Systemic research review 2

Order Description

Purpose
The purpose of this assignment is to provide students with practice in identifying, reading, and critiquing systematic research reviews (SRR) related to nursing. A systematic review is defined as “A summary of evidence, typically conducted by an expert or expert panel on a particular topic, that uses a rigorous process (to minimize bias) for identifying, appraising, and synthesizing studies to answer a specific clinical question and draw conclusions about the data gathered (Melnyk & Fineout-Overholt, 2011, p. 582).
You are to use the article attach to this page and critique the last 7 question below. The article is a systemic research review (SRR). Here is some explanation to help with the critique of this articles.
Frequency counts and frequency tables could be developed on all variables but are not as useful as means, modes, and medians when describing continuous variables, such as age or blood pressure. These measures of central tendency help the consumer of research understand the patterns in the data.

The mean of a distribution is simply the average. The standard deviation, a measure of the variation in scores, represents the average amount of deviation of scores or values from the mean. Both the mean and the standard deviation should be reported in statistical results, but the range of scores is also useful in giving the consumer or researcher information regarding the distribution of scores. The mode of a distribution is the most common score. The median of a distribution is the midpoint of a distribution, the point in the distribution where 50% of the scores lie below and 50% of the scores lie above.

Calculating probabilities is dependent primarily upon the mean and standard deviation of a collection of scores, which is the probability distribution. The probability of a specific outcome is the proportion of times that the outcome would occur most of the time in repeated interval observations. A simple example is tossing a coin 10 times. The probability is that the outcome results in heads four times and tails six times. However, if the coin is tossed 100 times, the probability of the outcome being heads will be 50% of the time and tails the other 50% of the time. This is considered the law of large numbers.

The probability distribution of a continuous variable (in the above example, coins tossed) lists the possible outcomes together with their probabilities. A probability distribution has parameters describing the central tendency (mean) and variability (standard deviation). When the values for a continuous variable are graphed, a normal probability distribution is the result. The properties and characteristics of the normal probability distribution are the following.

Bell-shaped and symmetrical
The empirical rule for normal distribution consists of the following:
68.2% of the population measurements lie within one standard deviation of the mean.
95.4% of the population measurements lie with two standard deviations of the mean.
99.7% of the population measurements lie within three standard deviations of the mean

Although descriptive statistics are very useful because they show the structure and shape of the findings from a research study and they illustrate any trends over time or differences among groups, these statistics only describe the sample. By themselves, descriptive statistics are only an estimate of a possible data point in the population; they do not give an indication of how likely that point estimation reflects the true value in the population.

In an effort to indicate how likely a point estimate like a mean value is, interval estimation can be used by constructing a confidence interval (CI) around a point estimate. A range is calculated around a mean value or odds ratio. The two most common CIs are 95% and 99%. A 95% CI means that out of 100 repetitions of a study, the true value in the population would be in the middle 95% of the distribution. A 99% CI means that out of 100 repetitions of a study, the true value in the population would be in the middle 99% of the distribution.

Another aspect of interpreting the confidence interval is that the wider the interval, the less useful the point estimate is because the point estimate is less precise. In addition, if the CI for a mean difference between groups contains zero (0), then the results will probably not have statistical significance (the null hypothesis of no difference would be true if the mean difference was zero). If the interval for an odds ratio contains one (1), then the results will probably not be statistically significant (the null hypothesis of no difference would be true if an odds ratio was one [either event is equally likely]).
Inferential statistics are used to determine how confident we can be that the descriptive statistics obtained from the sample can be inferred to the population. It usually is not practical to study an entire population. As a result, inferential statistical tests were developed to determine the probability that the findings from the sample in a study can be inferred to the population. In other words, inferential statistical tests determine whether the same differences or similarities in descriptive statistics obtained from the sample would be found in the population if the entire population were studied. Thus, inferential statistics help us infer from the sample to the population.

All significance tests have five components: assumptions, hypothesis, p-value, level of significance, and test statistics.

