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

1. Market Orientation

2. Strategic Orientation

3. 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

1. Establish the need for information

2. Specify research objectives

3. Determine research design

4. Develop data collection procedure

5. Design sample

6. Collect data

7. Process data

8. Analyze data

9. 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:

  1. Problem of choice

  2. 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

1. Exploratory Research:

   • Generates hypotheses or understanding of dimensions.

   • Qualitative Methods: Focus groups, interviews.

2. 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

1. Telephone Surveys

2. Personal Surveys

3. Mail Surveys

4. In-Home Surveys

5. Mall Intercept Surveys

6. 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

1. Nonprobability Sampling: Convenience, Judgmental, Quota, Snowball.

2. Probability Sampling: Systematic, Stratified, Cluster, Simple Random.

Page 20: Data Collection Method Functions

Functions in Data Collection

1. Selection of field workers

2. Training of field workers

3. Supervision of field workers

4. Validation of field workers

5. 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.