undermine the validity of your n the techniques you used to "clean" your data a minimally sufficient statistical procedure; provide a rationale for its use and a reference for it.
Keep figures small in size; include graphic representations of confidence intervals whenever tell the reader what to look for in tables and : when using pre-existing statistical data gathered and made available by anyone other than yourself [e. Government agency], you still must report on the methods that were used to gather the data and describe any missing data that exists and, if there is any, provide a clear explanation why the missing data does not undermine the validity of your final , earl r. Los angeles, ca: sage, research design for quantitative designing a quantitative research study, you must decide whether it will be descriptive or experimental because this will dictate how you gather, analyze, and interpret the results. An experimental design includes subjects measured before and after a particular treatment, the sample population may be very small and purposefully chosen, and it is intended to establish causality between introduction to a quantitative study is usually written in the present tense and from the third person point of view. Methods section of a quantitative study should describe how each objective of your study will be achieved. The methods section should be presented in the past population and sampling -- where did the data come from; how robust is it; note where gaps exist or what was excluded.
Collection – describe the tools and methods used to collect information and identify the variables being measured; describe the methods used to obtain the data; and, note if the data was pre-existing [i. Note that no data set is perfect--describe any limitations in methods of gathering analysis -- describe the procedures for processing and analyzing the data. If appropriate, describe the specific instruments of analysis used to study each research objective, including mathematical techniques and the type of computer software used to manipulate the finding of your study should be written objectively and in a succinct and precise format. In quantitative studies, it is common to use graphs, tables, charts, and other non-textual elements to help the reader understand the data. Further information about how to effectively present data using charts and graphs can be found tical analysis -- how did you analyze the data? Describe any limitations or unavoidable bias in your study and, if necessary, note why these limitations did not inhibit effective interpretation of the your study by to summarizing the topic and provide a final comment and assessment of the y of findings – synthesize the answers to your research questions.
Do not report any statistical data here; just provide a narrative summary of the key findings and describe what was learned that you did not know before conducting the endations – if appropriate to the aim of the assignment, tie key findings with policy recommendations or actions to be taken in research – note the need for future research linked to your study’s limitations or to any remaining gaps in the literature that were not addressed in your , thomas r. Doing quantitative research in the social sciences: an integrated approach to research design, measurement and statistics. Kennesaw state ths of using quantitative tative researchers try to recognize and isolate specific variables contained within the study framework, seek correlation, relationships and causality, and attempt to control the environment in which the data is collected to avoid the risk of variables, other than the one being studied, accounting for the relationships the specific strengths of using quantitative methods to study social science research problems:Allows for a broader study, involving a greater number of subjects, and enhancing the generalization of the results;. Generally, quantitative methods are designed to provide summaries of data that support generalizations about the phenomenon under study. In order to accomplish this, quantitative research usually involves few variables and many cases, and employs prescribed procedures to ensure validity and reliability;. Los angeles, ca: sage, tions of using quantiative tative methods presume to have an objective approach to studying research problems, where data is controlled and measured, to address the accumulation of facts, and to determine the causes of behavior.
As a consequence, the results of quantitative research may be statistically significant but are often humanly specific limitations associated with using quantitative methods to study research problems in the social sciences include:Quantitative data is more efficient and able to test hypotheses, but may miss contextual detail;. Development of standard questions by researchers can lead to "structural bias" and false representation, where the data actually reflects the view of the researcher instead of the participating subject;. In a similar manner to principal component analysis, it provides a means of displaying or summarising a set of data in two-dimensional graphical pondence analysis is used to make perceptual mapping illustrating graphically relationships between variables. Y=ax1 + bx2 + c) and a correlation coefficient r ranging from 0 to 1 estimating the efficacy of the regression function to predict y value from x1, x2, x3, le regression analysis is used in attitudinal segmentation study to provide the scoring system which can individually allocate customers to the identified and described market research 2012 | terms & conditions | privacy statement | site mming office personal al ing & on & al brand & study engine media ing ics & & mobile -digital raphy cial ional ational high your team access to udemy’s top 2,000 courses anytime, udemy for what you know into an opportunity and reach millions around the ed ibm spss- quantitative techniques in d of using a simple lifetime average, udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings. 962 students ed ibm spss- quantitative techniques in the basic to advanced skills required in quantitative research data analysis with newest version of ibm d of using a simple lifetime average, udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings. Your team access to udemy's top 2,000 courses anytime, udemy for tand the basic concepts for research to perform data analysis for quantitative the advanced skills required to perform analysis on ibm ready to learn a comprehensive basic to advanced spss microsoft word installed in their desktop or laptop l ibm spss statistics 20 or any new course is about advanced techniques required in the quantitative data analysis of business research through ibm- spss.
This course is for all learners since it provides the understanding from basic to advanced techniques making it easier for a beginner and also a learner with the basic knowledge of students who want to master the techniques to use spss and carry out analysis of the research data can take this course. It has mainly 11 sections and the main concepts taught through out the sections are:basics of spssmeasure of central tendencyadvanced learning- data analysisreliability analysisdemographic analysisdescriptive/ normalityfactor analysiscorrelationregression- linear and multipleregression in the case of mediation and moderationthe course has helping material to understand the concepts better in the form of short notes and also the practice exercises at every step to master the skills required to perform is the target audience? Interested to learn basics and advanced concepts in interested in learning and enhancing skills to perform quantitative data analysis in e to other spss ulum for this uction to ibm lecture is the basic road map for the spss ts will be able to get the basic knowledge about spss that how to insert data and get the case summaries for the ng data and case ts will be able to cover another basic tools of creating labels for items and then value labels for their ng labels and value ts will be able to get to know that how variables can be added, deleted or moved in the data set of ent functions with ts will learn to modify default options in spss according to their needs in data ing default options in ts will be able to measure mode, median and mean in a data ing central ts will learn the basic kind of statistical analysis to sum up their data by creating stem and leaf and leaf plot ts will be able to create the basic pie and bar ng bar and pie analysis- advanced ts will be able to learn what actually data analysis is and what kind of analyses we are going to perform in uction to data ts will be able to know what actually reliability is and refresh their concepts about uction to reliability ts will be able to perform reliability analysis by learning techniques and the concepts involved in ming reliability ts will be able to know that how to interpret reliability in apa reting reliability ts will be able to know what actually demographics are an then their existence in the data set in uction to ts will be able to perform demographic analysis and then interpret it in apa aphic analysis and ptive analysis/ ts will be able to know the meaning of descriptive and then importance of normality analysis in uction to descriptive and normality ts will be able to check the variability in their variables and data and see if the data is normal or not by performing normality ming descriptives and normality ts will be able to perform factor analysis if they are taking an adapted questionnaire or if they have made it by ts will be able to learn why do we conduct correlation and then different ranges lying within it for hypothesis uction to ts will be able to perform correlation for associative studies and then interpret them in apa mance and interpretation of ts will get the knowledge about the two basic types of regression- linear and uction to regression- linear and ts will be able to perform and interpret linear ming linear ts will be able to learn how to perform regression in case of multiple variables and then interpret it in apa ming multiple regression.