Revision slides HE study 2020
Page 1: Introduction to Quantitative Research
Overview
Course Title: Quantitative Research Revision
Instructor: Dr. Jonathan P. Ivy
Institution: Lancaster University
Part Identifier: Part 1
Page 2: Key Components of Research Design
Important Elements
Firm Objective
Information Requirement
Problem Definition/Objectives
Data Availability Assessment
Secondary Data Utilization
Research Design
Data Analysis
Presentation
Secondary Research Aspects
Advantages and Disadvantages
Sources of Secondary Data
Types of Problems Solvable with Secondary Data
Market Orientation
Strategic Orientation
Problem Orientation
Data Preparation Techniques
Data Manipulation Techniques:
t-tests and Cross Tabs
Chi-square
Experimental Design and ANOVA
Multivariate Techniques
Cost-Benefit Analysis
Types and Sources of Bias
Frequency and Ease of Use
Country or Regional Specific Bias
Issues in Primary Data Collection
Page 3: The Research Process Steps
Steps in Research Process
Establish the need for information
Specify research objectives
Determine research design
Develop data collection procedure
Design sample
Collect data
Process data
Analyze data
Present research results
Page 4: Identifying the Need for Information
Critical Analysis for Research Success
Importance of preliminary analysis leading to the research decision.
Types of problems to identify:
Problem of choice
A symptom
Page 5: Research Objectives Specification
Steps to Define Research Objectives
List necessary information needed (BADI).
Brainstorm information requirements and acquisition methods.
Examples of Objectives
To determine...
To identify...
Page 6: Defining the Research Objective
Key Considerations
Information-oriented approach.
Identify needed information and feasible acquisition methods.
Purpose Determination
Management Decision Problem: Improve store patronage at Orchard St Branch.
Market Research Objective: Identify strengths and weaknesses of Orchard St Branch compared to competitors.
Page 7: Research Design and Data Collection
Research Design Importance
Acts as a blueprint for conducting research.
Details procedures to achieve research objectives.
Page 8: Research Design Classification
Categories of Research Design
Exploratory Research:
Generates hypotheses or understanding of dimensions.
Qualitative Methods: Focus groups, interviews.
Conclusive Research:
Gathers definitive data to inform decisions.
Quantitative Methods: Surveys (mail, telephone, etc.).
Page 9: Classification of Secondary Data
Types of Secondary Data
Internal Data vs. External Data
Ready to Use vs. Requires Further Processing
Published Materials
Computerized Databases
Syndicated Services
Page 10: Comparing Qualitative and Quantitative Research
Qualitative Research
Purpose: Understand underlying motivations.
Characteristics: Small non-representative sample, unstructured.
Quantitative Research
Purpose: Quantify data, generalize results to population.
Characteristics: Large representative sample, structured.
Page 11: Exploratory Research Design Techniques
Main Qualitative Research Methods
Focus Groups
In-depth Interviews
Projective Techniques
Observation
Page 12: Purpose of Conclusive Research
Goal of Conclusive Research
Designed to evaluate alternatives for decision-making.
Page 13: Survey Methods Classification
Types of Survey Methods
Telephone Surveys
Personal Surveys
Mail Surveys
In-Home Surveys
Mall Intercept Surveys
Electronic Surveys (E-mail, Internet)
Page 14: Measurement in Data Collection
Measurement Defined
Assigning numbers or symbols to characteristics based on rules.
Focus on measuring consumer perceptions rather than individuals.
Page 15: Questionnaire Design for Quantitative Studies
Elements of a Questionnaire
Structured Format: Fixed questions with tick boxes.
Importance of response formats and scaling in design.
Page 16: Guides for Qualitative Studies
Design of Qualitative Guides
Open-ended Questions: Avoid one-word responses.
Use prompts and adjust between interviews as needed.
Page 17: Sampling in Research
Importance of Sampling
Complete censuses are often impractical due to population size.
A sample is a subgroup selected for study.
Page 18: Sample vs. Census
Distinction
Census: Complete enumeration of the population.
Sample: Subgroup selected for research purposes.
Page 19: Sampling Techniques Classification
Types of Sampling Techniques
Nonprobability Sampling: Convenience, Judgmental, Quota, Snowball.
Probability Sampling: Systematic, Stratified, Cluster, Simple Random.
Page 20: Data Collection Method Functions
Functions in Data Collection
Selection of field workers
Training of field workers
Supervision of field workers
Validation of field workers
Timing and budgeting considerations
Page 21: Importance of Data Analysis
Analysis in Research
Data analysis as a critical aspect, but other factors matter more.
The importance of problem definition, methodology, and data processing highlighted.
Page 22: Tips for Data Analysis
Key Aspects of Analysis
Identify trends, patterns, and exceptions in data.
Statistical software (e.g., SPSS) significantly aids the analysis process.
Page 23: Qualitative Data Analysis
Challenges in Qualitative Analysis
Handling large volumes of data (audio, videos, transcripts).
Techniques for simplification: basic quantification and restructuring.