Assumptions refer to suppositions about the type of data included in a study, the population distribution, characteristics of the population, the randomness of the sample, sample size, and the underlying theory being tested. We tend to assume that the sample represents the population in inferential statistics.
The hypothesis is the scientific method used to make a prediction about a population parameter. A parameter can be a mean, median, or proportion. The tentative prediction is tested based on the measure of the variable obtained from a sample. Once the hypothesis is identified, the researcher will perform experiments to either prove or disprove the hypothesis.

The null hypothesis is symbolized by Ho. The null hypothesis is the hypothesis that an intervention does not affect an outcome or that a relationship does not exist. The decision based on inferential testing is either to reject the null hypothesis or fail to reject the null hypothesis. An example of a null hypothesis is, “Nurses working at Magnet hospitals do not score higher on job satisfaction than nurses working at non-Magnet hospitals.” Researchers typically do not believe their null hypotheses but state their hypotheses negatively because proving that something is true is never possible.

The alternative hypothesis is symbolized by Ha. It is the hypothesis that contradicts the null hypothesis and is also known as the research hypothesis. An example of an alternative hypothesis is, “Nurses working at Magnet hospitals score higher on job satisfaction than nurses working at non-Magnet hospitals.” The decision as to whether to use a null or an alternative hypothesis, or both, belongs to the researcher.

The level of significance is represented by the Greek letter alpha (a). The two most common alpha levels are 0.05 and 0.01. Of these alpha levels, 0.05 is the more commonly used. If an alpha level is not specified in a published research article, then it is assumed to be 0.05.

Going back to the normal distribution, the area under the curve of a probability distribution is the probability of any value falling in that area. If the test statistic falls in the critical region beyond the tails (p= 0.05 or 0.01), the probability of that happening by error is acceptably small and the findings of the analysis are statistically significant. (See the above normal distribution illustration.)

The p-value summarizes the evidence in the data about the null hypothesis. The p-value is the probability, if Ho is true, that the test statistics would fall in this value.

For example, a p-value of 0.26 indicates that the observed data would not be unusual if Ho were true. However, if the p-value equaled .01, then the data would be very unlikely and would provide strong evidence against Ho.
Thus, if a p-value in the hypothesis example is .01, then the alternative hypothesis would be true. Using the previous example, with a p-value of .01, nurses at Magnet hospitals would score higher on a job satisfaction survey in comparison to nurses at non-Magnet Magnet hospitals, and the higher scores are not likely due to chance.
The test statistic is the statistical calculation from the sample data to test the null hypothesis (e.g., t-test, chi square tests). Researchers have developed hundreds of test statistics designed to detect relationships or differences in their data. We will cover the most common test statistics in the following section.

Utilize the Cochrane Database of Systematic Reviews to locate a true SRR for this assignment.
Paper length should be between 4-6 pages.

Book: Melnyk, B. M., & Fineout-Overholt, E. (2011). Evidence-based practice in nursing & healthcare (2nd ed.). Philadelphia, PA: Wolters Kluwer/Lippincott, Williams, & Wilkins

Please follow Rubric below for specific instruction:
1: Describes the relevance of the research problem addressed in the SRR to practice: Fully describes the relevance of the research problem.
2: Critiques the research rigor of the studies used in the SRR (see lesson’s levels of evidence): Fully critiques the rigor and levels of evidence of the studies used in the SRR.
3: Critiques the levels of evidence specifically the designs of the studies included in the SRR: Fully critiques the levels of evidence of the studies used in the SRR.
4: Critiques the clarity with which the studies are presented and critiqued: Fully describes the clarity with which the studies are presented and critiqued.
5: Describes the overall findings of the studies, as summarized in the SRR: Fully describes the overall findings of the studies summarized in the SRR. Critique the conclusions of the SRR, with implications for your current practice and future research.
6: Critiques the conclusions of the SRR, with implications for your current practice and future research: Critiques the conclusions of the SRR, with implications for current practice and future research. Critique the clarity with which the studies are presented and critiqued. (CO 6)
7: Uses appropriate grammar, syntax, and spelling.

