Reading Research Literature—Implementing the Study, Data Collection Methods
We continue our review of the steps of the research process. Earlier in the course, we discussed the following.
In general, the researcher wishes to learn more about the characteristics of a population. Research questions may include the following.
Why use numbers? According to Houser (2018), numbers are objective, standardized, consistent, precise, statistically testable, and an accurate representation of attributes.
The researcher may work with numbers that can be calculated and have numerical value—for example, body temperature. At other times, the researcher assigns numbers to traits simply to classify them. These numbers are not quantifiable but, rather, serve to sort, organize, or group subjects according to traits. An example would be a rating based on perception, such as self-esteem. Reading Research Literature—Implementing the Study, Data Collection Methods
As you can see, not all numbers possess the same properties. The researcher must set up rules regarding how numbers are determined, collected, recorded, and analyzed. These rules are called the measurement strategy.
Part of the measurement strategy is to define the research variables.
Example: The researcher may wish to study anxiety. The researcher offers a conceptual definition of anxiety as the emotional response to a perceived threat. The operational definition describes the emotional responses in concrete, measureable terms, such as sweating and rapid heart rate.
Think Like the Researcher
When you formulate clinical questions and identify PICO elements, your goal is to think like the researcher. Select key words to search that are similar to operational definitions the researcher may have chosen. Avoid searching on conceptual terms such as better health that are too vague to match. Operationalizing key terms will improve your chances of finding relevant research-based evidence. Reading Research Literature—Implementing the Study, Data Collection Methods
Variables must be expressed as numbers in order to analyze them statistically, but different types of numbers have different levels of measurement.
Table 5.1 Levels of Measurement | |||
Level of Measurement | Description | Examples | Implications for Statistical Testing |
Nominal | Variables arecategorized data;classified; andnot ordered. | GenderEthnicityReligion | Subjects cannot be compared. Analysis may include frequencies in each category. Analyze with nonparametric statistics. |
Ordinal | Variables arecategorized data;classified;ordered and may be ranked; andnot proportional, or no fixed intervals. | Pain scaleSmall, medium, large amountsFirst place, second place, third place | Subjects can be compared. Analysis may include frequencies and percentiles. The median may be computed. Analyze with nonparametric statistics. Reading Research Literature—Implementing the Study, Data Collection Methods |
Interval | Variables arecontinuous data;quantified;proportional, or fixed intervals; andno true zero. | Fahrenheit or Celsius body temperatureHeight | Values can be added and a mean computed. Analyze with parametric statistics.Reading Research Literature—Implementing the Study, Data Collection Methods |
Ratio | Variables arecontinuous data;quantified;proportional, or fixed intervals; andtrue zero. | Body weightHeart rate | Values can be added and a mean computed. Analyze with parametric statistics. |
Information adapted from Houser, 2018.
Errors
Errors may occur when collecting data. A measurement error is the difference between the true number and the number that the instrument reads.
Random error leads to readings that may be inaccurate for a variety of reasons. Some readings are accurate, whereas others are inaccurate; the occurrence of either type of reading is unpredictable and cannot be reproduced.
Systematic error happens consistently and can introduce bias into the readings. One example of a systematic error occurs when a poorly calibrated instrument produces readings that are all too high or too low, or a systematic error happens when an observer mistakes a behavior and marks it as one thing on the rating tool when the behavior should be marked as another. Reading Research Literature—Implementing the Study, Data Collection Methods.
Errors in measurement can ripple throughout the rest of the research process and lead to faulty findings.
Instruments
Instruments measure variables. Instruments must be reliable and valid in order to yield useful data.
Reliable instruments measure a variable with precision.
Valid instruments measure in a manner that is accurate and truthful. A valid instrument measures the correct thing.
An instrument may be reliable but not valid in that it may consistently measure something that is not accurate. Instruments must be reliable in order to be valid, and both attributes must exist in order to measure data in a way that inspires confidence in the research findings. Each method of data collection has its own strengths and weaknesses with respect to reliability and validity.
The measurements section of the research report describes the instruments that the researcher used to collect data. The researcher may describe the reliability and validity of the instrument. The existence of both attributes lends credibility to the claims that the researcher makes in the findings. Decide if the instruments actually measure the variables identified in the research question. If there is a mismatch, the findings should not be applied to your situation. Reading Research Literature—Implementing the Study, Data Collection Methods.
The computer industry has adopted the acronym GIGO, or garbage-in garbage-out, to explain that no matter how well information is processed, the quality of the information that comes out can be no better than the quality of the information that goes in (Business Dictionary, 2010). If data are improperly collected, the findings based on those data are worthless, or garbage. How does the researcher collect reliable, valid, clear, consistent, and unbiased data? The following are primary data collection methods, where the researcher or data collector directly measures the subjects.
Secondary data may be collected from sources that were not created for the current research study. Typically, the researcher looks through records or “mines” the relevant data that pertains to the variables. Examples include but are not limited to
A popular data collection instrument is the Likert scale. What is a Likert scale? What type of number does it measure?
Answer
The Likert scale is a 5- or 7-point scale that asks the subject to answer a question by indicating a number on the scale. Typically, the subject is asked to indicate the degree to which there is agreement or disagreement. The numbers represent ordinal data. The numbers can be ordered and ranked, but there is no fixed interval between the numbers. An example of a question using a Likert scale is, “Do all citizens have a right to healthcare regardless of their ability to pay?” Please choose a number between one and five that best indicates your answer to this question.
1 | 2 | 3 | 4 | 5 |
Strongly Agree | Agree | Neutral | Disagree | Strongly Disagree |
Interactive Research Report
The following research report contains descriptions of the various components that comprise most reports. The purpose of this interactive report is to help you learn where to find specific information.
The implementation of the research study is a crucial step of the research process. The way the researcher defines characteristics in the population leads to the selection of instruments to measure and collect data on the subjects that represent that population. The key terms that you choose to search for, research-based evidence, should try to match the way in which the researcher operationalizes the definitions of the variables. Reading Research Literature—Implementing the Study, Data Collection Methods.
It is important to remember that not all numbers have the same attributes. Some are merely for classification, whereas others can be quantified and analyzed statistically. Be sure that any instruments are reliable and valid and that they accurately collect data that will help the researcher to close the knowledge gap.
Garbage-in, garbage-out. (2010). Business Dictionary.com. Retrieved from http://www.businessdictionary.com/definition/garbage-in-garbage-out-GIGO.html
Houser, J. (2018). Nursing research: Reading, using, and creating evidence (4th ed.). Sudbury, MA: Jones and Bartlett. Reading Research Literature—Implementing the Study, Data Collection Methods.
Delivering a high-quality product at a reasonable price is not enough anymore.
That’s why we have developed 5 beneficial guarantees that will make your experience with our service enjoyable, easy, and safe.
You have to be 100% sure of the quality of your product to give a money-back guarantee. This describes us perfectly. Make sure that this guarantee is totally transparent.
Read moreEach paper is composed from scratch, according to your instructions. It is then checked by our plagiarism-detection software. There is no gap where plagiarism could squeeze in.
Read moreThanks to our free revisions, there is no way for you to be unsatisfied. We will work on your paper until you are completely happy with the result.
Read moreYour email is safe, as we store it according to international data protection rules. Your bank details are secure, as we use only reliable payment systems.
Read moreBy sending us your money, you buy the service we provide. Check out our terms and conditions if you prefer business talks to be laid out in official language.
Read more