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1-“Correlation is not causation” is a statistics mantra according to theguardian.com. “Correlation is not causation” means that just because two things correlate does not necessarily mean that one causes the other. Example provided by the article is as followed: just because people in the UK tend to spend more int eh shops when it is cold and less when it is hot, does not mean cold weather causes frenzied high-street spending; a more plausible explanation would be that cold weather tends to coincide with Christmas and the New Year sales.

Reference

Correlation Is Not Causation | Nathan Green’s S Word |n.d.| Access Date August 21, 2018| from

Nathan Green – https://www.theguardian.com/science/blog/2012/jan/06/correlation-causation

Correlation Vs. Causation: An Example – Towards Data Science |n.d.| Access Date August 21, 2018| from

100001147717970 – https://towardsdatascience.com/correlation-vs-causation-a-real-world-example-9e939c85581e

Linear Correlation | Documentation| n.d.| Access Date August 21, 2018| from

https://www.mathworks.com/help/matlab/data_analysis/linear-correlation.html

  

2-sure we need more information about the study variables like the sample size, age of participants, health history, smoking history and related variables so in the conclusion you can reduce errors and bias, for example.

  

3-

for your explanation. A correlation measures the degree of the relationship between the variables, while a casual relation means that one variable causes the other. However, a correlation between two variables does not imply causation.

 
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4-Interaction in statistics can be defined as the effect of one independent variable may depend on the level of the other independent variable. In statistics, an interaction may arise when considering the relationship among three or more variables, and describes a situation in which the simultaneous influence of two variables on the third is not additive. In order to find an interaction, you must have a factorial design, in which the two or more independent variables are “crossed” with one another so that there are observations at every combination of levels of the two independent variables.

 The presence of interaction can have important implications for the interpretation of statistical models. If two variables of interest interact, the relationship between each of the interacting variables and a third “dependent variable” depends on the value of the other interacting variables and this makes it hardest to anticipate or predict the consequences of the value of variable that changes particularly if the variable it interacts with are difficult to control. (Eastern & McColl 2016)

Example is if we want to examine the effect of two variables, gender and premature birth on health outcomes, we would first of all outline any differences in health outcome score among gender as a main effect.  Similarly, we will describe any difference in the scores of full term/premature as a main effect.  The presence of an interaction effect shows that the effect of gender on health outcome varies as a function of premature birth status.

Reference

Easton J.C & McColl 2016: Design of Experiments and Anova.  Retrieved August 17, 2018 from https://www.stats.gla.ac.uk/steps/glossar/anova.html#intermpediaiew.com.

  

5-Thank you all for sharing your examples of the variables interaction. Suppose you formulate a hypothesis and conduct an experiment, but the results were not significant. What information can you gain from that, or has your experiment been a failure? Support your argument with example.

  

6- for your example. The interaction effect here is useful as it is an indication for the treatment efficacy. It is important to consider different factors when assigning the treatment for the patient, for example looking at the effect at different age, by gender and if the patient will go with one treatment, diet or pills, or both.

 
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1-Many statisticians base ANOVA on the design of the experiment, especially on the protocol that specifies the random assignments of treatment to subjects; the protocol’s description of the assignment mechanism should include a specification of the structure of the treatments and of any blocking. It is also common to apply ANOVA to observational data using an appropriate statistical model.

Some popular designs use following types of ANOVA:

· One-way ANOVA is used to test for differences among two or more independent groups. e.g., different levels of urea application in a crop or different levels of effect of some medicine on groups of patients.  However, the one-way ANOVA is used to test for differences among at least three groups, since the two-group case can be covered by t-test when there are only two means to compare.

· Factorial ANOVA is used when the experimenter wants to study the interaction effects among treatments.

