LB management replies

Please reply to both parts with 150 words a piece.

Part 1

Research
questions provide a map to assist the researcher in the navigation
of thoughts, literature-based foundation, design strategy, and
interpretive lens through which to draw conclusions (Williams,
2007). When taking a quantitative approach, the goal is to conduct
an empirical investigation into reality using numerical measurements
and statistical analyses to emphasize objectivity (Leedy &
Ormrod, 2016; Williams, 2007). As illustrated in Figure 1 below
(also attached as a separate image to this discussion post),
quantitative research questions can be categorized as either
descriptive, comparative, or relationship-based (Onwuegbuzie &
Leech, 2006). The distinction among these categories hinges on the
underlying motivation and whether the objective is to measure a
descriptive response, detect a comparative difference, or uncover a
trend in relationship (Onwuegbuzie & Leech, 2006). The type of
research question also drives the choice of analysis method, as some
techniques are more appropriate for a certain setting (Onwuegbuzie
& Leech, 2006; Williams, 2007). An ordinary least squares (OLS)
regression analysis, for example, is best suited to find trends
among continuous or dichotomous predictors of a continuous response
(Onwuegbuzie & Leech, 2006).

Figure 1
.
Categories of Quantitative Research Questions. (Onwuegbuzie &
Leech, 2006).

For
my own research that is focused on the statistical analysis of
adaptive clinical trials, software efficiency is one aspect on which
I intend to concentrate. A research question tailored to this goal
could be: Is there a difference in efficiency among statistical
software packages? To translate this into a simplified and testable
analysis, I could create a dataset that contains the run-times
(measured in milliseconds) that it takes for each considered package
(SAS and R) to process the same program. Various types of adaptive
programs would be run, and the associated number of probability
dimensions of each program would also be recorded. My null
hypothesis would be that there is no difference in run-time between
the two packages. My alternative hypothesis is that programs with
higher dimensions will take a longer time to run in SAS than in R.
This is a directional, or one-tailed, alternative hypothesis because
it is specifying that not only is there an expected difference, but
that the difference is intended to go in a specific direction
(Pallmann et al., 2018). Tested with an analysis of variance model,
the dependent variable would be run-time, and the independent
variables would be statistical package, program dimension, and the
interaction between package and dimension.

Part 2

Quantitative
research questions and hypotheses go together for researchers using
quantitative research methods. Quantitative research questions focus
a research study on the relationships between independent and
dependent variables being studied by the researcher (Creswell &
Creswell, 2018). There are three categories of quantitative
research: (1) comparing groups on an independent variable to see the
impact on a dependent variable, (2) relating or correlating one or
more independent variables to one or more dependent variables, and
(3) describing responses to the independent, mediating or dependent
variables (Creswell & Creswell, 2018).

Research
Question: How does using emerging technologies for cross-project
knowledge transfer enable project managers to assimilate
cross-project knowledge and use that knowledge for problem solving
on their project?

The
purpose of my proposed quantitative study is to analyze how using
new and emerging technologies for project knowledge transfer enables
project managers to assimilate knowledge and use the knowledge to
solve problems on their projects. The research study looks at
technology factors enabling the transfer and assimilation of project
knowledge across projects in the same organization. The two types of
cross-project knowledge transfer methods to be studied are
technology-formal and technology-informal (Landaeta, 2003; 2008).
The study participants should be current, practicing project
managers across all disciplines.

Null
Research Hypothesis: There is no relationship between the use of
emerging technologies for project knowledge transfer and the project
manager’s ability to assimilate the cross-project knowledge
and use the knowledge to solve problems.

Quantitative
hypotheses predict the outcomes of the relationships between the
variables being studied (Creswell & Creswell, 2018). The study
hypothesis as constructed is nondirectional and does not make a
directional prediction about the study’s outcome (Creswell
& Creswell, 2018). The variables in my research study are
emerging technologies for cross-project knowledge transfer
(independent variable) cross-project knowledge assimilation by the
project manager (dependent or independent variable depending on
whether or not the study manipulates two independent variables
versus one relative to the outcome) and the project manager’s
use of assimilated knowledge for decision making and problem solving
(dependent variable). Identifying dependent and independent
variables does not guarantee your research data will support a cause
and effect relationship (Leedy and Ormrod, 2016).