Población, muestra y muestreo de estudio - Seminario de Tesis

Understanding Population and Sampling in Research

Introduction to Population and Sample

• The session aims to define key concepts such as population, sample, and sampling within the context of research projects.

• Participants should be able to specify these terms clearly in their research projects by the end of the session.

Key Topics Covered

• The discussion will include:

• Definition of population

• Limitations of population

• Sample selection methods

• Types of sampling techniques with examples

Case Study Analysis

• A scenario is presented where a study aims to understand why 1,000 workers left their jobs in 2015.

• This group represents the study's population, defined as all individuals or elements that exhibit a specific characteristic relevant for study (e.g., all private company administrators).

Defining Population and Exclusion Criteria

• Precise definition of the population is crucial; this process is known as delimitation.

• Researchers must establish inclusion criteria (who will be included) and exclusion criteria (who will not), such as excluding new employees or illiterate workers from a job satisfaction survey.

Sampling Techniques Explained

• If the target population is too large, researchers may opt for sampling instead of surveying everyone.

• A sample is a subset selected through specific sampling techniques; it can be probabilistic or non-probabilistic.

Importance of Sampling

• Sampling saves resources like time and money when dealing with large populations.

• Generalization from sample results to the entire population depends on how representative the sample is, which relies on proper random sampling methods.

Types of Sampling Methods

Probabilistic Sampling Techniques

• Probabilistic sampling ensures every individual has an equal chance of being selected. It includes:

• Simple random sampling: Every member has an equal probability.

• Stratified random sampling: Population divided into homogeneous groups (strata).

Systematic and Cluster Sampling

• Systematic sampling involves selecting one individual randomly then choosing others at regular intervals.

• Cluster sampling divides the population into sections (clusters), randomly selects some clusters, and surveys all members within those clusters.

Non-probabilistic Sampling Techniques

• Non-probabilistic methods do not rely on random selection; they are based on researcher judgment or convenience. Examples include:

• Convenience sampling: Selection based on ease rather than randomness.

This structured approach provides clarity on essential concepts related to research methodology concerning populations and samples while ensuring easy navigation through timestamps for further exploration.

Sampling Techniques in Research

Overview of Sampling Methods

• The term "intencional" (intentional sampling) refers to a method where the researcher relies on their experience within their research area.

• Quota sampling is noted as having the least bias among non-probabilistic methods and often serves as the first stage in stratified sampling. An example includes studying hygiene habits among schoolchildren, where each quota represents a school.

• Snowball sampling is highlighted for its use in clandestine or dispersed groups lacking a sample frame. It involves identifying an initial group that can connect researchers with others sharing similar characteristics.

Examples of Sampling Approaches

• Three companies investigate attendees' purchasing power at a classical music concert using different sampling methods:

• Company 1 selects the first 50 people entering.

• Company 2 randomly chooses from those seated in the lower section.

• Company 3 randomly samples from all attendees.

• Only Company 3's results can be generalized to the entire population due to its probabilistic approach, while Companies 1 and 2 are limited to specific subgroups.

Case Studies in Sampling

• A study conducted in Trujillo involved adults aged 40-65, utilizing simple random sampling based on municipal records. Participants were incentivized with payment for participation.

• Out of approximately 15,000 letters sent, over ten thousand were followed up by phone calls, resulting in a final sample of over five thousand participants, achieving a representativeness rate of 99%.

Further Illustrative Examples

• For researching children's academic performance influenced by community characteristics in Lima, researchers select representative neighborhoods based on specific criteria deemed relevant.

• Additional examples include:

• Women under age 35 living in Comas.

• History books from a library collection.

• First-grade public school children.

• Hospitalized individuals due to accidents in 2014.

• Randomly selected products from a factory for quality checks.

Importance of Sample Size Determination

• Knowing the exact population size allows for more precise sample size calculations and resource savings during research development.

• In one case involving students at an institution with an estimated population near ten thousand, researchers applied statistical formulas considering confidence levels and error margins to determine necessary sample sizes.

Conclusions on Population Definition and Sampling Procedures

• Defining inclusion and exclusion criteria is crucial when selecting populations for research. The population encompasses all elements sharing specific characteristics relevant to the study's focus.

• Researchers must understand that sampling is extracting subsets from larger populations through either probabilistic or non-probabilistic methods. Properly selected random samples allow findings to be generalized across broader populations.