  

2-When dealing with an uninformed person, it is good to make sure the explanations and the follow-up given is as simple as possible and accurate. Variance testing should first be explained as a way to get results from conducting a study or an experiment. There are one-way, two-way, three-way tests where the difference as indicated by their number is according to the variances analyzed in each of the study. One way will have one variable, while three way will have three variables. It is also necessary to make sure there is a connection between the different variables, as they need to come from the same population, and samples selected randomly

  

3-The analysis or ANOVA is a test that is conducted to find out if the survcey or experiments results are significant. There are different ways that this test might be done, depending on the study at hand. Statisticshowto described them as follows: 

  • One-way      ANOVA between groups: used when you want to test two groups to      see if there’s a difference between them.
  • Two      way ANOVA without replication: used when you have one group and      you’re double-testing that same group. For example, you’re      testing one set of individuals before and after they take a medication to      see if it works or not.
  • Two      way ANOVA with replication: Two groups, and the members of those      groups are doing more than one thing. For example, two groups of      patients from different hospitals trying two different therapies.

In order for the results to be accurate, ANOVA has the following assumptions in regards to the study:

  • there      needs to be similar variances
  • the      population where the samples are obtained should have normal      distribution 
  • samples      should be randomly selected
  • samples      should be independent 

Statisticshowto (n.d) ANOVA test: Definition, types, examples. Retrieved from http://www.statisticshowto.com/probability-and-statistics/hypothesis-testing/anova/

 
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4-Each alpha level is dependent on the circumstance that surrounds a particular study. The significance level(alpha) is the probability of committing a type 1 error. A type 1 error is committed when the researcher falsely rejects the null hypothesis. A significance level of 0.05 is the standard situation, most especially in the field science.

There are some experiments where you would most likely want to lower the type 1 error rate such as experiment that affects human health, like drug research or studies of psychological treatment. For some experiments, if the consequence of applying null hypothesis is extremely serious, for instance, if null hypothesis applies, there may be death, or serious injury, then you want to try your best to avoid the type I error. That means you must avoid the situation that null hypothesis is true but you reject it. As the significance level is the probability, you will make the type 1 error. So, for such experiments with serious results, we want to make the level smaller than standard situation. So, for such experiments, if you can’t tolerate a 5% chance of being wrong, use a lower significance level, 0.01 for example. 0.01 is common if there’s a possibility of death or serious disease or injury.

If the consequences of being wrong are especially minor such as political research or animal migration studies. you might use a higher significance level, such as 0.1, but this is rare in practice. That is, it may be common that we make the significance level much smaller than 0.05, but we rarely make the level larger than 0.05.

Reference

Hypothesis Testing (cont…) |n.d.| Access Retrieved on 08/08/2018 from https://statistics.laerd.com/statistical-guides/hypothesis-testing-3.php

The idea of significance test. Retrieved on 08/08/2018 from https://www.khanacademy.org.

  

5-The alpha is the level of statistical significance. It can be any number between 0-1. 0.10, 0.05 and 0.01 are most commonly used. A situation where we would want to accept a higher alpha level is with medical testing. We would much rather have false positive test results that would lead to additional testing, even though it is going to give our patients an insane amount of anxiety. It is better than a false negative where no further testing or treatment would be indicated, and the patient would go untreated.

References

Taylor, C. (2013, March 20). What Level of Alpha Determines Statistical Significance? Retrieved from https://www.thoughtco.com/what-level-of-alpha-determines-significance-3126422

  

6-Not all results of hypothesis tests are equal. A hypothesis test or test of statistical significance typically has a level of significance attached to it. This level of significance is a number that is typically denoted with eh Greek letter alpha Many journals throughout different disciplines define that statistically significant results are those for which is equal to 0.05 or 5%.

The number represented by  is a probability, so it can take a value of any nonnegative real number less than one. Although in theory any number between 0 and 1 can be used for , when it comes to statistical practices this is not the case. Of all levels of significance, the values of 0.10, 0.05, and 0.01 are the most commonly used .

In medical screening for a disease, consider the possibilities of a test that falsely tests positive for a disease with one that falsely tests negative for a disease; a false positive will result in anxiety for our patient but will lead to other tests that will determine that verdict of our test was indeed incorrect; a false negative will give our patient the incorrect assumption that he does not have a disease when he in fact does. The result is that the disease will not be treated; given the choice, scientists would rather have conditions that result in a false positive than a false negative.

