NUR 408 Final exam Exam All Possible Questions and Answers with complete solution

Descriptive statistics Inferential statistics - __ are used to synthesize and describe data. It allows us to draw conclusions from the use of graphs, charts, and so on. __are used to make inferences about the population based on sample data. It allows us to say whether difference is significant. They infer or draw a conclusion, based on laws of probability. Mode Median--doesnt take into consideration outliers Mean - Differential: The __ is the number that occurs most fq in distribution. In the following, it is 53: 50, 51, 52, 53, 53, 53, 54, 54, 55. Most popular value. The __ is the point in a distribution that divides scores in half. Consider: 2 2 3 3 4 5 6 7 8 9--it would be 4.5. The __ equals the sum of all values divided by the number of participants. Referred to as the average. Standard deviation - Differential: Like the mean, the __is calculated based on every value in a distribution. Summarizes the average amount of deviation of values from the mean. It is abbreviated as s or SD. The mean tells us the best value for summarizing an entire distribution, and an SD tells us how much on avg the scores deviate from the mean. It can be interpreted as our degree of error when we use a mean to describe entire sample. +1 or -1 means person achieved higher or lower score than 68%, +2 or -2 is 95%, and +3 or -3 is 99%. Crosstabulated - A crosstabs table or contingency table is a two dimensional fq distribution in which the fq of the two variables are __. Used for nominal or ordinal data. W M TotalNon smoker:n=10 45% n=6 27% n=16 36% Nominal measurement Interval measurement - Statistical operations depend on a variable's level of measurement. There are four major classes or levels of measurement. __ is the lowest level, involves using numbers simply to categorize attributes. Gender and blood type are examples of this. Ordinal measurement ranks people based on relative standing on an attribute, for ex: ADL: 1=complete dependent, 2=need another person for assist, 3=need mechanical assist, 4=complete independent=numbers signify incremental ability to perform ADL. __ occurs when researchers can rank ppl on an attribute and specify the distance between them, for ex: standardized test a 140 is higher than 120, which is higher than 100--measurements can be avg. Finally, ratio measurement is the highest level, they have a meaningful zero and thus provide info about absolute magnitude of the attribute. The Fahrenheit scale has a zero and weight scale and pulse has a zero. 60 degrees is twice as hot as 30 and 200 lbs is twice as heavy as 100. It can go into the negatives* Negative or inverse relationship Positive relationship - Correlation coefficients between .00 and -1.00 express a __. One variable increases in value as the other decreases in value. (exercise and weight). Correlation coefficients between 0.00 and +1.00 express __. In this, both variables increase (increase calories and weight). The greater the absolute value is, the stronger the relationship (disregard signs): -.45 is stronger than +.40. .05; .05 - Statistical hypothesis testing uses objective criteria for deciding whether research hypotheses should be accepted as true or rejected as false. The null hypothesis states there is no relationship between the independent and dependent variable. If p value is less than __ than you are going to reject the null hypothesis because it shows there is a difference so its significant. If it were greater than __ than you would accept the null hypothesis, because it shows no difference between the IV and DV; could have happened by chance, so it is true. The greater is rises above, the less significant it is. Type 1 error; type 2 error - Researchers make a __ when they reject a null hypothesis that is in fact true. It is a false positive. Researchers control the risk for this by selecting a level of significance, which is the probability of making a Type 1 error. Two most fq used are alpha .05 and .01--the risk is lower w .01. Sates that w 100 samples, either it would be wrongly rejected 5 or 1 time. A __ is when you accept a false null hypothesis.P value - The __ shows the actual probability that the relationship being tested is due to chance. For example is it is .025, then this means less than 3 times out of 100 would a group difference occur by chance. Then it is compared to the alpha level; .025 is less than 0.05 so it is significant--anything greater than .05 would be nonsignificant. The smaller that p value the more confident we are to reject the null hypothesis. If <.05 reject the null hypothesis. If greater than .05 accept. T-test - __ is a parametric test for testing the significance of differences in two group mean on an outcome. The test variable (DV) must be interval level and the IV is categorical (group). If the two mean scores come from an independent group then perform an independent t-tes (men versus women). If means are from related set then dependent (paired) t-test (means for pt before and after surgery). Early discharge moms and late discharge mom--find mean for each group. Independent t-test - __ tests for a difference in mean scores from two unrelated independent groups. Data in the one group is not in any way influenced by the other group. Most common use is in experimental design studies (tx versus control group). Remember in the pre-test you do not want differences bc you are trying to establish equivalency for the two groups. In the post-test you want there to be a statistical significance bc you want to be able to attribute the difference to your intervention. Dependent t-test - __ or paired test is an inferential test, you test related to groups data. Most common is pre-test/post-test scores on same individuals; can also be two different measures on the same person (blood glucose by stick or by lab value). Scores are compared to see if there is a difference. Also look at mean differences of the one group are compared to the mean difference of the other group. You want to see a significant p value to detect that the mean of the differences of the pairs is not equal to 0. Pearson's r correlation - __ is both descriptive and inferential. As a descriptive statistic, r summarizes magnitude and direction of a relationship between two variables. As an inferential statistic, r tests hypotheses about population correlations; the null hypothesis is that there is no relationship between two variables, that is the population is r=.00. Possible values range from -1 to +1. The closer to one on either side, means a stronger relationship (ignore signs)=-.7 is stronger than .5. A positive r means the relationship is positive or direct, value of x increase, y increase. A negative r means the relationship is inverse or negative, meaning x increase, y decreases. Chi-square - __ is used to test hypotheses about the proportion of cases in different categories, as in cross tabulation. Compares observed frequencies in each cell w expected frequencies of two categorical variables--fq are expected if there was no relationship. Asks the question are the frequencies (numbers) occurring independently of one another or is there an influence. Cross tabulation table is the data display format. It is non-parametric test.ANOVA - __ is used to test mean group differences of three or more groups. Sorts out the variability of an outcome variable into two components: variability due to the IV (experimental group status) and variability due to all other sources (individual differences, measurement error). Variation between groups is contrasted w variation within groups to yield an F ratio statistic. A significant overall ANOVA tells you there is a difference in the means scores.