The practice of nursing research: Appraisal, synthesis, and generation of evidence. Amsterdam, Netherlands: Elsevier Health Sciences.

nursing research

TEXTBOOK:Grove, S. K., Burns, N. & Gray, J. (2012). The practice of nursing research: Appraisal,
synthesis, and generation of evidence. Amsterdam, Netherlands: Elsevier Health Sciences.

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From your textbook, The Practice of Nursing Research: Appraisal, Synthesis, and Generation of Evidence, read:

• Introduction to Statistical Analysis
• Using Statistics to Describe Variables
• Using Statistics to Examine Relationships
• Using Statistics to Predict
Data Analysis Part One

After collecting empirical data, quantitative researchers use statistical methods and hypotheses to establish relationships or describe the sample. Most quantitative nursing research studies use basic statistical tests, such as chi-square, t-tests, ANOVA, and Pearson’s Correlation.
Understanding statistics in a research study can seem overwhelming if you are not comfortable with numbers or the manner in which statistics are approached. For example, size of the sample is very important because you need to know if there were enough participants in a study to make generalizations to other populations. Selecting the correct sample size requires you to deal with numbers.
So, let us find answers to some questions which are related to quantitative statistics:
• What is a normal curve?
The normal curve represents a theoretical frequency where the mean, median, and mode all fall in the center of the curve and the remainder of the values fall in a “normal” distribution to make a bell-shaped curve.
• What are confidence intervals?
A confidence interval lets us infer how close the sample mean is to the theoretical population mean. Since we can never measure the entire population, we estimate the population values by using the values of the sample. The two common numbers you will see when evaluating research will be a 95% confidence interval, which means the scores in the distribution are +/- 1.96 standard deviations from the mean and a 99% confidence interval which means the scores in the distribution are +/- 2.58 standard deviations from the mean.
• Types of Error
Error is classified as Type I or Type II error, and the classification is based on the null hypothesis.
o Type I Error—Type I error occurs when the researcher rejects a true null hypothesis, i.e., there is no relationship between the variables (the null hypothesis), but the researcher rejects the null hypothesis and states there is a relationship between the variables. The probability of making a Type I error is called α (alpha).
o Type II Error—Type II error occurs when the researcher accepts a false null hypothesis. The researcher agrees with the null hypothesis that no relationship between variables exists when in reality there are significant relationships between the variables.
o Decreasing Type I Error by setting the p value higher, i.e., setting the level of significance at .01 instead of .05, can increase the risk of a Type II error and make it more difficult to find significant results. You can reduce the risk of a Type II error by increasing sample size. You can find a more in-depth explanation of this topic in a statistics text book.
• What is power?
Power is the more likely a test is to find a statistically significant result when one actually exists. There are four components to determining power: the significance level, the sample size, the effect size, and the power (.80 is standard in nursing research). Power analysis is a statistical procedure used to determine sample size, since knowing three of the components will find the fourth.
• What is the difference between a one-tailed and a two-tailed test?
If you have a directional hypothesis you will generally use a one-tailed test; for all others use a two-tailed test. The “tails” refer to the ends of the normal distribution curve.
• What are descriptive statistics?
Descriptive statistics describe a sample and population.
• What are inferential statistics?
Inferential statistics are calculations on the data that allow you to make generalizations about the population.
• What is “significance level” (alpha) and the p value?
The term significance level (alpha) is used to refer to a pre-chosen probability (generally p < .05 in nursing research) and the term “P value” or calculated probability is the estimated probability of rejecting the null hypothesis (H0) of a study question when that hypothesis is true.
• How do you interpret an r value?
An r value is the correlation coefficient. The r value tells you the strength of the relationship between an independent and dependent variable or between two sets of numbers. R values range from -1 to 1. Read the table below to understand how you read the score.
o Value of r Strength of the Relationship
o 0 No correlation
o Closer to -1 Negative correlation
o Cloer to 1 Positive correlation