Reference

What Level Of Alpha Determines Statistical Significance? |June 25, 2018| Access Date| August 6, 2018 from

Courtney Taylor – https://www.thoughtco.com/what-level-of-alpha-determines-significance-3126422

Hypothesis Testing (cont…) |n.d.| Access Date August 6, 2018| from

https://statistics.laerd.com/statistical-guides/hypothesis-testing-3.php

 
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1-A T-test and a Z-test are almost similar in theory, but have a different application mechanism. The major difference with these two is the size of the sample applied in each case. The t-test is appropriate for smaller samples. Any sample less that 30 units are best analysed using the t-test, while beyond 30 units require a z-test. A z-test is also better if the standard deviation is not known.  

 T tests are used to compare a given mean to the mean of the given population; it can be applied to either individual values or ones that are paired. The T test can be helpful when you do not know the standard deviation and is best utilized when your sample size is smaller (n<30 sample size) (Lango, 2015). For example, in a Z test you must know the standard 

  

2-Z-test- implies a hypothesis test which ascertains if the means of two datasets are different from each other when variance is given. It is based on normal distribution with a known population variance and a large sample size >30 units

 T-test- refers to a type of parametric test that is applied to identify how the means of two sets of data differ from one another when variance is not given.  It is based on student T distribution when the population is unknown and the sample size is smaller, <30 units.  

Reference

Difference Between t-test and z-test (with Comparison Chart) – Key Differences. (2018, March 10). Retrieved from https://keydifferences.com/difference-between-t-test-and-z-test.html

  

3-Yes, in theory these two test are common with differences only setting in when applying them. Both used in hypothesis setting, the z-test is better applicable when the standard deviation is known, and when dealing with a larger sample size. A z-test is better to use when dealing with a larger sample size than of over 30. A t-test is better when the sample size is less than 30, and when dealing with an unknown standard deviation

 
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1-In order to get real results, and a truly random study we need to not have bias. Its one of the things that I notice most when people are having their vaccine debate, or with political garb. You can almost always tell when someone has an agenda they are trying to push. 

  

2-An important feature of a good study is that the sample is randomly selected from the target population which is the entire group of individuals that the researcher is interested in studying. To choose randomly means that every member of the target population has an equal chance of being included in the sample. This will prevent biased in the sample group.

To prevent problems in obtaining a truly random sampling, several steps can be taken.

  1. The      target population is well defined. If not well defined, the sample that is      actually studied may be outside the intended population or the survey may      include people that should be included.
  2. The      sample matches the target population. It would be difficult to ask      everyone in the target population to provide the wanted information, so      the best that the researcher can do is select a subset of individuals from      the population and gather information from them.
  3. The      sample size is large enough. If there is a large sample size that is      representative of the target population (randomly selected) the      information is likely to be accurate.

In any research project, steps must be taken to prevent biased. Part of that process is to realize the importance of random sampling. Bias can easily be introduced by not defining the target population well enough, ensuring the sample matches the target population and ensuring the sample size is large enough.

Reference:

Rumsey, D., (2010). Statistics Essentials for Dummies. Wiley Publishing, Inc. Indianapolis, IN

  

3-Sampling is the process of selecting a representative group from the population under study. It is more or less impossible to study every single person in a target population so psychologists select a sample or sub-group of the population that is likely to be representative of the target population we are interested in. If the sample we select is going to represent the target population then we need to make sure that the people in it are similar to the other members of the target population. This is important because we want to generalize from the sample to target population. The participants in research, the sample should be as representative as possible of target population. The more representative the sample, the more confident the researcher can be the results can be generalized to the target population. Everyone in the entire target population has an equal chance being selected. This is s similar to the national lottery. If the ‘population’ is everyone, who has bought a lottery ticket, then each person has an equal chance of winning the lottery. Random samples require a way of naming or numbering the target population and then using some type of raffle method to choose those to make up the sample. Random samples are the best method of selecting your samples from the population of interest. The advantages are that your sample should represent the target population and eliminate sampling unfairness, but the disadvantage is that it is very difficult to achieve.

