Basic-Research-and-Statistical-Tool
Page 1: Basics of Research and Statistical Tools
Page 2: Definition of Research
Research comprises creative work undertaken systematically to increase knowledge.
Can be qualitative, quantitative, descriptive, or experimental.
Includes knowledge of humanity, culture, and society.
Aims to devise new applications based on accumulated knowledge.
Page 3: Basic Parts of Research
Table of Contents
Title Page
Abstract
Approval Sheet
Acknowledgment
List of Tables
List of Figures
Chapters:
Chapter I - The Problem and Its Background
Rationale
Research Framework
Statement of the Problem
Hypothesis
Significance of the Study
Scope and Limitations of the Study
Definition of Terms
Chapter II – Review of Related Literature
Chapter III – Research Methodology
Research Design
Respondents of the Study
Research Instrument
Data Gathering Procedure
Statistical Treatment
Chapter IV – Presentation, Analysis and Interpretation of Data
Chapter V – Summary, Conclusion, and Recommendation
Bibliographies
Appendices
Curriculum Vitae
Page 4: Collaborative Research
LIF encourages collaborative research with a team of 3 or 4 members.
Page 5: Chapter I - Rationale
Presents the problem and the reasons for conducting the study.
Example: Strengthening the culture of research at LIF.
Page 6: Chapter I - Research Framework
Theoretical Framework: Concepts proven by established theories that are useful to the study.
Conceptual Framework: Researcher's own position on the problem, influenced by existing theories.
Page 7: Chapter I - Research Paradigm
Input: Independent variables
Process: Moderating/intervening variables
Output: Dependent variables
Page 8: Chapter I - Statement of the Problem
States the general problem followed by specific questions (sub-problems).
Page 9: Chapter I - Example of a Statement of the Problem
Investigating ways to strengthen the research culture in LIF.
Includes sub-problems to seek answers.
Page 10: Chapter I - Research Hypothesis
A scientific assumption.
Required only in experimental/inferential studies.
Example: "There is no significant difference/relationship between..."
Page 11: Chapter I - Scope and Limitations of the Study
General Purpose: Strengthen research culture in LIF
Subject Matter: Research culture, capability building, costs, benefits, incentives
Population: All faculty and students.
Page 12: Chapter I - Significance of the Study
Benefits and beneficiaries: Administration, Faculty, Students, Parents, and Future Researchers.
Page 13: Chapter I - Definition of Terms
Definition of terms, key words, or phrases with unique meanings in the study.
Page 14: Chapter II - Review of Related Literature
Page 15: Chapter II - Criteria for Materials
Materials must be:
Recent
Objective and unbiased
Relevant
Minimum of 10 sources.
Page 16: Chapter III - Research Methodology
Overview of methodologies used in research.
Page 17: Chapter III - Research Design
Types of designs:
Historical
Descriptive
Experimental
Respondents of the Study: Includes sampling and sampling design.
Slovin’s formula: n = N / (1 + Ne²)
Where: n = sample size, N = population, e = error.
Page 18: Chapter III - Research Instrument
Describes the tools used to measure variables.
Page 19: Chapter III - Data Gathering Procedure
Explanation of data collection methods and instrument development.
Page 20: Chapter III - Statistical Treatment of Data
Statistical treatment depends on the nature of the problems and specific gathered data.
Page 21: Importance of Statistics
Statistics are indispensable.
Essential for valid and reliable data.
Helps in providing meaning and interpretation of data.
Page 22: Guidelines for Statistical Procedures
Organize data based on desired outcomes.
Page 23: Guidelines for Statistical Procedures
1.1 Talligram (tabulation table)
1.2 Score or frequency distribution
1.3 Scattergram
Page 24: Proportions and Population Variables
When certain proportions based on variables are desired.
Page 25: Frequency Counts and Percents
Use frequency counts, percentages, textual, tabular, or diagrammatic presentations.
Page 26: Typical or Norm Average
Use the mean for typical or average values.
Page 27: Continuous Variables Measurement
Use weighted mean for abstract or continuous variables.
Page 28: Variability Measurement
Use standard deviation when measuring population variability.
Page 29: Relative Placement Measurement
Use ranking, quartile deviation, and percentile rank.
Page 30: Significance of Responses
Use chi-square for significance of responses toward an issue.
Page 31: Difference between Two Groups
Use chi-square for significance of responses of two distinct groups.
Page 32: Variability Correlation
Use correlation coefficients to determine variable variation.
