Today in the first class
of SPSS i.e. Statistical Package for the Social Sciences I came to know about the actual use of the SPSS and
realized that it far more powerful tool than Excel. Although as the name
suggest that it is meant for the social science but still it is very much
popular among market researchers, health researchers, survey companies,
government, education researchers, marketing organizations and others, no
wonder as it help them for decision-making through data analysis, its ability
to access, manage and analyze enormous amounts of data by data transformation,
or statistical formula
Something
which I really appreciated about the SPSS is that it is menu-driven and hence
it helps to execute statistical analyses, simple or complex, by clicking a
series of pull-down menus and selecting the desired pre-programmed commands and
no need to remembering all those formulas, commands and all.
The graphical
user interface has two views which can be toggled by clicking on one of
the two tabs in the bottom left of the SPSS window.
The
'Data View' which somewhat looks like excel sheet with cases (rows) and
variables (columns) but unlike excel sheet, the data cells can only contain
numbers or text and formulas cannot be stored in these cells.
The
'Variable View' displays the metadata dictionary where each row represents a
variable and shows the following items
· Variable Name
· Variable Type – It defines define the type of data contained in the
column (e.g. characters, strings, numbers etc.)
· Variable Width – To alter the number of digits displayed in the
column.
· Variable Decimal – To change the number of digits after the decimal.
· Variable Label – To give a detail description of the variable
name.
· Variable Value – A text label for category codes by clicking on the
appropriate cell in the column values.
· Variable Missing – To define different type of missing value code.
· Variable Columns– To change the width of column in the data view sheet.
· Variable Align– To align the text or numbers.
Variable
can be of two type
· Category – Have some value
· Continuous –Doesn’t have any value
Measurement
level can be broadly categorized in 3 parts
· Nominal – When the value of a variable represent categories with
no intrinsic ranking.
· Ordinal – When its values represent categories with some
intrinsic ranking.
· Scale – When the data values indicate both the order of
values and the distance between values.
Techniques
can be classified in following categories:
1.a. 1st level analysis — Ex frequencies, cross-tabs, OLAP cubes
1.b. 2nd level
analysis —Uses multi-variate techniques
2.a. Univariate – Use/ show only one variable at a time Ex Scatter
diagram.
2.b. Bivariate –
Ex Correlation, t-test
2.c. Multivariate – Ex Bubble diagram
Group
data into ranges –
At times we want “bin” data so that we can make easy comparison by looking at
its ranges. For example, we might want to group ages by ranges (less than 20,
20-29, 30-39, 40-49, and so forth) to examine the buying habits of different
age groups. And SPSS make it very simple.
Today
we have explored the Descriptive statistics in which we had hands on experience
on
· Frequency – It depicts the number of time a variable
has occurred.
· CrossTabs – Crosstab is useful to show the
relationship between two or more categorical variables.
In CrossTabs , we assume an Alternative Hypothesis
that there is some relation between the two variables.
· Chi-Square – SPSS can compute the expected value for
each cell, based on the assumption that the two variables are independent of
each other. The chi-square test essentially tells whether the results of a
crosstab are statistically significant or not. A chi-square will be
significant if the residuals for one level of a variable differ as a function
of another variable
In Chi square we test the null hypothesis which means
that there is no relation between the two variables.
Null hypothesis is normally being rejected if there is a .05 probability that our findings are due to chance
Null hypothesis is normally being rejected if there is a .05 probability that our findings are due to chance
By
Rohit Jayant
14160
Team D
Operation
Ref :-
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