     One of the  problems that can occur when selecting a sample from a target population is sampling bias, sampling bias refers to situation where the samples doesn’t reflect the characteristics of the target population. Many psychology studies have a biased sample because they have used a opportunity sample that comprises university students as their participants

Reference:

McLeod, S.A. (2014) .Sampling methods. Retrieved from www. simply psychology.org//sampling.html

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4-I so agree with you, its not what you but its how you do it, i always say it never the big things that are done but its always the smallest things that is done will make a difference. Treating employees with kindness will go along way, dont tell the staff that how good their doing but show them sometimes its good to let people knows that they are appreciated and mean it and you wil get more out of them. A few weeks ago we were short staff by 4 CNA i called in a few of them they came in and work we had no grievences on monday i went into my own funds with two other nurses and give each staff members gift cards and then brought them in lunch i do this to let them know that i appreciate all they did those two days for us when they didnt have after all it was their weekend but they gave it up to come in and work, when the administrator heard she call me into the office and told me that i didnt have to do that i looked at her and said i know but the reason i do it is because i wanted to number one and i appreciate them and we need each other,  this is not a one mans band and sometimes its nice to show and tell staffs how we truly feels about them expecially if there help is needed and they come to the  rescue and their hard work she said oh they didnt do it for free they getting paid she said that i must not kid my self they came in because they needed the overtime, i looked at her and walked out, there was nothing more to be said.

  

5-As a nurse leader, I would develop an outreach program that would benefit our much diverse population in our community. First I would create a vision of a needy program such as having a monthly health fair in the local adult community center.  Leaders and managers overlap in their job functions and this is where a leader can collaborate with the managers to carry out his or her vision.  With leader’s motivation and inspiration, and manager’s task–oriented skills and solid actions, the outreach program can become a reality.  Nurse Managers and Leaders must understand their roles and good communication is crucial to carry out plans either by verbal, non-verbal or written communications (Lecture 2, 2018).

  1. A      vision is your personal view that you create for the benefit of your      community
  2. Look      at your community makeup, for example, buildings, public spaces, jobs,      kind and types of people (cultural diverse)
  3. Think      of some of their needs and develop a vision
  4. Communicate      your vision and get feedback, talk to people, go to different community centers,      adult clinics and discuss your vision through seminars, posters, flyers      and get a feel of how strong your vision is
  5. Develop      a plan and strategies for your shared vision

Strategic planning is an organizational management process that allows you to involve your staff and monitor the results regularly.  We can be proactive and keep up and stay ahead with the changing trends in the workplace.  It can offer a great foundation with realistic objectives and goals.  It guides managers’ discussion and decision making in resources and budget for the vision.  It can increase profits, provide marketing success and have a strong foundation (Ong, 2015). 

Now as a leader, you have to help people make this vision their own, so that with a sense of ownership everyone can work together in a committed group.  You should do this by communicating effectively and be courageous to all members in order to have the same basic goal of the vision.  The outreach program will be presented to the stakeholders, such as staff and board members during meetings and get feedback. Then step is to go out to the community center and execute the vision and finally get evaluation to see if changes need to be made.

References

Lecture 2. (2018). NRS-451V. Theories and Concepts in Leadership and Management. Phoenix, AZ: Grand Canyon University.

Ong, C. (2015). 5 Benefits of Strategic Planning. Retrieved from https://www.envisio.com/blog/benefits-of-strategic-planning

  

6-I think that in order to implement any kind of new program, I feel that it is necessary to do the research first. It is also necessary to have good communication with an effective way to present the information that has been gathered. The new program will need to share the same values of the stakeholders in your organization, so it will be important to ensure the match before the presentation. Huber explains that the following steps are useful in developing the framework of a new plan.

•Developing a strategic mission or vision

•Setting objectives

•Developing strategies to achieve the objectives

•Implementing the strategies

•Evaluating the results (Huber, 2014, p. 331).