Systemic research review 2

Order Description

Purpose
The purpose of this assignment is to provide students with practice in identifying, reading, and critiquing systematic research reviews (SRR) related to nursing. A systematic review is defined as “A summary of evidence, typically conducted by an expert or expert panel on a particular topic, that uses a rigorous process (to minimize bias) for identifying, appraising, and synthesizing studies to answer a specific clinical question and draw conclusions about the data gathered (Melnyk & Fineout-Overholt, 2011, p. 582).
You are to use the article attach to this page and critique the last 7 question below. The article is a systemic research review (SRR). Here is some explanation to help with the critique of this articles.
Frequency counts and frequency tables could be developed on all variables but are not as useful as means, modes, and medians when describing continuous variables, such as age or blood pressure. These measures of central tendency help the consumer of research understand the patterns in the data.

The mean of a distribution is simply the average. The standard deviation, a measure of the variation in scores, represents the average amount of deviation of scores or values from the mean. Both the mean and the standard deviation should be reported in statistical results, but the range of scores is also useful in giving the consumer or researcher information regarding the distribution of scores. The mode of a distribution is the most common score. The median of a distribution is the midpoint of a distribution, the point in the distribution where 50% of the scores lie below and 50% of the scores lie above.

Calculating probabilities is dependent primarily upon the mean and standard deviation of a collection of scores, which is the probability distribution. The probability of a specific outcome is the proportion of times that the outcome would occur most of the time in repeated interval observations. A simple example is tossing a coin 10 times. The probability is that the outcome results in heads four times and tails six times. However, if the coin is tossed 100 times, the probability of the outcome being heads will be 50% of the time and tails the other 50% of the time. This is considered the law of large numbers.

The probability distribution of a continuous variable (in the above example, coins tossed) lists the possible outcomes together with their probabilities. A probability distribution has parameters describing the central tendency (mean) and variability (standard deviation). When the values for a continuous variable are graphed, a normal probability distribution is the result. The properties and characteristics of the normal probability distribution are the following.

Bell-shaped and symmetrical
The empirical rule for normal distribution consists of the following:
68.2% of the population measurements lie within one standard deviation of the mean.
95.4% of the population measurements lie with two standard deviations of the mean.
99.7% of the population measurements lie within three standard deviations of the mean

Although descriptive statistics are very useful because they show the structure and shape of the findings from a research study and they illustrate any trends over time or differences among groups, these statistics only describe the sample. By themselves, descriptive statistics are only an estimate of a possible data point in the population; they do not give an indication of how likely that point estimation reflects the true value in the population.

In an effort to indicate how likely a point estimate like a mean value is, interval estimation can be used by constructing a confidence interval (CI) around a point estimate. A range is calculated around a mean value or odds ratio. The two most common CIs are 95% and 99%. A 95% CI means that out of 100 repetitions of a study, the true value in the population would be in the middle 95% of the distribution. A 99% CI means that out of 100 repetitions of a study, the true value in the population would be in the middle 99% of the distribution.

Another aspect of interpreting the confidence interval is that the wider the interval, the less useful the point estimate is because the point estimate is less precise. In addition, if the CI for a mean difference between groups contains zero (0), then the results will probably not have statistical significance (the null hypothesis of no difference would be true if the mean difference was zero). If the interval for an odds ratio contains one (1), then the results will probably not be statistically significant (the null hypothesis of no difference would be true if an odds ratio was one [either event is equally likely]).
Inferential statistics are used to determine how confident we can be that the descriptive statistics obtained from the sample can be inferred to the population. It usually is not practical to study an entire population. As a result, inferential statistical tests were developed to determine the probability that the findings from the sample in a study can be inferred to the population. In other words, inferential statistical tests determine whether the same differences or similarities in descriptive statistics obtained from the sample would be found in the population if the entire population were studied. Thus, inferential statistics help us infer from the sample to the population.

All significance tests have five components: assumptions, hypothesis, p-value, level of significance, and test statistics.

