Chapter 11: Understandign Statistics In Reaseach Flashcards
What theory is used to explain a relationship, the probability of an event occurring in a given situation, or the probability of accurately predicting an event ?
(Key points)
Probability theory
What theory assumes that all the groups in a study used to test a particular hypothesis are components of the same population in relation to the study variables
(Key points)
Decision theory
What error occurs when the null hypothesis is rejected, although it is true. The researchers
conclude that significant results exist in a study, when in reality they do not. The risk of this type is indicated by the level of significance (a). (False positive)
(Key points)
Type I
What error (β) occurs when the null hypothesis is accepted when it is actually false. The re-
searchers conclude that the study results are nonsignificant when the results are significant.
This errors often occur because of flaws in the research methods, and their risk can be exam-
ined using power analysis. (False negative)
(Key points)
Type II
Quantitative data analysis includes the following steps: (5)
Key points
(1) management of missing data;
(2) description of the study sample;
(3) examination of the reliability of measurement methods;
(4) performance of exploratory analyses of the data; and
(5) conduct of inferential analyses guided by the hypotheses, questions, or objectives.
Which statistic includes frequency distributions, percentages, measures of central tendency, measures of dispersion, and scatterplot they are calculated to describe the sample and key study variables.
(Key points)
Descriptive or summary statistics
Which analysis was conducted to examine relationships that are covered in this text include
Pearson product-moment correlation and factor analysis.
(Key points)
Statistical
Which analysis is conducted to predict the value of one dependent variable using one or
more independent variables.
(Key points)
Regression analysis
Which analyses is conducted to examine group differences and determine causality included in
this text are the chi-square test, t-test, ANOVA, and ANCOVA.
(Key points)
Statistical
Interpretation of results from quasi-experimental and experimental studies is traditionally
based on decision theory, with five possible results:
(Key points)
(1) significant results predicted by the researcher;
(2) nonsignificant results;
(3) significant results that are opposite from those predicted by the researcher;
(4) mixed results; and
(5) unexpected results.
What usually includes findings, significance of findings, limitations, conclusions,
generalization of findings, implications for nursing, and recommendations for further studies.
(Key points)
Research outcomes
When should you evaluate the appropriateness and completeness of the
researchers’ results and discussion sections ?
(Key points)
In critically appraising a study
Is a theoretical frequency distribution of all possible values in a population.
Normal curve
In a normal distribution curve what is equal?
The mode, mean, and median
What is based on the logic of the normal curve
Levels of significance and probability
What is calculated based on the theory of the normal curve
The probability that any data score will be within a certain range of a mean value
is referred to as alpha (a) which is the probability level at which the results of statistical analysis are judged to indicate a status ally significant difference between the groups.
Level of Significance (the cutoff point) :cutoff point
Two-tailed test of significance =
the analysis of nondirectional hypothesis
One-tailed test of significance =
the analysis of directional hypothesis
Which tailed statistic test is uniformly more powerful
One-tailed
There is a greater risk of a type I error with:
A 0.05 level of significance (5 errors in 100) than with a 0.01 level of significance (1 chance for error in 100)
There is a greater risk of a type II error when:
The significance if 0.01 than when it is 0.05
is the probability that a statistical test will detect a significant difference if one exists (The probability of correctly rejecting H0= 1-β (power).
Power
What can be determined using power analysis ?
The risk of a type II error
What are the four parameters of a power analysis:
- The level of significance (α = 0.05)
- Sample size
- Power (minimum acceptable power is = 0.80 (80%)
- Effect size (> 0.50 : large)
Describes the occurrence of scores or categories in a study
Frequency distribution
Types of frequency distribution
- Ungrouped frequency distribution: in which a table is developed to display all numerical values obtained for a particularvariable. This approach is generally used on discrete rather than continuous data. Examples of data commonly organized in this manner are gender, ethnicity, marital status, diagnoses of study participants, and values obtained from the measurement of selected research and dependent variables.
- Grouped frequency distribution : are used when continuous variables are being examined. Many
measures taken during data collection, including body temperature, vital lung capacity, weight
age, scale scores, and time, are measured using a continuous scale. - Percentage distribution: indicates the percentage of study participants in a sample whose scores
fall into a specific group and the number of scores in that group, Percentage distributions are par
ticularly useful for comparing the present data with findings from other studies that have difleren
sample sizes.
What were the measured of central tendency
- The mean, used only for ratio/interval data, is the average of all values. Outliers do affect the mean value.
- The median, used for ordinal data, is the middle value. If n is an even number, there are two middle values; their average is the median. Outliers do not affect the median value.
- The mode, used for nominal data, is the most commonly occurring value. There may be more than one mode, of course. Outliers do not affect the mode.
-In a perfectly normal distribution, the mean is also the median, and is also the mode.
What are the measures of dispersion:
- Range: is obtained by subtracting lowest score from the highest score
- Variance: indicates the spread of dispersion of the score
- Standard deviation: the square root of the calculated value of the variance, indicates the average difference between each data point and the mean of a data set.
- Confidence interval: when the probability of including the value of the population within an interval estimate is known
- Standardized scores: expressed raw scores as deviations from the mean, so that scores across different instruments can be compared: GPA verses SAT, for instance Raw scores that cannot be compared and are transformed into standardized scores,Common standardized score is a Z-score
- Scatterplot: is a visual representation of data, on a scaled graph, with two axes. Used to display matched values- height versus weight, for instance. Unless drawn to scale, a scatterplot is only fairly good at displaying dispersion, and provides no quantification
are used to test an actual or implied hypothesis emanating from the research purpose – those testing relationships and difference are the primary ones. Infer or address objectives, questions, and hypothesis
Inferential statistics
What statistics test are conducted to examine differences
Chi-square test
T-test
Analysis of variance (ANOVA)
Determines wether two variables are independent or related; test can be used with nominal or ordinal
Chi square test
One of the most common analysis conducted to test for significan differences between two samples is …
The t-test
Is a parametric statistical technique conducted to examine differences among three or more groups
ANOVA
Statistics that are conducted to predict outcomes
Regression analysis
Is used to predict the value of one variable when the value of one or more other variables is known
Regression analysis
Statistics conducted to examine relationships:
Pearson product-moment correlation
Factor analysis
Is an inferential analysis technique conducted to e mine bisaría te correlations in studies
Person product-moment correlation
Commonly conducted to examine the interrelationships among large numbers of items on a scale and disentangles those relationships to identify clusters of items that are most closely linked
Factor analysis
