Categories of Quantitative Research

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).

Sample Solution

ACED ESSAYS