Applied Quantitative Methods MBA course Montenegro

AppliedQuantitative MethodsMBA course Montenegro Peter Balogh PhD baloghp@agr.unideb.hu

BASIC DATA OF THE SUBJECT • Name of thesubject:AppliedQuantitativeMethods • Course status:obligatory • Language: English • SubjecteducatorName: Dr. Peter Balogh • Title:Associate Professor • Affiliation: University Debrecen • Period:September 2011 • Prerequisite:none • Objective of thetraining: The studentsbecamefamiliarwiththeuse of quantitativemethodsin business • Contacteducation: 20 hours • Consultation: 0 hours • Individualassignment: 85 hours • Total: 105 hours • Credit: ECTS : 8

Description of theindividualassignment: • Prepare and present a casestudyusingthequantitativemethodswithinaworkinggroup. • Examinationsrequirements: • Oral: Presentation • Written: Prepare a casestudyusingthequantitativemethods • Compulsoryliterature: • JonCurwin and Roger Slater: QuantitativeMethodsfor Business Decisions, Fifthedition, • CengageLearning Business Press, ISBN-13: 978-1861525314 • Recommendedliterature: • David R. Anderson, Dennis J. Sweeney, Thomas A. Williams, Jeffry D. Camm, Kipp Martin: QuantitativeMethodsfor Business, CengageLearningBusiness Press, (2010) ISBN-13: 978-0-324-65175-1

Course Design • Part lecture, part skills development • Usually one major topic per day • Some time devoted to working with statistical software packages (excel and SPSS)

Course Reading Jon Curwin and Roger Slater: • Quantitative Methods for Business Decisions

Statistical Software • All course examples will use EXCEL • You can download the excel files of the course book: http://www.agr.unideb.hu/~baloghp/Montenegro

Software and Computers Bring your laptop to class if applicable. We will devote class time in many sessions to working with statistical software. I encourage you to sit with anyone who knows the MS EXCEL software package when we begin to use it in class.

Overall Course Goals • You will have good knowledge of common research methods used in quantitative research (surveys, experiments) • You will understand basic univariate statistics, bivariate statistics, linear regression and time series analysis • You will be able to use the MS EXCEL to conduct statistical analyses

Description of the contact education I. • Hour 1-4: Quantitativeinformation • The quantitativeapproach • Managingdata • Surveymethods • Presentation of data • Hour 5-8: Descriptivestatistics • Measures of location • Measures of dispersion • Index numbers

Description of thecontacteducation II. • Hour 9-11: Measuring uncertainty • Probability • Discrete probability distributions • The normal distribution • Statistical inference • Confidence intervals • Significance testing • Non-parametric tests

Description of thecontacteducation III. • Hour 12-15: Relating variables and predicting outcomes • Correlation • Regression • Multiple regression and correlation • Time series

1. The quantitative approach • Quantitative information: • We can get data quickly, but we need to be sure that we are working on the right problem and that the data is valid. • Data means • a few recording • an extensive national or international survey • An item of data becomes information when it informs the user.

1. The quantitative approach • Quantitative information: • Internet has transformed the flow and availability of data. • The ability to manage data, produce information and work with problems are all seen as and important business competencies.

1. The quantitative approach • Quantitative information: • Desk research: • First you need checking what work has already been done. • Provide information or identify techniques. • It is always helpful to find a questionnaire that has been used previous study and may only require some modification.

1. The quantitative approach • Quantitative information: • Managing numbers is an important part of understanding and solving problems. • The collecting together of numbers, and other facts and opinions provides data. • This data only becomes information when it informs the user!! • The quantitative approach is more than just ‘doing sums’. • It is about making sense of numbers within a context.

1. The quantitative approach • 1.1 Problem solving • 1.2 Methodology • 1.3 Models • 1.4 Measurement • 1.5 Scoring models

1.1 Problem solving • To understand problems within a context, it can be useful to work through a number of stages: • defining (and redefining) the problem, • searching for information, • problem description (and again redefinition if necessary), • idea generation, • solution finding and finaly, • acceptance and implementation.

Problem solving what we have and what we want!!!

