NURS 231 Lecture Notes - Lecture 10: Nominal Level, Descriptive Statistics, Moodle
231 last class. Quantitative analysis
last quiz opens tomorrow at noon and closes Friday at noon. And it closes after 15
minutes when you open it.
final:
7 multiple choice
4 short answer questions about a research study
The emphasis will be after the midterm but there are questions from before as well.
There is an exam review posted on Moodle
Purposes of statistical analysis in quantitative research
• To describe the data
• To test hypotheses
• To provide evidence regarding measurement properties
Descriptive statistical analysis
• Uses to organize and describe the characteristics of the data
• Ex. average, mean, median, mode
• Parameters: descriptors for a population
• Statistics: descriptive index for sample
• Inferring something to a larger populations based on sample data
• P values are used
Measurement
• Assignment of numbers to variables or events
• Purpose of assigning numbers is to differentiate amoung people who posses
varying degree of the critical attribute
• Each variable in a study is given a number
• Nominal level- data that is classifies into categories and cannot be arranged
in any particular order
• Ordinal level- data arranged in some order, but the differences between
data values cannot be determined or are meaningless. Whenever they have a
scale
• Interval level and ratio data are the same - similar to ordinal, with the
additional property that meaningful amounts of differences between data
values. Interval has no absolute zero and ratio has a set zero. The numbers
actually mean something in this
• Examples: color of a person hair is nominal
The age in years of the youngest member of each household – interval
The postcode of household- ordinal
Academic performance measured by number of marks – interval
Academic performance measured by pass or fail- nominal
The number on the back of a football player- nominal
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Descriptive statistics- they take data so they can get meaning out of them.
Central tendency-
• compute the mean (but the mean is very sensitive to the extreme scores)
• Compute a weighted mean- instead of each data point contributing equally to
the final mean, some data points contribute more weight than others.
• Computing median- the midpoint in a set of scores. List all the values in
order then find the middle. If there are two central numbers you add both
numbers and divide by two. Median is used for when there are outliers
• Computing the mode- the most frequent number. Mode = most. Mode is use
to capture the frequency.
Understanding variability- the degree oto which scores in a distribution are
spread ou of dispersed.
• Homogeneity- little variable
• Heterogeneity- great variability
• Computing range – take highest number and subtract the lowest score
• Computing the standard deviation – most frequently used measure of
variability
It represents the average amount of variability in a set of score. It’s the
average distance from the mean. The larger the SD the larger the average
distance each
the sum of (x-mean)^2/ (numbers in study-1) and all of that is squared
find more resources at oneclass.com
find more resources at oneclass.com
Document Summary
Quantitative analysis last quiz opens tomorrow at noon and closes friday at noon. And it closes after 15 minutes when you open it. final: 4 short answer questions about a research study. The emphasis will be after the midterm but there are questions from before as well. There is an exam review posted on moodle. Purposes of statistical analysis in quantitative research: to describe the data, to test hypotheses, to provide evidence regarding measurement properties. Descriptive statistical analysis: uses to organize and describe the characteristics of the data, ex. average, mean, median, mode, parameters: descriptors for a population, statistics: descriptive index for sample, p values are used. Inferring something to a larger populations based on sample data. Interval level and ratio data are the same - similar to ordinal, with the additional property that meaningful amounts of differences between data values. Interval has no absolute zero and ratio has a set zero.
