Course : Package Based Data Analysis

Course Code : BMKT 41242

Credit Value : 2

Status : Compulsory

Level : Level 4

Semester : Semester I

Overall Learning Outcomes : At the end of the session students should be able to;

  1. propose a cost effective tool for both quantitative and qualitative data for a given research situation
  2. assess the viability of a statistical model with the help of an appropriate statistical package and to take confident decision
  3. outline the variables identified in a spreadsheet to make marketing related business decisions supported by a package
  4. recommend appropriate statistical model to be deployed in the package environment based on a given context
  5. demonstrate confidence in using both quantitative and qualitative packages in organizational environment
  6. prepare reports based on the outputs generated by statistical and qualitative software packages
  7. internalize the ability of adopting to any packaged based data analysis environment

Course Content         :

Topic No

Topic

Learning Outcome

Teaching and Learning Method

Method of Assessment

1

Introduction to quantitative data analysis environment

At the end of the session students  should be able to;

  1. compare different quantitative analysis packages available for data analysis in social science research environment
  2. propose suitable quantitative data analysis package to analyze variables in a questionnaire for a given organizational context
  3. develop spreadsheets for the current working environment by importing data from databases, text files or from another spreadsheet
  4. evaluate the spreadsheet’s suitability to conduct analysis
  5. develop data sheets by using codes for a given situation of a data analysis
  6. internalize the quantitative data analysis philosophy to make better marketing related business decisions
  7. demonstrate package customization skill to work in aesthetic working environment and to produce customized outputs


Lectures

Laboratory experiments

End Semester Examination

2

Quantitative Data Analysis-

Part I

At the end of the session students should be able to;

  1. illustrate scales of the variables included in spreadsheet pertaining to the analysis
  2. investigate the importance of data cleaning in conducting quantitative analysis
  3. compare univariate, bivariate and multivariate data analysis techniques available for analyzer in decision making environment
  4. evaluate theoretical assumptions underlying univariate, bivariate and multivariate analysis techniques
  5. propose suitable analysis techniques for the research instrument by observing the variables in the instrument
  6. develop descriptive statistics pertaining to an identified variable existing in a spreadsheet using a statistical package
  7. produce graphical representation of individual variables by using a package for easy decision making process
  8. display sound interpretation ability of univariate statistics generated by a package for a given marketing context


Lectures Laboratory experiments

Individual Assignment

End Semester Examination


3

Quantitative  Data Analysis-

Part II

At the end of the session students  should be able to;

  1. assess the assumptions of bivariate analysis with the help of example data sheet
  2. prepare variables using packages to conduct bivariate analysis
  3. internalize the assumptions and check them before conducting bivariate analysis for a given data set.
  4. differentiate the effect of missing data and non-missing data situations on an analyzing output generated by a package
  5. evaluate the measurement ability of variables by conducting reliability analysis with the help of statistical package
  6. calculate parametric and non-parametric bivariate statistics for a given situation
  7. display sound interpretation ability of multivariate statistics generated by a package for a given marketing context


Lectures Laboratory experiments

Individual Assignment

End Semester Examination


4

Quantitative Data Analysis-

Part III

At the end of the session students should be able to;

  1. demonstrate an understanding of different multivariate statistics suitable for different marketing related decisions
  2. assess the assumptions pertaining to the recommended analysis technique with the help of a package
  3. modify the spreadsheet to meet the assumptions to conduct the desired multivariate analysis for a given data set
  4. recommend suitable multivariate analysis technique pertaining to the interested variables in a spreadsheet
  5. display sound interpretation ability of multivariate statistics generated by a package for a given marketing context
  6. analyze the output pertaining to selected multivariate statistic generated by the package


Lectures Case studies Laboratory experiments

Individual Assignment

End Semester Examination

5

Qualitative Data Analysis

At the end of the session students should be able to;

  1. appraise the importance of packages on analyzing the qualitative data in marketing research context
  2. develop an understanding of transcribing techniques and practice with a selected tool
  3. examine different coding styles, different annotations and memos available in qualitative data analysis package environment
  4. develop a searching process to test the ideas and theories with the help of tools available in the package
  5. illustrate concepts with the help of data analysis tools available in the package environment
  6. practice the uncovering process of qualitative data based on the data analysis technique chosen


Simulation and Games

Group Assignment End Semester Examination

6

Writing a Research Report

At the end of the session students should be able to;

  1. outline the contents of a research report pertaining to marketing related decisions
  2. demonstrate the industry standards on reporting output generated from different analysis techniques
  3. internalize the reporting process of outputs pertaining to each analysis technique
  4. analyze the decision making process based on quantitative and qualitative outputs generated by packages


Guest Lectures

Individual Assignment End Semester Examination

Recommended Reading       :

  1. Aldrich, J. O. and Rodriguez, H. M. (2012),"Building SPSS Graphs to Understand Data", Sage.
  2. Bazeley, P. and Jackson, K. (2013),"Qualitative Data Analysis with NVivo", SAGE Publications.
  3. Diamantopoulos, A.andSchlegelmilch, B. B. (1997),"Taking the Fear Out of Data Analysis: A Step-By-Step Approach", Dryden Press.
  4. Field, A., Miles, J. and Field, Z. (2012)," Discovering Statistics Using R", SAGE Publications.
  5. Field, A. (2013), "Discovering Statistics using IBM SPSS Statistics", SAGE Publications.
  6. Friese, S. (2012),"Qualitative Data Analysis with ATLAS.ti", SAGE Publications.
 

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