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| Use of best clinical evidence in making patient care decisions (We use it everyday!) |
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| It answers our 3 questions |
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| What are the 3 questions? |
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How reliable is the evidence?
What is the magnitude of effects?
How precise is the estimate of effects? |
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| The term for a collection of mathematical methods of organizing, summarizing, analyzing, and interpreting informatino gathered in a study. Not Math. |
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| Why are we studying statistics?* |
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| To do nursing research so we're able to generate evidence that can contribute to EBP for nurses |
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| something that takes on different values |
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| height, sex, and respiratory rate vary from one person to the next |
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| The hypothesized cause of, or influence, on an outcome. |
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| The outcome of interest, hypothesized to depend on, or be caused by, the independent variable |
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| Are the queries researchers seek to answer through the collection and analysis of data |
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| What two components make up a research question? |
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| Variables and the population (the entire group of interest) |
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| Categories are indivisible, with a finite number of values between two points (must be a whole number) |
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| Discrete Variable example |
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| Number of siblings (has to be a whole number) |
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| Can (in theory) assume an infinite number of values between two points |
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| Continuous Variable (think of a clock) |
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| Example of Continuous Variable |
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| Time elapsed since birth (55.0384 years) |
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Lowest form of measurement
Numbers are used simply as labels to name categories
Just assigning a number (doesn't show distance cannot be calculated) #'s used as labels |
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Uses numbers to designate ordering on an attribute
Conveys some information about amount
But does not indicate distance between values
(ex. degree of pain) |
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Also uses numbers to designate ordering on an attribute and conveys information about amount
Distance between values are assumed to be equal
Averages can be computed |
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| Interval Measurement (like Temperature) |
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Uses number to designate ordering, conveys information about amount, distances are equal
And there is a real, rational zero
Averages can be computed
ex. Medication dose |
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Based on laws of probability, help researchers draw objective conclusions about a population, using data from a sample
Used to test hypotheses (predictions) about relationships between variables |
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Researchers collect their data from a sample of study participants-a subset of the population of interest.
Describes and summarize data about the sample. |
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| Statistical procedures for analyzing a single variable at a time. |
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When three or more variables are included in the same analysis
(researchers may use sex, height, weight, activity level) |
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| A systematic arrangement of data values, with a count of how many times each value occurred in data set. |
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| Two components of Frequency distribution |
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| Relative frequencies and cumulative relative frequencies |
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| Cumulative relative frequencies |
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| the percentage for a given score value combined with percentages for all preceding values |
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| Frequency distributions can be constructed for variables measured at any level of measurement but... |
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| for categorical (nominal level) variables, cumulative frequencies do no make sense. (why? THEY DON'T COMPUTE!) |
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Frequency Distributions on pages 24-28
How to use and construct it |
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| Page 29 and 30 read about symmetry, skewness, and kurtosis(especially) |
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| Page 32 Construct a chart similar to this on the exam |
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| Come from the center of distribution of data values, indicating what is "typical", and where data values tend to cluster |
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Central Tendency
Called average |
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| Two havles of the distribution folded over in the middle, are identical |
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| Peaks are "off center" and there is a tail trailing off for data values with low frewquency |
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| Degree of pointedness of flatness of the distribution's peak |
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| What are the three alternative indexes? |
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| The mode, median, and mean |
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| The score value with the highest frequency: the most "popular" score |
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Can be used with data measured on any measurement level (including nominal level)
Easy to Compute
Reflects an actual value in the distribution so it is easy to understand
Useful when there are 2+ popular scores (i.e. multimodal distributions) |
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Ignores most information in the distribution
Tends to be unstable (value varies from one sample to the next)
Sometimes there may not be a mode |
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| The score that divides the distribution into two equal halves |
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not influenced by outliers
Good index of what is typical when distribution is skewed
Easy to "compute"
Appropriate when data are ordinal level |
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Does not take actual data values into account-only an index position
Value of median not necessarily an actual data value, so it is more difficult to understand than mode |
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The arithmetic average
Most frequently used measure of central tendency-usually preferred for interval and ratio-level data |
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M = (Sigma)X \ N
X=actual data values
Sigma=Sum of numbers |
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Sum of deviations above the mean always exactly balances those below it
Does not ignore any information
The most stable index of central tendency
Many inferential statistics are based on the mean |
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Sensitive to outliers
Gives a distorted view of what is "typical" when data are skewed
Value of mean is often not an actual data value |
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| In normal distribution all three indexes (mean median and mode) |
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| A heterogeneous distribution |
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| A homogeneous distribution |
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| The difference between the highest and lowest value in the distribution |
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Easy to compute
Readily understood
Communicates information of interest to readers of a report |
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Depends on only two scores, does not take all info. into account
sensitive to outliers
tends to be unstable-fluctuates from sample to sample
Influenced by sample size |
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Based on quartiles
The range of scores within which the middle 50% of scores lie |
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| Point below which 75% score lies |
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Reduces influence of outliers and extreme scores in expressing variability
Uses more information that the range
Important in evaluating outliers
appropriate as index of variability with ordinal measures |
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Is not particularly easy to compute
Is not well understood
Does not take all values into account |
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| An index that conveys how much, on average, scores in a distribution vary |
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Standard Deviation (SD)
(Like IQ's) |
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| The SD indicates the average amount |
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| of deviation scores from the mean |
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| Standard deviation has to be |
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Takes all data into account in describing variability
Is more stable as a measure of variability than the range or IQR
Lends itself to computation of other measures often used in inferential statistics
Is helpful in interpreting individual scores when data are distributed approx. normally |
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Can be influenced by extreme scores
Not as "intuitive" or easy to interpret as the range |
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| An important variability concept in inferential statistics, but not used descriptively |
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| Page 13 chapt. 2 pg 13 look at chart |
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| There are also descriptive statistics to describe individual scores (ie their relative standing or position in a distribution: percentile ranks and standard score) |
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| A one-hundreth of a distribution |
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