Page 33: Significance Testing
Use t-tests for significance between perceptions of two groups.
Page 34: Effectiveness of Different Methods
Use ANOVA for comparing different methods applied in randomized groups.
Page 35: Effects of Multiple Variables
Use partial or multiple correlations for effects of several variables.
Page 36: Association Between Two Variables
Use chi-square for assessing independence/association.
Page 37: Summary of Basic Tools
Overall Performance Metrics:
Mean or Average
Homogeneity and Heterogeneity
Standard Deviation
Significant Difference (t-test, z-test, ANOVA)
Relationship or Association (Chi-square, Correlation, Regression).
Basic Tools: Frequency, Percentage, Weighted Mean, Ranking.
Page 38: Chapter IV - Presentation, Analysis, and Interpretation of Data
Page 39: Techniques for Data Organization
1. Talligram
Page 40: Faculty Development Program Assessment
Response options analyzed on a scale from Not a Factor (1) to Very Serious Factor (5).
Page 41: Frequency Distribution Table for Faculty Development Program
Page 42: Techniques in Organizing Data
2. Frequency and Percentage
Page 43: Age Frequency and Percentage Distribution
Breakdown of respondent ages and calculation of percentages.
Page 44: Age Frequency Distribution
Page 45: Age Frequency Distribution Continued
Page 46: Age Frequency Distribution Continued
Page 47: Age Frequency Distribution Continued
Page 48: Age Frequency Distribution Continued
Page 49: Graphical Representation of Age Distribution
Figure: Percentage Distribution of Respondents' Age.
Page 50: Educational Qualification Assessment
Weighted mean analysis using factor scale.
Page 51: Educational Qualification Frequency Distribution Table
Page 52: Interval Scales for Qualification Factors
Page 53: Educational Qualification Analysis
Page 54: Educational Qualification Analysis Continued
Page 55: Educational Qualification Analysis Continued
Page 56: Educational Qualification Analysis Continued
Page 57: Educational Qualification Analysis Continued
Page 58: Educational Qualification Analysis Continued
Page 59: Educational Qualification Analysis Continued
Page 60: Educational Qualification Summary
Page 61: Interval Scale Calculations
Page 62: Techniques in Organizing Data - Ranking
Page 63: Ranking of Variables Based on Responses
Page 64: Techniques in Organizing Data - Scattergram
Page 65: Graphical Data Representation of Experience
Page 66: Textual Presentation of Age Distribution
Percentage distribution of the respondents' age.
Page 67: Tabular Presentation of Age Data
Page 68: Graphical Presentation of Age Data
Page 69: t-test for Significant Differences
Page 70: z-test Application
Page 71: ANOVA Analysis
Page 72: Testing Relationships - Chi-square
Page 73: Testing Relationships - Correlation
Page 74: Correlation Interpretation
Scale for interpreting correlation coefficients.
Page 75: Correlation Examples
Page 76: Regression Analysis Basics
Basic regression equation format.
Page 77: Important Note
"In Statistics, don't lie. But lies do statistics."
Page 78: Chapter V - Summary, Conclusion, Recommendation
Page 79: Guidelines for Presenting Findings
Findings should contain specific results without explanations.
Page 80: Findings Presentation Guidelines
Structured presentation of data.
Page 81: Cohesive Data Structure Guidelines
Page 82: Textual Presentation in Data Reporting
Page 83: Example of Age Distribution Presentation
Page 84: Analysis and Interpretation Definitions
Distinction between analysis and interpretation.
Page 85: Types of Research Analysis
Univariate, Bivariate, Multivariate Problems.
Page 86: Univariate Problem Example
Page 87: Bivariate Problem Example
Page 88: Multivariate Problem Example
Page 89: Levels of Interpretation of Data
Table reading, implications, cross-referencing.
Page 90: Age Data Example Summary Table
Page 91: Age Distribution Insights
Page 92: Inference Language Examples
Page 93: Chapter V - Summary, Conclusion, Recommendation Overview
Page 94: Summary of Findings
Findings presented without extraneous details.
Page 95: Findings of the Study
Human resources status assessment.
Page 96: Conclusions Guidelines
Conclusions based on study findings.
Page 97: Findings vs. Conclusions Example
Page 98: Recommendations Guidelines
Specific and actionable recommendations.
Page 99: Forms of Recommendations
Narrative and Enumeration forms.
Page 100: Sample Findings and Conclusions
Page 101: Motivational Note for Students
Encouragement to unlock potential for research.