Quantitative Methods Assignment

7SSMM002 QUANTITATIVE METHODS ASSIGNMENT 1
RESEARCH METHODS IN HRM AND ORGANISATIONAL ANALYSIS7SSMM002
QUANTITATIVE METHODS ASSIGNMENT– WORTH 50% OF YOUR TOTAL MARK FOR THIS MODULE
For this assignment, make reference to the statistical output with which you have been supplied.All
of the information that you will need to complete the sections that follow is contained within the
supplied statistical output.
NOTE WELL:
(A) DO NOT EXCEED THE WORD LIMIT FOR EACH SECTION – YOU WILL BE MARKED DOWN
FOR DOING SO. STATE YOUR WORD COUNT FOR EACH SECTION.
(B) DO NOT PRINT OUT THE STATISTICAL OUTPUT BECAUSE THERE’S TOO MUCH OF IT
(C) YOU CAN INCLUDE REFERENCES. IN-TEXT REFERENCES ADD TO YOUR WORD COUNT.
THE REFERENCE LIST WILL NOT ADD TO YOUR WORD COUNT.
Nominal-Level Data
Before we conduct analyses, it is important to check our data for artifacts relevant to the statistical
tests that we will perform(Field, 2018). This can be achieved in various ways, but one approach is to
peruse frequency tables. Frequency tables are particularly relevant tonominal-level data. We can
also use frequency tables to check interval-level data, but, in this case, we will use different
approaches for our interval or scaled variables, described later.
1. Name each nominal variable in this study and briefly comment on the frequency tables
associated with each (50 words).
The nominal variables are :
Gender- from table it is found that male were 451 and female were 829
Ethnicity- the most employees were Australian that is 441, 398 were New Zealand and 138 was
European
Occupation – the table shows that 709 were full time basis and 344 were part time.
State word count:
Interval-Level Data: Internal Consistency
The scaled variables (i.e., variables that, in practice, we treat as being at the interval level) in this
study relate to variables that are psychological in nature. Oftentimes, we want to get some idea
about evidence in favour of unidimensionality for each construct. With simple measures where

7SSMM002 QUANTITATIVE METHODS ASSIGNMENT 2
items are the only facet1, this is often achieved using exploratory or confirmatory factor analysis. But
in the present case, this work has already been completed for us in previous research. So, as is
routinely assumed in applied research, it will suffice to check the internal consistency for each
measure and to assume the factor structure will hold.
2. Name each interval-level variable in this study and state if the coefficients alpha are at an
acceptable level (50 words).
Here, variables are
Positive and negative affect terms, job involvement items and organizational commitment items
It is found that cronbach’s alpha value is .800 that means that it is at acceptable level that value is
significant.
Also, in job involvement items cornbach alpha value is .905 that means it is at acceptable level.
State word count:
Interval-Level Data: Data Distributions and Out-of-Range Scores
For scaled variables, we need to check about whether our data make sense as we did when we used
frequency tables for our nominal variables. For scaled variables, depending on what is under
scrutiny, it can sometimes be convenient to look at summary statistics for the aggregated scale (i.e.,
the average of the items that make up the scale, for example [item 1 + item 2 + item]/3) as well as
the range of the scale. This will help to ensure that we have no out-of-expected-range data, that our
standard deviations are not too high relative to our mean values, and so that our mean values are
not at some unexpected value. We can also check the N for each of our variables because this can
sometimes vary as some respondents might refuse to answer specific questions. If anything seems
to be amiss, then we can look back at our data set, perhaps at the item level, and find out more
about any problems that arise.
Another consideration is the distribution shape for each of our variables. In an ideal world,
frequency distributions relating to our aggregated scales would be perfectly bell-shaped to fulfil a
common assumption in many statistical tests that variables should be normally distributed. One way
to address this is by looking at histograms of our aggregated scales. We cannot expect perfection
with real world data, but we can at least hope for a distribution that is not cataclysmically different
from normal. Bear in mind that many or most statistical tests are robust against the assumption of
normality.Also some tests assume multivariate normality, for which specific tests have been
1 A facet, in this context, is a source of variability (e.g., items) that does not include the object of measurement
(i.e., the people being measured, see Brennan, 2001).

7SSMM002 QUANTITATIVE METHODS ASSIGNMENT 3
developed (e.g., Mardia’s coefficient). However, it is rare that applied data meet the assumption of
multivariate normality (Byrne, 2010).
3. Comment on the descriptive statistics and distribution shape of the Job Involvement
variableONLY (50 words).
By analyzing the graph it is stated that mean of job involvement is 2.54 and standard deviation
is .803. besides that, minimum value is 1 and maximum is 5. Thus, it means that most employees are
involved as fixed term in organization. The SD is .803 that is high so it indicate that values are
spread.
The distribution shape of job involvement is bell curved which means that it is unimodal
distribution.
State word count:
Multiple Regression
Often our choice of statistical technique is based on how our original variables were measured.
Multiple regression is routinely used when the variables under scrutiny are at the interval-
level(Spicer, 2005). Nominal variables can be included in multiple regression analyses, but these
need to be dummy-coded first. In the present case, all of the variables included in the multiple
regression output are at the interval-level.
4.Name the variables that are included as predictors in the multiple regression output. How are
these predictors arranged into blocks? Name the outcome variable in this analysis. Comment on
the effect size and significance of the models relevant to the multiple regression. Comment on the
beta coefficients associated with specific predictors and what they mean. Comment very briefly
on the residual plots associated with this analysis. (150 words)
In this the predictor variable is job involvement and organization commitment. They are arranged as
positive and negative affect, org commitment and job involvement.
The effect size in regression is standardized mean difference. And significance is constant.