After the information is gathered, I would develop a PowerPoint presentation that would allow for the information to be shown to the stakeholders. This would include a summarized list of the information, and a detailed packed would be given for reading on their own time. I would want to keep the information short, as I would not want to overwhelm or bore them with the topic. I would include a time for discussion and questions to be asked and answered. Good verbal abilities and eye contact would be necessary for a successful presentation.  In order for this plan to become a reality, the stakeholders of the organization would have to find common ground with the presenting nurse. When they are on the same page, there is the possibility to provide quite the impact on the members in need in the community.

Huber, D. (2014). Leadership and Nursing Care Management, 5th Edition. [Pageburstls].

           Retrieved from https://pageburstls.elsevier.com/#/books/9781455740710/

 

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4-The functions of management include planning, organizing, staffing, directing, and controlling. Within each of these functions, decisions must be made to optimize the care provided while remaining fiscally responsible.

Leadership is a more challenging role to define. At times, leaders may not have formal authority, but they may have power through their ability to influence others. A leader’s emphasis is on interpersonal relationships; they may be known as mentors, coaches, advocates, or role model. (Lecture 1)

Management and leadership may coexist in one, though they may not at all. Some managers are leaders as well, and some are not. You may think they would go together, but we have all had experience with a manager that is NOT a leader. You know the one. The one who is the boss, and handles business, but not one that inspires, advocates or mentors staff.

One the other side of the coin, you may have had the pleasure of experiencing a manager that is a leader as well, someone who is part of the team and inspires, mentors, and coaches staff. The inspiration and the management. The boss who rally’s the troops and makes you WANT to be there and WANT to work for them. The two overlap in multiple areas.

As a nurse leader, I do believe I can expand my influence to create change by taking advantage of this overlap. I feel that I am inspiring and able to mentor and encourage and advocate for not only my patients, but my coworkers, company, and my profession. I would take advantage of the overlap by using my leadership abilities to continue to mentor, inspire, and encourage others in doing their best and being team players. A huge part of this is leading by example, which I strive for daily.

Resources

Lecture 1. (2018). NRS-451V. Theories and Concepts in Leadership and Management. Phoenix, AZ: Grand Canyon University.

  

5-management and leadership does overlapped to be a good manager you must have good leadership skills, managing is also means to lead making things happen,leader shares what he/she knows with others so that they can follow willingly. good leadership and  managment together can build a successful organization

  

6-Although often used interchangeably leadership and management are very different. “management and leadership do share many similar duties which consist of working with people and influencing others to achieve goals. Management skills are used to plan, build, and direct organizational systems to accomplish missions and goals, while leadership skills are used to focus on a potential change by establishing direction, aligning people, and motivating and inspiring.”(Algahtani, 2014). As a nurse leader you can expand your influence to create change and accomplish goals by overlapping your leadership and management skills. By using your leadership skills you can motivate other nurses to go above & beyond when caring for patients and encourage them to come up with solutions to our everyday problems. Leadership skills will also help you when developing your nurses to become future leaders or to meet their personal goals, doing so will in turn provide them with a sense of fulfillment. By using your management skills you can also ensure that your nurses have the adequate staffing and supplies needed to get perform their duties. A managers main function is “to provide services to the community in an efficient and sustainable manner”(Algahtani, 2014).

Algahtani, D. A. (2014). Are Leadership and Management Different? A Review. Journal of Management Policies and Practices,2(3). doi:10.15640/jmpp.v2n3a4

 
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1-Professor, I honestly think my manager is original in the sense that she is both professional and business like, yet human and compassionate. I am referring to the fact that she is not afraid to “jump in” and provide nursing care when the unit in need of an extra hand. I have not heard of another unit in “my” hospital where managers are like my manager. When it comes to listening to nurses and their suggestions, I know of at least one other unit that is managed in a similar way. I feel lucky. 

