Lecture 1

METHODS OF DATA ANALYSIS
Lecture-1
Instructor: KONPAL DARAKSHAN

METHOD OF DATA ANALYSIS
 Data
 Data Analysis
 Methods of Data Analysis
 Packages

DATA
 What is data?—(Collection of Facts and Figure)
 Types of data
 Measurement of Scale
 Collection of data

COLLECTION OF DATA
Primary Data Secondary data
PRESENTATION OF DATA
textual, tabular, and graphical forms

DATA ANALYSIS
 The purpose
To answer the research questions and to help
determine the trends and relationships among the
variables.
o Types of Data Analysis
Descriptive Analysis
Inferential Analysis

DESCRIPTIVE STATISTICS
 Procedures that allow researchers to describe and
summarize data you definitely know (describes the
sample).
 Measures of Central Tendency
 Measures of Dispersion
 Bivariate Descriptive Statistics

INFERENTIAL ANALYSIS
 Numerical values that enable the researcher to
draw conclusion about a population based on the
characteristics of a population sample.
 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
 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

 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 statistic 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.

 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

LEVEL OF SIGNIFICANCE
 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
 Parametric and Nonparametric Statistics are used
to determine significance.
 Parametric Statistics assume underlying statistical
distributions in the data. Therefore, several
conditions of validity must be met so that the result
of a parametric test is reliable..
 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.
 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..
 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
 Simple and multiple regression.

PACKAGES
 SPSS-(Statistical Package for the Social Sciences)
 E-views
 Minitab
 R-language
 SAS-(Statistical Analysis System)
 STATA
 Python

SPSS
 SPSS means “Statistical Package for the Social
Sciences” and was first launched in 1968. Since
SPSS was acquired by IBM in 2009, it's officially
known as IBM SPSS Statistics but most users still
just refer to it as “SPSS”.

SPSS - QUICK OVERVIEW MAIN FEATURES
 SPSS is software for editing and analyzing all
sorts of data. These data may come from basically
any source: scientific research, a customer
database, Google Analytics or even the server log
files of a website. SPSS can open all file formats
that are commonly used for structured data such as
 spreadsheets from MS Excel;
 plain text files (.txt or .csv);
 Stata and SAS.

SPSS DATA VIEW
 After opening data, SPSS displays them in a
spreadsheet-like fashion as shown below.
 This sheet is called data view- always displays our
data values. For instance, our first record seems to
contain a male respondent from 1979 and so on

SPSS VARIABLE VIEW
 An SPSS data file always has a second sheet
called variable view. It shows the metadata
associated with the data. Metadata is information
about the meaning of variables and data values.
This is generally known as the “codebook” but in
SPSS it's called the dictionary.

DATA ANALYSIS
 Right, so SPSS can open all sorts of data and
display them -and their metadata- in two sheets in
its Data Editor window. So how to analyze your
data in SPSS? Well, one option is using SPSS’
elaborate menu options.
For instance, if our data contain a variable holding
respondents’ incomes over 2010, we can compute
the average income by navigating to Descriptive
Statistics as shown below

DATA ANALYSIS
Doing so opens a dialog box in which we select one or many
variables and one or several statistics we'd like to inspect

SPSS OUTPUT WINDOW
 After clicking Ok, a new window opens up: SPSS’
output viewer window. It holds a nice table with all
statistics on all variables we chose. The screenshot
below shows what it looks like.

SPSS REPORTING
 SPSS Output items, typically tables and charts, are
easily copy-pasted into other programs. For
instance, many SPSS users use a word processor
such as MS Word, Open Office or Google Docs for
reporting.

SPSS SYNTAX EDITOR WINDOW
 The output table we showed was created by running
Descriptive Statistics from SPSS’ menu. Now, SPSS
has a second option for running this (or any other)
command: we can open a third window, known as the
syntax editor window. Here we can type and run
SPSS code known as SPSS syntax. For instance,
running descriptive income_2010. has the exact same
result as running this command from SPSS’ menu like
we did earlier.

SPSS - OVERVIEW MAIN FEATURES
 Now that we have a basic idea of how SPSS works, let's take
a look at what it can do. Following a typical project workflow,
SPSS is great for
 Opening data files, either in SPSS’ own file format or many
others;
 editing data such as computing sums and means over
columns or rows of data. SPSS has outstanding options for
more complex operations as well.
 creating tables and charts containing frequency counts or
summary statistics over (groups of) cases and variables.
 running inferential statistics such as ANOVA, regression and
factor analysis.
 saving data and output in a wide variety of file formats.

TABLES AND CHARTS
 All basic tables and charts can be created easily
and fast in SPSS. Typical examples are
demonstrated under Data Analysis.

 SPSS contains all basic statistical tests and
multivariate analyses such as
 t-tests;
 chi-square tests;
 ANOVA;
 correlations and other association measures;
 regression;
 nonparametric tests;
 factor analysis;
 cluster analysis

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