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Bias

Bias

Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...

Bias in Epidemiological Studies

Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:

Systematic Error: Methodological and Sampling Errors

Systematic Error: Methodological and Sampling Errors

In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...

Surveys

Surveys

Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally. Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.

Scientific Nature of Social Psychology

Scientific Nature of Social Psychology

Social psychology is a scientific discipline dedicated to understanding how individuals think, feel, and behave in social contexts. Unlike common sense, which relies on anecdotal experiences and intuition, social psychology employs systematic research and empirical methods to ensure objectivity and reliability. This distinction is fundamental in distinguishing scientifically supported findings from mere speculation.Four fundamental scientific values guide a structured approach to research in...

Ethics in Research

Ethics in Research

Today, scientists agree that good research is ethical in nature and is guided by a basic respect for human dignity and safety. However, this has not always been the case. Modern researchers must demonstrate that the research they perform is ethically sound.

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Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

Published on: May 31, 2019

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries.

Alexandra Olteanu^1,2, Carlos Castillo³, Fernando Diaz²

¹Microsoft Research, New York, NY, United States.

Frontiers in Big Data

|March 11, 2021

Summary

This summary is machine-generated.

Social data offers valuable insights but is prone to biases and inaccuracies. This paper introduces a framework to identify and navigate the various challenges in social data analysis.

Keywords:

biases ethics evaluation social media user data

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Area of Science:

Social Computing
Data Science
Digital Sociology

Background:

Digital social data, including user-generated content and behavioral traces, fuels popular platforms and research.
Social data promises insights into public opinion and improved decision-making across policy, healthcare, and economics.

Purpose of the Study:

To identify and categorize the various challenges and "menaces" associated with the use of social data.
To propose a framework for recognizing and addressing the limitations and ethical considerations in social data analysis.

Main Methods:

Qualitative analysis of existing research and practices concerning social data.
Development of a conceptual framework to organize identified issues in social data utilization.

Main Results:

Social data usage is fraught with potential biases, inaccuracies, and methodological pitfalls.
Ethical boundaries and unintended consequences are frequently overlooked in social data practices.

Conclusions:

A structured approach is necessary to illuminate, rather than solely solve, the complex problems in social data analysis.
Researchers must be aware of the limitations and potential pitfalls when interpreting and applying insights derived from social data.