Transcript importance of STATISTICS - Akal College Of Nursing

IMPORTANCE OF STATISTICS
MR.CHITHRAVEL.V
ASST.PROFESSOR
ACN
Importance of Statistics in Nursing
Research
Researchers link the statistical analyses they
choose with the research question, design,
and level of data collected.
Allows us to critically analyze the results.
Provide organization and meaning to data.
Where Do You Find Them?
Methods section will contain the planned
statistical analysis.
Results section will provide the data
generated from testing the hypothesis or
research questions.
Data is the analysis using descriptive and
inferential statistics.
Levels of Measurement
 Measurement is the process of assigning
numbers to variables.
 For example: Males and females in a study.
Males would be assigned as 1 and females assigned
as 2.
 Every variable in research study that is assigned a
specific number must be similar to every other
variable assigned that number.
Levels of Measurement
 Nominal- aka categorical, naming or classifying.
Either does or does not have the characteristic.
 Lowest level of measurement and allows for the
least amount of statistical information.
 Examples- gender, marital status, religious
affiliation.
 Can you think of one?
Ordinal
 Used to show relative rankings of variables or events.
 Ranks in order from high to low, but does not
indicate how much higher or how much lower.
 Intervals are not necessarily equal and there is no
absolute zero.
 Limited in the amount of mathematical manipulation
possible.
 Examples- class rank, levels of wellness, levels of
height.
Interval
Shows rankings of events or variables on a
scale with equal intervals between.
Zero point remains arbitrary and not absolute.
Allows for more mathematical manipulation of
data.
Examples- test scores and temperature on a
Fahrenheit scale.
Ratio
Shows rankings of events or variables on
scales with equal interval and absolute zero.
Most often used in physical sciences.
Highest level of measurement, allows for most
manipulation of data.
Number represents the actual amount of the
property the object possesses.
Example- height, weight, pulse and BP.
Descriptive Statistics
Procedures that allow researchers to describe
and summarize data you definitely know
(describes the sample).
Examples: Demographics, clinical data.
Frequency distribution is one way to display
data.
Descriptive Statistics
Measures of central tendency are used to describe
the pattern of responses among a sample.
 Mean- most frequently used average, add up
numbers (sum) and then divide by the #. Defined
as a balance point in a distribution of scores.
 Median-50% are above and 50% are below the
score. Defined as the middle point in a
distribution. Insensitive to extreme scores.
 Mode-Most frequently occurring score. May have
more than one mode.
Normal Distribution
 Most important curve (Bell-shaped).
 Most often found in nature and used as the basis
for a number of inferential statistics.
 Mean, median and mode are equal.
Measure of Variability
 Concerned with the spread of data.
 Range- the difference between the highest and lowest
score.
 Semiinterquartile range- indicates the range of the
middle 50% of the scores.
 Standard Deviation-most stable and most useful,
provides an overall measurement of how much
participants scores differ from the mean of the group.
 Z score-used to compare different measurements,
scores are converted to Z scores and them compared.
Inferential Statistics
Data collection procedures that allow
researchers to estimate how reliably they can
make predictions and generalize findings.
Allows us to compare groups and test
hypothesis.
Answer research question in a study.
Inferential Statistics
Parameter- a characteristic of a population.
Statistic- characteristic of a sample.
Not possible to study the whole population so
we study a sample and make predictions or
statements related to our findings.
Inferential Statistics
2 important qualifications must be conducted
to use inferential statistics.
Sample must be representative (drawn with
probability, some form of random selection).
Scale used must be either interval or ratio
level of measurement.
If nonprobability sampling occurs techniques
such as power analysis are used to
compensate for this.
Inferential Statistics
Researchers are able to make objective
decisions about the outcome of their study by
using statistical hypothesis testing.
Scientific hypothesis is what the researcher
believes will be the outcome of the study.
Null hypothesis is what can actually be tested
by the statistical methods.
Inferential stats use the null hypothesis to test
the validity of a scientific hypothesis.
Inferential Statistics
Probability- the notion that in a repeated
trial/study under the same conditions we
would get the same results.
Statistical probability is based on sampling
error. The tendency for stastics to fluctuate
from one sample to another is known as
sampling error.
Type I and Type II Errors
 2 types of errors in statistical inference.
 Type I- researcher rejects a null hypothesis when it is actually
true.
 Type II- researcher accepts a null hypothesis that is actually
false.
 Type I errors are considered more serious because if a
researcher declares that differences exist when none are
present the potential exists for patient care to be adversely
affected.
 Type II errors occur when sample is too small.
Level of Significance
The probability of making a type I error.
Minimum accepted level for nursing research
is 0.05.
“ If I conduct this study 100 times, the
decision to reject the null hypothesis would be
wrong 5 times out of 100”
LOS
If wanting to assume smaller risk level will be
set at 0.01.
Meaning researcher is willing to be wrong only
once in 100 trials.
Decision to use alpha level 0.05 or 0.01
depends of the study significance.
Decreasing the risk of making a type I error
increases the risk of making a type II error.
Parametric and Nonparametric Statistics are
used to determine significance.
 Parametric have 3 attributes:
1. Estimation of at least one population parameter.
2. Require measurement on at least an interval scale.
3. Involve certain assumptions about the variables being
studied.
 Variable is normally distributed in the overall
population.
 Most researchers prefer parametric statistic when
possible because they are more powerful and more
flexible.
Nonparametric
Not based on the estimation of population
parameters; usually applied when variable
measured on a nominal or ordinal scale , or
distribution of scores is severely skewed.
Most Commonly Used Inferential Statistics
 Parametric
 t statistic-commonly
used in nursing
research, tests whether
2 group means are
different.
 ANOVA
 ANCOVA
 Nonparametric
 Chi-square- used when
data is at the nominal
level, determine
difference between
groups. Robust and
used with small
samples.
 Fisher’s exact
probability.
Tests of Relationships
Interested in exploring the relationship
between 2 or more variables.
Studies would use statistics to determine the
correlation or degree of association between 2
or more variables.
Pearson r, the sign test, the Wilcoxon matched
pairs, signed rank test and multiple regression.