# How to Determine the Sample Size in Survey Research?

## Introduction

Selecting a suitable sample size is essential for deriving reasonable inferences from study results. While there are a number of commonly used guidelines for calculating sample size, researchers are still unsure which guideline to use for their individual investigations. â€˜How large should the sample be?â€™ is one the most frequently asked questions in survey research.Â

The focus of the video tutorial is on: First, discuss the factors that influence sample size decisions. Second, review existing rules of thumb related to the calculation of sample size and also perform power analysis using the G*Power program. In this insightful video, we delve into the complexities of determining sample size for survey research, a perennial challenge for social science researchers.Â

I use the paper titled “Sample size for survey research: Review and recommendations” published in the Journal of Applied Structural Equation Modeling,” that succinctly encapsulates crucial considerations for estimating sample size. Exploring factors such as the nature of research, statistical analysis types, and the influence of research supervisors, the video provides valuable insights for young scholars and those working on papers or theses.Â

## Learn Determine the Sample Size in Survey Research

### Learn to Determine the Sample Size in Survey Research

Sample size calculation poses a persistent challenge for researchers in the social sciences, particularly when conducting survey research. The quest for an appropriate sample size often perplexes young scholars and those embarking on research endeavors. In this tutorial, we will explore essential considerations and guidelines to help scholars estimate an optimal sample size for survey research.

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#### Nature of Research and Statistical Analysis

Understanding the nature of your research is paramount. For instance, conducting research on top executives in organizations demands a tailored approach. The sample size will inevitably vary based on the targeted individuals or entities. Moreover, the type of statistical analysis employed plays a crucial role. Exploratory factor analysis requires a minimum of 50 observations, while simple regression analysis necessitates at least 100 samples in most scenarios.

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#### Choice of Analysis Program

The choice of analysis program, such as AMOS, Mplus, LISREL, or SmartPLS, can influence the required sample size. It is imperative to dispel the misconception that certain tools, like PLS SEM, possess magical capabilities for handling small sample sizes. Understanding the nuances of your chosen analysis program is crucial to ensure accurate results.

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#### Supervisory Influence

Research supervisors often advocate for larger sample sizes to enhance generalizability. However, scholars should exercise caution, as excessively large sample sizes may lead to statistically significant yet unreliable results, introducing the risk of type one errors. It is vital to align the sample size with the study’s objectives and analytical techniques.

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#### Existing Rules and Guidelines

Several established rules and guidelines aid in determining sample sizes. The sample-to-item ratio, often recommended for exploratory factor analysis, suggests a ratio not less than 5:1. Additionally, sample-to-variable ratios, Krejcie and Morgan tables, and various online calculators provide valuable insights. Researchers must be mindful of the assumptions inherent in these approaches.

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#### Guidelines for PLS SEM

For Partial Least Squares Structural Equation Modeling (PLS SEM), the conventional “10 times rule” suggests the minimum sample size should be ten times the larger number of formative indicators. However, recent alternatives like the inverse square root method and gamma exponential method offer more nuanced approaches, considering factors like effect size and measurement item reliability.

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