PowerPoint 6 Theme 5 Quantitative Research Approach_025f35d7475c46b89a52603270f623e1

Quantitative Research Proposal

Overview

  • Proposals for quantitative research follow a logical sequence.

  • Arrangement of topics can vary, but common formats prevail in quantitative studies.

Structure of a Proposal

A. Problem Statement

  • The problem and its setting

  • Statement of the problem and subproblems

  • Hypotheses

  • Definitions of terms

  • Assumptions

  • Delimitations and limitations

  • Importance of the study

B. Literature Review

  • Review of related literature

  • Data and the treatment of the data

C. Data Requirements

  • Types of data needed

  • Means of obtaining the data

D. Research Methodology

Treatment of Data

F. Subproblem Analysis

  • Specific treatment of the data for each subproblem

    1. Subproblem 1

    • Data needed for subproblem

    • Treatment of data

    1. Subproblem 2

    2. Additional subproblems discussed similarly

G. Researcher Qualifications

  • Qualifications of the researcher and assistants

H. Study Outline

  • Steps to be taken and timeline

I. References
J. Appendices

Key Concepts in Quantitative Research

Contrasting Approaches

  • Numerical vs. Qualitative: Quantitative is statistical, focusing on numbers while qualitative often explores words and meanings.

  • Researcher Perspectives: Distant vs. close involvement of the researcher.

  • Theory Testing vs. Emergent Theory: Quantitative often tests existing theories, whereas qualitative may develop new theories.

  • Static vs. Process: Quantitative aims for a static understanding, approach is structured rather than unstructured.

Sampling in Quantitative Research

Definitions

  • Population: Total units of interest (individuals, events, etc.).

  • Sample: Subset taken from the population that is representative.

Importance of Sampling

  • Quality of conclusions based on sample choices.

  • Decisions on sample size and selection methods essential.

Types of Sampling Schemes

1. Random Sampling Schemes

  • Simple random sampling

  • Systematic sampling

  • Stratified random sampling

  • Cluster sampling

  • Panel sampling

2. Non-Random Sampling Schemes

  • Types:

    • Accidental sampling

    • Purposive sampling

    • Snowball sampling

    • Quota sampling

    • Dimensional sampling

    • Target sampling

    • Sequential sampling

    • Key informant sampling

    • Theoretical sampling

    • Deviant sampling

    • Volunteer sampling

Non-Probability Sampling Techniques

  • Haphazard Sampling: Selection based on convenience, often leads to unrepresentative samples.

  • Quota Sampling: Improving over haphazard; fixed categories for participant numbers.

  • Purposive Sampling: Judgment-based selection for specific situations, useful in hard-to-reach populations.

  • Snowball Sampling: Starts with initial cases, expanding through networks.

Probability Sampling

  • Each unit has a specified chance of selection; aims for representativeness.

  • Random sampling does not ensure perfect representation but provides a reliable approximation of the population.'