NUR 504 ALL WEEK DISCUSSIONS PAPER NUR 504 ALL WEEK DISCUSSIONS PAPER NUR504 NUR 504 Discussions 1 Discuss the differences between research, research utilization, and evidence-based practice. You may want to link this to the historical evolution of research in nursing. NUR 504 Week 1 Discussions 2 Identify and discuss two major ways in which qualitative research differs from quantitative research. Is one better than the other? Provide reference(s). CLICK HERE TO ORDER YOUR NUR 504 ALL WEEK DISCUSSIONS PAPER NUR 504 Week 2 Discussions 1 Discuss sources of bias for both quantitative and qualitative research. For quantitative research, be sure to address both random and systematic bias. You may use examples from the articles you selected as illustrations of bias and/or preventing bias. NUR 504 Week 2 Discussions 2 Researchers often identify the research problem and then go in search of a theory. Discuss the disadvantages of doing this. What does the textbook recommend that researchers do to assure a true fit between theory and designing the study? ? NUR 504 Week 3 Discussions 1 Describe the quantitative design of the article you selected. Present the strengths and limitations of this type of design according to the textbook and how these are reflected in your study. Contrast the design you have selected with at least one design presented by a classmate in one of your responses. NUR 504 Week 3 Discussions 2 Describe the qualitative design (or methodology) of the article you selected. Present the strengths and limitations of this type of design according to the textbook and how these are reflected in your study. Contrast the design you have selected with at least one design presented by a classmate in one of your responses. ? NUR 504 Week 4 Discussions 1 Read the section Questionnaires versus Interviews on pages 305-306 in the textbook. How are these guidelines similar and different from data collected by nurses when giving care? What principles did you identify that are new to you but could be important in improving your collection of clinical data? NUR 504 Week 4 Discussions 2 You are interested in nurses attitudes toward EBP. Which method do you think would work best to obtain this information: a questionnaire, a face-to-face interview, or a group interview? Defend your answer. ? NUR 504 Week 5 Discussions 1 Demographic data is collected for every study. What is the purpose of describing the demographic data? NUR 504 Week 5 Discussions 2 There is a tendency for novice researchers to develop their own instrument if they cannot readily find one. How might you respond to a peer or manager who asks you to help develop a new tool to collect patient data on anxiety prior to cardiac catheterization? ? NUR 504 Week 6 Discussions 1 State in your own words what is meant by Type I and Type II errors. Why are these important? Name one thing that can be done to improve internal validity of a study. NUR 504 Week 6 Discussions 2 An example of a multivariate procedure is analysis of covariance (ANCOVA). Explain what is meant by the following statement: ANCOVA offers post hoc statistical control. Provide an example ? NUR 504 Week 7 Discussions 1 In the final section of study reports, there is a section on implications and recommendations. Describe the difference between these terms. Provide examples from one of the studies that you critiqued. NUR 504 Week 7 Discussions 2 Researchers have a responsibility to identify the limitations of a study. What is meant by limitation? Provide examples from one of the studies that you critiqued. ? NUR 504 Week 8 Discussions 1 Post your groups CLC EBP project. Critically read two CLC EBP projects (other than your own). Name one barrier for each that could impact the implementation of the guideline in practice and how you would work through this issue. NUR 504 DQ2 Discuss what is meant by mixed-methods designs. What are the limitations of these designs. 

ADDITIONAL INFORMATION;

Discuss sources of bias for both quantitative and qualitative research

Introduction

The field of research is broad and varied, and the data that researchers generate often requires them to use different methodologies. While quantitative research seeks to quantify or measure variables, qualitative research focuses on understanding the human experience. The two types of studies often differ in how they are collected and analyzed, which can lead to biases in their results. Here are some sources of bias you should be aware of:

Sample: A nonrandomly chosen group of people, used to represent the entire population to which the researcher wants to generalize.

The sample is a nonrandomly chosen group of people, used to represent the entire population to which the researcher wants to generalize. It may be biased and not representative of all members of its population. A sample can also be “nonrandom” if it includes only individuals who want to participate in research or are willing to answer questions honestly (e.g., because they have nothing better going on).

The sample does not necessarily represent any specific unitary phenomenon; it simply provides information about how members of that unitary phenomenon behave under certain conditions or else how they don’t behave at all! This means that no matter how many times we look at our sample data, there will still be some degree of uncertainty due to this lack of precision and therefore variability in our results.

Validity: The extent to which a tool measures what it claims to measure.