  

2-you talk about that at your place of work; beverages are not allowed at the nurses’ station only in the break room. That too was true at my place of work, however we recently implemented a hydration station at the nurses’ station, which gives us a place to contain our beverages. There are however specific rules, for example they have to be in a proper beverage container, containing a lid, they have to have the name of the nurse and current date, or else it will get thrown out. According to United States Department of Labor and OSHA (n.d.), they do not have a prohibition against having beverages at the nurses’ station, all they require is that it is not near blood or infectious material and because of that we do have a weekly audit of the hydration station, which verifies that we are in compliance with OSHA. Just a thought. Thank you for sharing.

Reference

Occupational Safety and Health Administration [OSHA]. (n.d.). Requirements for Covered Beverages at the Nurses’ Station. Retrieved from

https://www.osha.gov/laws-regs/standardinterpretations/2006-05-17-1

  

3-in our critical care unit, we have units champions; we have cardiovascular champions, foley catheter champions, infectious disease champions, and they too are encouraged to review evidenced-based practice, present their ideas to the unit educator and unit manager for review and if it turns out to be something that is feasible, they are encouraged to present it to staff. Not too long ago, we had our cardiovascular champions give a brief presentation on transvenous cardiac pacing, and what is needed at the bedside to assist the physician during insertion. 

 
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4-. I just want to add the levels of evidence from strongest to weakest

· Systemic review of experimental studies (well-designed randomized controlled trials [RCTs])

· Meta-analyses of experimental (RCT) & quasi-experimental studies

· Integrative reviews of experimental (RTC) & quasi-experimental studies

· Single experimental study (RCT)

· Single quasi-experimental study

· Meta-analysis of correlational studies

· Integrative reviews of correlational & descriptive studies

· Qualitative research meta-synthesis & meta-summaries

· Single correlational study

· Single qualitative or descriptive study

· Opinions of respected authorities based upon clinical evidence, reports of expert committees (Grove, Gray, & Burns, 2015, p. 24)

Reference

Grove, S. K., Gray, J., & Burns, N. (2015). Understanding nursing research: Building an   evidence-based practice(6th ed.). Retrieved from https://bookshelf.vitalsource.com/ #/books/9781455770601/cfi/20!/4/2/18/18/8/2/4/4/[email protected]:0

  

5-Let me put my share for peer-review as you mentioned this in your post. Feedback from colleagues with similar backgrounds, expertise and knowledge can be a valuable asset. Positive peer reviews contribute to increased funding opportunities, academic advancement and a good reputation. On the other hand, peer reviewers can fall prey to bias, both positive and negative, which can affect the prospects of the research being reviewed, independent of its quality (APA, 2018).

Peer reviewers are expected to meet strict deadlines, which is a challenge when one has numerous responsibilities. Reviewers are also expected to remain impartial during the review, which can be difficult if the research being reviewed is, for example, submitted by a rival researcher. During the review process, the reviewer must knowledgeably assess the quality of the research, honestly judge the importance of the research and must preserve confidentiality. It is essential that researchers are aware of the expectations and commitments required of a peer reviewer prior to becoming one. Although participating in peer review is a way to provide professional service, those who cannot meet the requirements should seriously consider whether being a peer reviewer is right for them.

Reference

American Psychological Association (2018). Peer review. Retrieved from

http://www.apa.org/research/responsible/peer/index.aspx

    

6-You mention Meta-analysis in your post, I have some more information about meta-analysis. Meta-analyses are a subset of systematic review. A systematic review attempts to collate empirical evidence that fits prespecified eligibility criteria to answer a specific research question (NCBI,2018). The key characteristics of a systematic review are a clearly stated set of objectives with predefined eligibility criteria for studies; an explicit, reproducible methodology; a systematic search that attempts to identify all studies that meet the eligibility criteria; an assessment of the validity of the findings of the included studies (e.g., through the assessment of risk of bias); and a systematic presentation and synthesis of the attributes and findings from the studies used. Systematic methods are used to minimize bias, thus providing more reliable findings from which conclusions can be drawn and decisions made than traditional review methods. Systematic reviews need not contain a meta-analysis. there are times when it is not appropriate or possible; however, many systematic reviews contain meta-analyses.

Reference

National Center for Biotechnology Information (2018). Meta-analysis in medical research. Retrieved from

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3049418/

 
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