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NCHEC MCHES (MCHES) Practice Tests & Test Prep by Exam Edge - Topics



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Understanding what is on the NCHEC MCHES exam is crucial step in preparing for the exam. You will need to have an understanding of the testing domain (topics covered) to be sure you are studying the correct information.

  • Directs your study efforts toward the most relevant areas.
  • Ensures efficient and adequate preparation.
  • Helps identify strengths and weaknesses.
  • Allows for a focused approach to address gaps in understanding.
  • Aligns your preparation with the exam's expectations.
  • Increases the likelihood of success.
  • Keeps you informed about your field's current demands and standards.
There is no doubt that this is a strategic step in achieving certification and advancing your career.

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NCHEC Master Certified Health Education Specialist Exam Blueprint

Understanding the exact breakdown of the NCHEC Master Certified Health Education Specialist test will help you know what to expect and how to most effectively prepare. The NCHEC Master Certified Health Education Specialist has 150 multiple-choice questions The exam will be broken down into the sections below:

NCHEC Master Certified Health Education Specialist Exam Blueprint
Domain Name % Number of
Questions
Assess Needs, Resources and Capacity for Health Education/Promotion 10% 15
Plan Health Education/Promotion 16% 24
Implement Health Education/Promotion 15% 23
Conduct Evaluation and Research Related to Health Education/Promotion 20% 30
Administer and Manage Health Education/Promotion 18% 27
Serve as a Health Education/Promotion Resource Person 12% 18
Communicate, Promote and Advocate for Health and the Profession of Health Education/Promotion 9% 14

NCHEC Master Certified Health Education Specialist - Exam Topics Sample Questions

A probability sample that selects participants from a sampling frame by taking every Nth person after a random start is which of the following?

Correct Answer:
systematic sample
the correct answer to the question is "systematic sample."

to understand why, let's first define what a systematic sample is. a systematic sample is a type of probability sampling method where participants are selected from a larger population according to a fixed, periodic interval. this method is implemented by arranging the population in some order, usually as a list. after determining the total population size and the desired sample size, the sampling interval \( k \) is calculated. the sampling interval is typically found by dividing the population size by the sample size (\( k = \frac{n}{n} \)), where \( n \) is the total population and \( n \) is the number of samples to be taken.

once the interval \( k \) is determined, the starting point within the first interval is selected randomly. this randomness in the selection of the starting point is crucial as it ensures that the sample is a probability sample, where each member of the population has a known and non-zero probability of being included in the sample. after the starting point is chosen, every \( n \)th individual from the list is selected until the desired sample size is achieved.

this method is distinguished from simple random sampling, where each participant has an equal chance of being chosen independently of the others. in systematic sampling, once the first individual is randomly selected, the rest of the sample is determined by the fixed interval.

systematic sampling is generally easier and quicker to perform than simple random sampling, especially when dealing with large populations. it is commonly used in various fields such as market research, auditing, and population surveys. however, it's important to be cautious of periodicity within the population list, as this could introduce bias if the interval \( k \) aligns with a pattern in the population structure.

in conclusion, a systematic sample efficiently selects participants by using a fixed interval approach after a random start, ensuring a probability sampling method where every individual has a known chance of selection, albeit not necessarily equal. this method provides a practical balance between randomness and convenience in many statistical sampling requirements.