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Sampling Plans

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...

Sample Proportion and Population Proportion

Sample Proportion and Population Proportion

Collecting samples or responses from an entire population takes significant time and effort, so a researcher collects responses from only a sample of that population. Suppose a study needs to collect information about a specific mobile application. After sample collection, the researcher analyzes the data and discovers that most individuals in the sample use that specific mobile application. The sample proportion measures the number of individuals in a sample who either use or don't use the...

Stratified Sampling Method

Stratified Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...

Cluster Sampling Method

Cluster Sampling Method

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To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...

Selected Data About Geographic Locations

Selected Data About Geographic Locations

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Analysis of Population Pharmacokinetic Data

Analysis of Population Pharmacokinetic Data

Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...

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Related Experiment Video

Updated: May 21, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

Walking distance by trip purpose and population subgroups.

Yong Yang¹, Ana V Diez-Roux

¹Department of Epidemiology, Center for Social Epidemiology and Population Health, University of Michigan, Ann Arbor, Michigan 48109, USA. yongyang@umich.edu

American Journal of Preventive Medicine

|June 19, 2012

Summary

Most U.S. residents do not walk daily, but many walking trips exceed 0.25 miles. Walking distances and durations vary significantly by purpose and demographics, impacting physical activity strategies.

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Area of Science:

Public Health
Transportation Science
Urban Planning

Background:

Walking distance is a key factor in transportation and public health research.
A 0.25-mile threshold is frequently used in U.S. studies, yet comprehensive data on walking trip characteristics are limited.
Understanding walking behavior is crucial for promoting physical activity and designing walkable communities.

Purpose of the Study:

To investigate the prevalence of walking among U.S. residents.
To analyze the distances and durations of walking trips across various purposes.
To identify demographic variations in walking trip characteristics.

Main Methods:

Utilized nationally representative data from the 2009 National Household Travel Survey (NHTS).
Employed distance decay functions to model walking distance and duration distributions.
Compared trip characteristics across different purposes and population subgroups.

Main Results:

Only 16% of respondents reported daily walking trips.
The mean walking distance was 0.7 miles, with a median of 0.5 miles.
Approximately 65% of walking trips exceeded 0.25 miles, and 18% exceeded 1 mile.
Recreational walking trips were significantly longer than those for other purposes.
Income influenced walking distances for work (longer for lower income) and recreation (shorter for lower income).

Conclusions:

Despite low daily walking prevalence, longer walking trips are common.
Significant variability exists in walking trip distance and duration based on purpose and demographics.
Findings inform strategies to enhance physical activity through walking interventions.