1.2 Methodology • Old methods New methods • Reliability and validity of findings (conclusions) • Was the purpose of the research clear? • Was this research necessary? (desk research) • Was the means of data collection appropriate? • What can we infer? (-inductive approach generalization -deductive approach)

1.3 Models • 1.3.1 Model abstraction • 1.3.2 The development of a mathematical model • 1.3.3 Models of uncertainty • 1.3.4 Computer-based modelling

Modelling Transformationprocess Outcomes Inputs Assumptions

1.3 Models • A model is a representation of real objects or situations • A good understanding of the object or situation • The recognition of all relevant variables • The understanding of relationships • The ability to undertake analysis

1.3.1 Model abstraction • Physical (or iconic) • Schematic (organization charts, flowcharts) • Analogue (colours on a map: water, forest) • Symbolic (or mathematical) (numbers, letters, special characters, symbols) Least abstract Most abstract

1.3.2 The development of a mathematicalmodel • A variable is a quantity or characteristic of interest that is allowed to change within a particular problem (students’ mathematics mark, travel time) • A parameter is fixed for a particular problem. • An assumption is something we accept to be true for the model we are working on.

1.3.3 Models of uncertainty • Deterministic Stochastic (Probabilistic) • Expected value Mean

1.3.4 Computer-based modelling Least abstract • Computational (spreadsheets, ‘what if’) • Analytical (mathematical techniques and manipulation) • Simulation (equations and distributions) • Expert systems (advising on solution) Most abstract

1.4 Measurement • Measurement is about assigning a value or a score to an observation. • Measurement is the representation of • type, • size or • quantity by numbers. • How we work with data will depend on the level of measurement achieved. • Measurement can be categorized as: nominal, ordinal, interval, ratio

1.4 Measurement • Nominal (or categorical) level of measurement: • If responses merely classified into a number of distinct categories, where no order or value. • The classification of survey respondents on the basis of • religious affinity, • voting behaviour or • car ownership. • The numbers assigned give no measure of amount or importance.

1.4 Measurement • Nominal (or categorical) level of measurement: • For data processing convenience, we may code respondents 0 or 1 (e.g. YES or NO) or 1, 2, 3 (Party X, Party Y, Party Z), but these numbers do not relate to meaningful origin or to a meaningful distance. • We cannot calculate statistics (mean, standard deviation). • We can make percentage comparisons (e.g. 30 % will vote for party X), present data using bar charts or use more statistical methods (non-parametric tests).

1.4 Measurement • Ordinal level of measurement: • has been achieved when it is possible to rank order all categories according to some criteria. • The preferences indicated on a rating scale ranging from ‘strongly agree’ to ‘strongly disagree’ or the classification of respondents by social class (occupational groupings A, B, C1, C2, D, E) are both common examples where ranking is implied. • Individuals are often ranked as a result of performance in sporting events or business appraisal.

1.4 Measurement • Ordinal level of measurement: • In these examples we can position a response or a respondent but cannot give weight to numerical differences. • It is as meaningful to code a five point rating scale 7, 8, 12, 17, 21 as 1, 2, 3, 4, 5 though the latter is generally expected. • Only statistics based on order really apply.

1.4 Measurement • Ordinal level of measurement: • You will, however, find in market research and other business applications that the obvious codings are made (e.g. 1 to 5) and then a host of computer-derived statistics calculated. • Many of these statistics can be useful for descriptive purposes, but you must always be sure about the type of measurement achieved and its statistical limitations. !

1.4 Measurement • Interval scale: • is an ordered scale where the differences between numerical values are meaningful. • Temperature is a classic example of an interval scale, the increase on the centigrade scale between 30 and 40 is the same as the increase between 70 and 80. • However, the heat cannot be measured in absolute terms (0 oC does not mean no heat) and it is not possible to say that 40 oC is twice as hot as 20 oC, but we can say it is hotter. • In practice there are few business-related measurements where the subtlety of the interval scale is of consequence.

1.4 Measurement • Ratio scale: • The highest level of measurement, - which has all the distance properties of the interval scale and in addition, - zero represents the abscence of the caracteristic being measured. • Distance and time are good examples. • It is meaningful, for example, to refer to 0 time and 0 distance and refer to one journey taking twice as long as another journey or one distance as being twice as long as another distance.

1.4 Measurement

1.4 Measurement • In summary, it is considered more powerful to achieve measurement at higher level as this will contain more discriminating information; • it is more useful to know how many cigarettes a respondent smokes on average (0 or more) than just whether they smoke or not. • The measurement sought will depend on the purpose of the research.