Validity is the extent to which a tool measures what it claims to measure. Validity can be measured in several ways, including reliability and discriminating power. Reliability refers to consistency among repeated measurements of the same variable; for example, if you measure the height of your subject twice and get two different results (say they’re 5’7″ on one test, 5’8″ on another), then your test is considered reliable because there is no significant difference between those two sets of data points. Discriminating power describes how well a test discriminates between people who have different levels of ability; for example, if we compare two groups of people who are asked questions about their favorite sports teams one group has an average score of 100 while another group has an average score of 80 it may seem obvious which group has more knowledge about sports since both sets scored 100%. But remember that these questions were constructed so that each individual had exactly equal knowledge base?

Reliability: The degree to which an instrument produces stable and consistent results over time.

Reliability is the extent to which a test or instrument produces consistent results. It’s not the same as validity, accuracy, precision, stability and many other concepts we’ll discuss later in this guide.

Reliability is often described as “the extent to which an instrument produces stable and consistent results over time.” In other words: if you take your test again after some time has passed (e.g., a month), will your score still be the same?

Random sample: A sample in which every individual in the population has the same chance of being included.

Random sample: A sample in which every individual in the population has the same chance of being included. A random sample is often used to obtain information about a large group or population with no knowledge of how they will be distributed among different sampling units. Random sampling procedures have been developed to ensure that each element (or “unit”) of interest has an equal probability of being selected; these procedures ensure that there are no systematic errors in selecting individuals based on their characteristics.

Random numbers are generated by a computer algorithm, which generates numbers at random until some stopping criterion is reached (e.g., until 100 iterations have been completed). Random number tables can also be used to generate random numbers; however, they do not guarantee that each element has an equal probability of selection due to possible biases in these tables.[1]

Self-selection bias: A type of bias that occurs when a study’s participants are not selected at random from a defined population but rather select themselves for inclusion.

Self-selection bias is a type of bias that occurs when a study’s participants are not selected at random from a defined population but rather select themselves for inclusion. This can be avoided by using an unbiased sampling method, such as convenience or quota sampling.

The selection process must be carefully planned to ensure that the sample is representative of your target population.

Researcher bias: An unintentional error or distortion introduced into research by its own researcher, for example by failing to understand or correctly implement a research design.

Researcher bias is a type of error that occurs when a researcher’s personal beliefs, feelings or experiences affect the way in which he or she interacts with and collects data from participants in a study. It can lead to incorrect conclusions that are based on faulty data collection.

For example, consider an interviewer who conducts focus groups and asks participants if they agree or disagree with statements such as “I’m not sure how much time I spend watching TV” or “My family doesn’t understand me.” The interviewer then uses these responses as measures of attitudes toward television viewing behavior instead of using the actual amount of time spent watching TV (or any other form) because he feels strongly about this issue.

Measurement bias: An error following from incorrect assessment of the data; for example, if facts or responses are measured incorrectly because of the use of inappropriate equipment or processes.

Measurement bias is the difference between the true value and the measured value. It occurs when there is an error following from incorrect assessment of data, such as when facts or responses are measured incorrectly because of inappropriate equipment or processes used in collecting them.

Measurement bias can occur when:

• The measurements are not accurate because of inaccurate equipment (for example, an instrument that reads too high).

• The measurements are not accurate because they were taken with inaccurate equipment (for example, a thermometer may read higher than it should).

• The measurements were taken using appropriate methods but not calibrated properly (for example, temperature readings at different times during a day).

• A process for determining validity was followed but it wasn’t followed correctly; for example: if you need to know how much weight someone weighs before you can tell what kind of diet plan will work best for them then this would be known as validity checking because it ensures that all steps in your research process have been carried out correctly.”

Interviewer bias: An interviewee’s responses may be influenced by how questions are framed, asked, and interpreted by the interviewer.

• Interviewer bias: An interviewee’s responses may be influenced by how questions are framed, asked, and interpreted by the interviewer.

• The interviewer effect is a form of self-selection that occurs when researchers or interviewers of studies select participants based on their own preconceptions and expectations about what constitutes an effective response.

Interviewer bias can lead to false data—for example, if an interviewer asks leading questions such as “What did you think about that?” instead of open-ended questions such as “Tell me more about your experience.”

We need more than some answers in order to draw meaningful conclusions from research results — we also need information about how those answers were collected and analyzed.

One of the most important things to remember when you’re doing research is that bias can be introduced by both the researcher and participant. Bias can also come from the instruments used to measure data, as well as their methods for collecting and analyzing it.

When we say that there are sources of bias in quantitative research, this means that some people might have an advantage over others when it comes to being able to answer certain questions or perform certain tasks—including those who are more knowledgeable about a subject matter than others without prior experience with it (such as researchers). This leads us back again toward our original question: How does this affect our results?

Conclusion

In order to draw real conclusions from a study’s findings, you must know about these biases and how they can affect the results. It also helps to have some experience working with researchers who are familiar with these issues so that it’s easier for them to anticipate what kinds of questions might cause bias in your data collection process.