Assumptions refer to suppositions about the type of data included in a study, the population distribution, characteristics of the population, the randomness of the sample, sample size, and the underlying theory being tested. We tend to assume that the sample represents the population in inferential statistics.
The hypothesis is the scientific method used to make a prediction about a population parameter. A parameter can be a mean, median, or proportion. The tentative prediction is tested based on the measure of the variable obtained from a sample. Once the hypothesis is identified, the researcher will perform experiments to either prove or disprove the hypothesis.

The null hypothesis is symbolized by Ho. The null hypothesis is the hypothesis that an intervention does not affect an outcome or that a relationship does not exist. The decision based on inferential testing is either to reject the null hypothesis or fail to reject the null hypothesis. An example of a null hypothesis is, “Nurses working at Magnet hospitals do not score higher on job satisfaction than nurses working at non-Magnet hospitals.” Researchers typically do not believe their null hypotheses but state their hypotheses negatively because proving that something is true is never possible.

The alternative hypothesis is symbolized by Ha. It is the hypothesis that contradicts the null hypothesis and is also known as the research hypothesis. An example of an alternative hypothesis is, “Nurses working at Magnet hospitals score higher on job satisfaction than nurses working at non-Magnet hospitals.” The decision as to whether to use a null or an alternative hypothesis, or both, belongs to the researcher.

The level of significance is represented by the Greek letter alpha (a). The two most common alpha levels are 0.05 and 0.01. Of these alpha levels, 0.05 is the more commonly used. If an alpha level is not specified in a published research article, then it is assumed to be 0.05.

Going back to the normal distribution, the area under the curve of a probability distribution is the probability of any value falling in that area. If the test statistic falls in the critical region beyond the tails (p= 0.05 or 0.01), the probability of that happening by error is acceptably small and the findings of the analysis are statistically significant. (See the above normal distribution illustration.)

The p-value summarizes the evidence in the data about the null hypothesis. The p-value is the probability, if Ho is true, that the test statistics would fall in this value.

For example, a p-value of 0.26 indicates that the observed data would not be unusual if Ho were true. However, if the p-value equaled .01, then the data would be very unlikely and would provide strong evidence against Ho.
Thus, if a p-value in the hypothesis example is .01, then the alternative hypothesis would be true. Using the previous example, with a p-value of .01, nurses at Magnet hospitals would score higher on a job satisfaction survey in comparison to nurses at non-Magnet Magnet hospitals, and the higher scores are not likely due to chance.
The test statistic is the statistical calculation from the sample data to test the null hypothesis (e.g., t-test, chi square tests). Researchers have developed hundreds of test statistics designed to detect relationships or differences in their data. We will cover the most common test statistics in the following section.

Utilize the Cochrane Database of Systematic Reviews to locate a true SRR for this assignment.
Paper length should be between 4-6 pages.

Book: Melnyk, B. M., & Fineout-Overholt, E. (2011). Evidence-based practice in nursing & healthcare (2nd ed.). Philadelphia, PA: Wolters Kluwer/Lippincott, Williams, & Wilkins

Please follow Rubric below for specific instruction:
1: Describes the relevance of the research problem addressed in the SRR to practice: Fully describes the relevance of the research problem.
2: Critiques the research rigor of the studies used in the SRR (see lesson’s levels of evidence): Fully critiques the rigor and levels of evidence of the studies used in the SRR.
3: Critiques the levels of evidence specifically the designs of the studies included in the SRR: Fully critiques the levels of evidence of the studies used in the SRR.
4: Critiques the clarity with which the studies are presented and critiqued: Fully describes the clarity with which the studies are presented and critiqued.
5: Describes the overall findings of the studies, as summarized in the SRR: Fully describes the overall findings of the studies summarized in the SRR. Critique the conclusions of the SRR, with implications for your current practice and future research.
6: Critiques the conclusions of the SRR, with implications for your current practice and future research: Critiques the conclusions of the SRR, with implications for current practice and future research. Critique the clarity with which the studies are presented and critiqued. (CO 6)
7: Uses appropriate grammar, syntax, and spelling.