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Related Concept Videos

Surveys02:16

Surveys

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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.
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Data Collection by Survey01:07

Data Collection by Survey

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The systematic method of obtaining and analyzing accurate information of a population is called data collection. A survey is a standard method of data collection that involves collecting information from a target human population about their experience, opinion, or knowledge of a product, service, or process. The responses are recorded and interpreted. The most common survey examples are written questionnaires, face-to-face or telephonic conversations, focus groups, and electronic (e-mail or...
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Randomized Experiments01:13

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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
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Blind Procedures02:07

Blind Procedures

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Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which...
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Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs01:15

Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs

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Body:Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
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Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

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Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
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Related Experiment Video

Updated: Feb 16, 2026

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
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Using pre- and post-survey instruments in interventions: determining the random response benchmark and its

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Random answering on pre/post surveys can show surprisingly high improvement rates. This study provides benchmarks to correctly evaluate nutrition intervention effectiveness and avoid faulty inferences.

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Area of Science:

  • Nutrition Education
  • Survey Methodology
  • Statistical Analysis

Background:

  • Pre/post surveys are common for evaluating interventions.
  • Interpreting improvement rates requires a baseline understanding of random response.
  • High random improvement rates can mask true intervention effects.

Purpose of the Study:

  • To demonstrate the high rate of improvement from random survey answering.
  • To provide formulas and tables for calculating random answering benchmarks.
  • To establish appropriate null hypotheses for pre/post survey analysis.

Main Methods:

  • Developed formulas and tables to estimate random answering improvement percentages.
  • Applied methods to analyze pre/post survey data from the USDA Expanded Food and Nutrition Education Program.
  • Tested if actual improvements differed significantly from random response expectations.

Main Results:

  • The observed number of improvements was not significantly less than expected from random answering.
  • This indicates potential misinterpretation of pre/post survey data.
  • The survey instrument itself was not necessarily flawed.

Conclusions:

  • A benchmark for random answering improvement is crucial for valid intervention evaluation.
  • Accurate benchmark calculation prevents faulty inferences about program effectiveness.
  • This research aids analysts in correctly interpreting pre/post survey results.