1.4 Measurement • Another useful system of classification is whether measurement is discrete or continuous. • Measurement is discrete if the numerical value is the consequence of counting. (the number of respondets, the number of companies) • Continuous measurement can take any value within a continuum, limited only by the precision of the measurement instrument. (5 seconds or 5.17 seconds)

Quantitative and Qualitative Perspectives • "There's no such thing as qualitative data. Everything is either 1 or 0“ • Fred Kerlinger • "All research ultimately has a qualitative grounding“ • Donald Campbell

Quantitative and Qualitative Perspectives • First it is useful to distinguish between the quantitative and qualitative approaches to problem solving. • Essentially, the quantitative approach will describe and resolve problems using numbers. • Emphasis will be given to: • the collection of numerical data, • the summary of that data and • the drawing of conclusions from data. • Measurement is seen as important and factors that cannot be easily measured, such as attitudes and perceptions, are generally difficult to include in the analysis.

Quantitative and Qualitative Perspectives • Qualitativeapproaches describe the behaviour of people individually, in groups or in organisations. • Description is difficult in numerical terms and is likely to use illustrative examples, generalization and case studies. • The qualitative approach can use a variety of methods such as observation and the written response to unstructured questions. • Data may come in the form of script, for example, transcripts of interviews or observations such as video recordings.

1.5 Scoring models • Scoring models provide a way of combining such information and informing decision-making. • Can provide a useful basis for thinking about the problem

2. Managingdata „The truth is out there somewhere”

2.1 Issues of data collection • The fiveW’s and H technique: • Who?, What?, Where?, When?, Why? and How? • Who? is an importantquestioninanyproblem. Data willalwaysrelateto a particulargroup of peopleorsetofitemsintime and weusethisconcepttodefinethepopulationwewill be working. • The population is definedasthosepeopleoritems of interest. • Given limited resources, includingtime, theidentification of therelevantpopulationis essential. Women Man

2.1 Issues of data collection (cont.) • Having decided who, we must then consider whether we need information on all of them or just a selection. • A census is a complete enumeration of all those people or items of interest (whereas a sample is just a selection from all those people or items).

2.1 Issues of data collection (cont.) • What? datawilldependonthepurpose of theresearch. • A statisticalenquirymayrequirethecollection of newdata, referredtoasprimarydata, or be abletouseexistingdata, referredtoassecondarydata. (Combination of bothsources.) • Sources of primarydataincludeobservation, groupdiscussions and theuse of questionnaires. Collectionfor a specific project. • Take a longtimetocollect, • Be expensive

2.1 Issues of data collection (cont.) • Secondary data has been collected for some other purpose. • Low cost but may be inadequate for purposes of the inquiry. • Example: The impact of a new shopping center on the local community!! • First step Second step

2.1 Issues of data collection (cont.) • Where? tofindthe right kind of datawhenyouneeditorwheretofindthepeopleof interest whenyouneedthem is an importantskill. • Inorganizationalresearchit is oftenusefultodistinguishbetweeninternal and externallygenerateddata. Recentsalesvolume, salesvalue, number of employees, expenditureonadvertising, expenditureonresearch The datageneratedbynationalgovernments, local governments, chambers of commerce, Internet Youstillneedtoquestionitsvalidity and reliability. Thisstage of searchingfordata is oftenreferredtodeskresearch.

2.1 Issues of data collection (cont.) • Why? is seen as part of a questioning approach that should lead to greater clarification and a justification of approach. Useful technique called the why technique . • Why did you say that? • Why should that be the case? • Why use that data?

2.1 Issues of data collection (cont.) • Chapter 2 and 3 areparticularlyconcernedwiththehow? • Havingdefinedthepopulation of interest and thepurpose of theresearch, a number of issueswillneedto be addressed: • Whetherexistingpublishedsourcesprovidesufficientinformation • Whetherusefulinformationcan be foundthrough an Internet search • Whattype of samplingshould be used, ifany • Howdatashould be collected • Howquestionsshould be designed, ifrequired

2.2 Published sources Office for National Statistics (ONS) • The Annual Abstract of Statistics • The Monthly Digest of Statistics • Financial Statistics • Economic Trends The Economic and Labour Market Review • Social Trends

Applied Quantitative Methods MBA course Montenegro