Page 24: Quantitative Analysis Overview
Data Description Techniques
Use of frequencies, mean scores, and standard deviations.
Inferential Tools
Chi-squared tests, t-tests, ANOVA, correlations, regression, multivariate analysis (e.g., factor analysis, discriminant analysis).
Page 25: Reporting Research Findings
Considerations in Reporting
Consider the audience: clarity in graphs and definitions.
Address information needs relating to objectives.
Maintain objectivity and conciseness in reports.
Page 26: Example-Based Revision
Contextualized Revision
Title: Revision Using an Example
Presenter: Dr. Jonathan P. Ivy
Institution: Lancaster University
Page 27: Task Overview for Students
Assignment Context
Role: International Recruitment Manager for Major University in the UK.
Task: Present findings and recommendations on international recruitment strategy.
Page 28: Interpretation of Univariate Data
Summary of Preferences
Respondents (669 total): 44.1% male, 55.9% female.
63.5% prefer to study abroad instead of at home (given no limitations).
Page 29: Bivariate Cross-Tabulation
Crosstabulation of Gender and Country Preference
Analyzed preference for home country in university selection by gender.
Chi-Square Tests Results
Male: 49.6% yes, Female: 50.4% yes.
Significance of results shown in table with chi-square score and significance level identified.
Page 30: Gender Differentiated Strategy Interpretation
Insights from Gender Preferences
Both genders indicated a preference for studying abroad.
Significant difference: females (67%) are more likely than males (59%) to choose studying abroad.
Consider developing gender-specific strategies in marketing and school visits.
Page 31: Univariate Gender and Education Data
Education Background of Participants
Breakdown of respondents: 44% male, 56% female.
Educational levels: 13% in school, 63% in Bachelors, 25% in Post Graduate.
Page 32: Bivariate Crosstabulation of Education and Gender
Crosstabulation Results
Gender vs. Education level cross-tabulated.
Chi-Square Test Value and Interpretation
No significant difference found between genders across educational qualifications.
Page 33: Interpretation of Education Qualifications
Findings and Implications
No significant differences between genders regarding education qualification.
Marketing strategies do not need differentiation based on education profile.
Page 34: Group Statistics Overview
Gender Differences in Opinions
Statistical representation of differences in perceptions about studying in the UK.
Page 35: Independent Samples T-test Overview
Statistical Testing Process
T-tests used to compare means between genders on various statements regarding UK studies.
T-values, significance levels, and p-values noted across assessment statements.
Page 36: Correction of Statistical Tables
Need for Clarity in Reporting
Importance of clear and well-structured statistical representation.
Adjustments to charts and tables to enhance communication of results.
Page 37: Interpretation of Gender Comparison
Analysis of Group Responses
No significant differences in likelihood of acceptance at UK universities.
Discussion on parental influence and ranges of responses among genders noted.
Page 38: Parental Attitudes Interpretation
Insights on Gender Attitudes
Gender perceptions on parental desire indicate significant differences.
Opportunities for targeted marketing based on parental involvement highlighted.
Page 39: Statistical Findings and Marketing Opportunities
Recommendations for Marketing Strategies
Gender-specific marketing activities recommended based on attitudes found.
Page 40: Purpose of One-way ANOVA
Understanding F-tests in ANOVA
Significance determination amongst variable comparisons and the focus on post hoc tests.
Page 41: Comparing Standard Deviations
Role of Levene's Test in Comparative Analysis
Comparison of standard deviations between different groups highlights differences.
Page 42: ANOVA in Career Decision Confidence
Analysis of Educational Confidence Levels
Significant differences noted in confidence across educational levels.
Page 43: Leisure Versus Work Balance Perceptions
Analysis of Expectations Across Educational Categories
Similarity in leisure expectations across different educational groups without significant differentiation.
Page 44: Career Break Perceptions
Differences in Career Break Motivation
School leavers less likely to view university as a break; suggests marketing should focus on career development.
Page 45: Correlation Findings
Positive Correlations Identified
Strongest correlation with teaching quality in relation to likelihood to apply.
Page 46: Regression Analysis Overview
Key Statistical Outputs
Highlighting the explanatory power (R-square) of independent variables in predicting likelihood to apply.
Page 47: Regression ANOVA Insights
Importance of F-value
Discussion on the significance of the F-value in validating regression models.
Page 48: Regression Model Specification
Components of Regression Model Specification
Clarity on defining the model and independent variables impacting the likelihood to apply.
Page 49: Impact of Independent Variables
Focus on Relevant Education's Impact
Discussion on how relevant education significantly influences willingness to apply to a UK university.
Page 50: Expected Likelihood Scores
Future Predictions Based on Changes in Ratings
Forecasting impact of strategic improvements on applicant likelihood metrics.
Page 51: Testing Expectations
Closing Remarks on Research Tests
Emphasis on clear communication and suitability of responses in report compilations.
Page 52: Conclusion and Best Wishes
Final Remarks
Encouraging note for students and well-wishes for upcoming breaks.