Interpretation of SPSS Results

This article discusses the interpretation of SPSS results in research methods. It covers topics such as frequency tables, crosstabulation, chi-square results, and Phi/Cramer's test. The article provides a comprehensive understanding of these statistical methods and their application in data analysis.

Research Methods

Contents
Research Methods............................................................................................................................1
5. Interpretation of the SPSS results................................................................................................3
Frequency Table:....................................................................................................................3
Crosstabulation:......................................................................................................................3
Chi-square result:....................................................................................................................4
Phi/Cramer’s:..........................................................................................................................4
REFERENCES................................................................................................................................6

5. Interpretation of the SPSS results
Frequency Table:
A frequency table is a way to arrange relevant data in a concise manner by showing a
sequence of metrics in lowest to highest, along with respective frequencies, the amount of times
each performance appears in the corresponding sample group. A table for the distribution of
frequencies is a map summing up values including their size. It's a system for organizing
statistics if they have a sequence of names in a sample representing the probability of a given
outcome (Bell, Bryman and Harley, 2018). There are two columns of a standard normal
distribution. From the Spss output data related to the Noisy neighbour and loud parties it has
been determined that valid option are very common, Fairly common, Not very common, Not at
all common and missing value is don’t know anything. The frequency column shows the total
actual count for each option such as very common are 148, Fairly common are 255, Not very
common are 1105 and Not at all common are 1635. On the other side the realistic count of don’t
know are 3 from the total number of 3146 observation. In the percent section these frequencies
are translated to percentages (such as 4.7 for very common, 8.1 for fairly common, 35.1 for Not
very common and 52.9 for Not at all common). The Attributes are category midpoints alternative
can be added to other numeric data coded in a really form that their importance depends on a
range's half way point. Similarly 0.1 for missing value in case for don’t’ know anything.
Remember that the column with True Percent displays the same values. In case if there are
missing data than these would be dissimilar; i.e. this column modifies the percentages predicated
on missing values.
Crosstabulation:
Crosstabulation is a fundamental method for analysing the relationships between two
categorical data. For instance, if analyser can create a two-dimensional crosstabulation that use
the age range as a row variable as well as Gender as just a section variable which displays the
amount of people in each age group. This is among the important scientific methods and a
cornerstone of market analysis. Cross-tabulation research, also recognized as contingency data
analysed, is most frequently used to analyse descriptive statistics (nominal number of
measurement techniques). The Chi-square statistics are the key statistics used for checking the
cross-tabulation table's statistically significant results (Brannen, 2017). From the table result

related to the noisy neighbour and loud parties are is discussed as follows such as in case of very
common the count for young adult is 38 and the % within age group is 6.6% (38/148), for adult
the count is 90 which is 5.5% of total count of very common and 2.2% for elderly. In Fairly
common case the age count for young adult is 67 which is 11.6% of (67/255), for adult the count
is 134 which is (134/255) is 8.2% and 54 for elderly is (5.9% which is 54/255). Similarly in case
of not very common the count for young adult is 235 which is 40.7% (235/1102), for adult
608 of 1102 which is (37.1%) and 259 for elderly is 28.3% (259/1102). In case of not at all
common 238 for young adult which is 41.2% (238/ 1629), for adult 808 is 49.3% (808/1629) and
583 for elderly which is (583/1629 is 63.6%).
Chi-square result:
The Chi-Square Test of Independence decides when numerical things are linked (i.e., if the
factors are independent or associated) it is a non-parametric test. The independence test in Chi-
Square could only evaluate categorical data. This cannot allow distinctions between categorical
variables, as well as between categorical and continuous. In comparison, the Chi-Square
Independence analysis is a tests only relationship among categorical variables and cannot make
any causal inferences (Hennink, Hutter and Bailey, 2020). There have been two primary ways
they might originally set up results. The data format will evaluate how the Chi-Square Test of
autonomy should be run. The data should at least contain two sets of data (represented in
columns) which will be included in the research. From the table it has been determined that
person chi square value is 91.182 and the difference value is 6 which states that 0 cells (0.0%)
have expected count less than 5. The minimum expected count is 27.30. The Likelihood ratio for
the collected data is 93.437 and the df value is 6. Similarly in case of Linear-by-Linear
Association is 78.762 and difference is for the 1 which is representing the total case of valid
cases is 3134. A case shows the subjects and every subject occurs in the database frequently.
That is, every column shows an interpretation of a specific subject. The dataset includes at least
two normative dependent variables (string or numeric). The categorical variables used
throughout the test should be in two categories more than.
Phi/Cramer’s:
Phi is a test of interaction dependent on Chi-square. The coefficient of chi-square relay on
the degree of connection and sampling technique. By multiplying chi-square by n, the sample
size, and taking the square root, Phi reduces sample size (LoBiondo-Wood and Haber, 2017). Phi

is one test of symmetry. It doesn't make any difference what attribute is independent (column).
Phi has a propensity to undervalue inherently unstable relations. Cramer's V is perhaps the most
common of nominal interaction chi-square measurements as it provides strong standardization
from 0 to 1 irrespective of table size, while row marginal seats equal columns marginal. The
symmetric measure show nominal by nominal for Phi value is 0.171 and the approximate 0.000
and in the context of Cramer’s V value is 0.121 and approximate significance is 0.000 for the
total number of cases. Cramér's V is often called phi (coefficient) 5 by Cramér. This is an
expansion of the above phi coefficient for columns greater than 2 by 2 and therefore its \ (\phi c\)
notation. It was proposed that "V" was changed because old machines were unable to display the
letter. This figure indicate sometime now connection among music preferences and study course:
study standard deviations are dissimilar for groups of music preferences.

REFERENCES
Books and Journals
Bell, E., Bryman, A. and Harley, B., 2018. Business research methods. Oxford university press.
Brannen, J. ed., 2017. Mixing methods: Qualitative and quantitative research. Routledge.
Hennink, M., Hutter, I. and Bailey, A., 2020. Qualitative research methods. SAGE Publications
Limited.
LoBiondo-Wood, G. and Haber, J., 2017. Nursing research-E-book: methods and critical
appraisal for evidence-based practice. Elsevier Health Sciences.