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What are Intervention Studies in Research?
Introduction Intervention studies are research studies designed to assess the effects of specific interventions or treatments on a particular population. They are conducted to evaluate the effectiveness of interventions in achieving desired outcomes and informing evidence-based practices. In this article, we will explore the relevance of intervention studies for researchers and practitioners. Overview of Intervention…
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Documentary Research: Definition, Types, Applications & Examples
Introduction Over the years, social scientists have used documentary research to understand series of events that have occurred or happened in the past. Here, they explore available recovered or existing documents and material to get information and gain insight into a research question or particular topic. In this article, we would define the concept of…
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What is Backfire Effect? Meaning, Examples, Implications & Mitigation
Introduction Have you ever noticed that sometimes when you present someone with evidence that contradicts their beliefs, they become even more entrenched in their opinions? This puzzling phenomenon is known as the backfire effect. Understanding and addressing the backfire effect is crucial for productive discussions and effective persuasion. In this article, we will delve into…
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Understanding Survey Weighting: Purpose, Methods & Implications
Introduction Survey weighting is an important aspect of survey research, and it plays a significant role in achieving accurate results which represent the true perspective of respondents. In survey research, sometimes it can be difficult to obtain samples that reflect the target audience being studied, due to factors like errors in the sampling population, non-responsiveness…
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What is Anchoring Bias? Meaning, Examples, Implications & Mitigation
Let’s say you want to buy a pair of shoes, and the salesperson tells you the price is $120. You’d most likely think you can get it at a lower price if you negotiate a little bit. Now imagine the salesperson tells you the price is $200, but then offers you a discount of $80.…
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Processing Errors in Surveys: Causes, Effects & How to Minimize
Introduction Processing errors are a significant aspect of survey research that can have a significant impact on the quality and reliability of the collected data. In order to ensure the accuracy and validity of survey results, it is crucial to understand processing errors and their effects. This blog post aims to provide a comprehensive overview…
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The Frequency Illusion in Surveys: Meaning, Examples, Implications & Mitigation
Ever noticed how as soon as you learn a new word or idea, you start seeing it everywhere? Once you discover a new concept suddenly social media, emails, and everyone around you start talking about it nonstop. There is a term for this- the frequency illusion. The frequency illusion is the perception that something is…
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What Are Brushfire Polls? Meaning, Examples & How to Conduct Them
We have seen public opinion shift dramatically as a result of a major event countless times. This opinion shifts significantly impacts important decisions such as campaign strategy, policy development, and other issues. So, how can you measure public opinion in real-time to make the right decision or policy? Brushfire polls are short and frequent surveys…
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What are Tracking polls In Surveys Research?
Introduction Tracking polls play a vital role in survey research by providing valuable longitudinal data and trend analysis. These polls are essential for monitoring changes in public opinion over time and understanding shifts in attitudes and preferences. In this article, we will delve into the concept of tracking polls, their significance in survey research, and…
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Specification Error in Surveys: Causes, Effects & How to Minimize
Introduction Specification error refers to a critical issue in survey research where the chosen model or framework used to analyze data does not accurately capture the underlying relationships or characteristics of the phenomenon being studied. It occurs when the specification of the model deviates from the true data-generating process, which causes bias or unreliable results. …