C431 Healthcare research, statistics exam with 100% correct answers already graded A+

Inferential statistics used to make predictions based on probability. Inferential statistics focus on the probability that the observed outcome happened by random chance. allow healthcare managers to make industry-wide generalizations from a small slice of data. Statistical tests Healthcare managers must be well versed in statistical tests in order to verify that data within a study. Inferential statistics can be used for: measure the accuracy of diagnostic procedures, the efficacy of treatment interventions, the impact of risk factors on a given outcome, or the efficacy of a preventive strategy to reduce the burden of disease 3 major phases of clinical research (1) identifying the need, (2) designing the study, and (3) performing the study. empirical treatment clinicians provide treatment based on the patient's history and physical exam, rather than on lab tests, imaging, or other studies. If a patient responds positively to the treatment, then the diagnosis is confirmed truncated trial Trials that end early instrumentation determining the instruments to be used for data collection Parametric statistical tests • Ratio or interval scale. • Normal in shape (or nearly normal). • Sufficient sample size. • If there is more than one group, each group has the same (or nearly the same) amount of variability, the same shape, and the same (or nearly the same) sample size. Nonparametric statistical tests • Nominal or ordinal scale. • Non-normal shape (skewed). • Small sample size. • If there is more than one group, the groups have differing variability, shapes, or sample sizes. t-test a statistical test used to evaluate the size and significance of the difference between two means six assumptions for the independent samples t-test 1. Each variable has a sufficient sample size to enable accurate measurement of a mean. 2. The data are ratio or interval scale (which is necessary to calculate a mean). 3. There are only two measurements to be compared. 4. The two measures are independent of one another. 5. The variation within the data set for each variable is similar between the two measures. 6. Each data set has a normal-shaped distribution. effect size referring to the expected amount of clinically important effect p-value The probability level which forms basis for deciding if results are statistically significant (not due to chance). If your experimental design has high power, then there is a high chance that your experiment will find a 'statistically significant' result if the treatment really works simple random sample every member of the population has a known and equal chance of selection stratified random sample sampling frame is divided into strata (groupings) and a random sample is drawn from each stratum multi stage sampling occurs in one or more steps (stages). This procedure often is used when dealing with very large sampling frames and when researchers want to ensure that every subgroup is included. convienence sampling sampling frame on individuals to whom they have easy access purposive sampling the researcher's knowledge of the population and its elements is used to hand-pick the cases to be included in the sample snowball sampling used in quantitative research when the researchers are interested in studying a population that is difficult to identify, such as gang members, IV drug users, or illegal aliens. The difference between the purposive sample and the snowball sample is the characteristic of statistical power. Alpha (α) probability of a Type I error. It is often referred to simply as probability. It is the chance that a researcher will incorrectly reject the null hypothesis H0 The null hypothesis that the research reveals no effect. Alpha Halves The risk of a Type I error when a test of equality is performed. If equality is tested the researchers assume that either group could turn out to be superior, in which case alpha should be divided in half. The Normal Curve includes all of the "normal" results that occur in the population, even those that might represent only a very small proportion of the population 68-95-99.7 rule in a normal model, about 68% of values fall within 1 standard deviation of the mean, about 95% fall within 2 standard deviations of the mean, and about 99.7% fall within 3 standard deviations of the mean Chi-square test A significance test used to determine if a linear relationship exists between two variables measured on interval or ratio scales. confidence interval statistical range, with a given probability, that takes random error into account test for differences compares a statistic, such as a mean or a proportion, between two or more groups independent-samples t-test used to compare two means between different conditions one sample t-test quantitative test that compares the mean score of a sample to the known mean (normative or expected) value from a trustworthy source paired-samples t-test type of repeated measures test where measures are taken over time and the results are compared from one point in time to another. ANOVA (analysis of variance) an inferential statistical test for comparing the means of three or more groups Chi-square, goodness-of-fit tests group of tests generally used with qualitative or non-normal data. most common use of the chi-square test is to test for differences between two groups of count data χ2 Chi-square tests for relationships used to determine if variables change in relationship to other variables, positive relationship exists when an increase in one variable is associated with an increase in another. A negative relationship exists when an increase in one variable is associated with a decrease in another. Pearson's correlation coefficient (symbolized with "r") test for relationship between two variables that are interval or ratio data types. It is a particular type of regression analysis that is used very often regression analysis determines the line that best fits within a set of coordinates. factor analysis procedure for combining items in a survey instrument to represent a single concept (called a construct or domain) The authors of a study set alpha at 0.05. If the null hypothesis was a test of equality, what is alpha halves? 0.025