Discussion board #1 post should be at least 350 words Part I: Considering the five quantitative approaches, pick two approaches, and explain them both. Illustrate your explanation with examples. Par

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Discussion board #1 post should be at least 350 words

Part I: Considering the five quantitative approaches,  pick two approaches, and explain them both. Illustrate your explanation with examples.

Part II: What type of research study are you considering for your dissertation? What population sample will you draw, and how will it be representative of the general population? What sampling procedures might you use? If you have not yet settled on a dissertation topic, what implications are there for using a specific sampling method?

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Part III: In your own words, describe the primary purpose of inferential statistics, and explain what you believe are the advantages and limitations of inferential statistics. Use examples throughout your discussion. Part I: Introduce yourself to your classmates with your name, location, current employment, and future goals.



Respond to post: responses should be at least 200 words (NO)

Comparative

The comparative approach is a research method that involves analyzing and comparing several groups while considering both dependent and independent variables. This approach allows researchers to identify differences and similarities between the groups being studied and to determine any potential causal relationships between the variables under investigation (Gliner et al., 2016). In the comparative approach, there is no random participation assignment to groups, and no independent variable is active.

An example of the comparative approach is seen by Herron and his colleagues in 2021, when they completed a comparative study of violence in home care and long-term care settings in two Canadian provinces (Herron et al., 2021). They chose this methodology to understand the causes of violent situations in specific contexts of care.

Associational

The associational approach is a statistical method that examines the relationship between two or more continuous variables associated with the same group of participants. In this approach, one variable is treated as an attribute, and the other variables are analyzed to determine how they relate to the attribute (Gliner et al., 2016). This method establishes correlations between variables, providing valuable insight into the relationship between them (Gliner et al., 2016).

In 2021, Schina and colleagues conducted an associational research study on pre-service teachers’ acceptance and self-efficacy toward Educational Robotics during university courses. They also sought to examine their perceptions of the course using quantitative research methods (Schina et al., 2021). The study’s findings revealed the participants positively evaluated the course and provided suggestions for improving it (Schina et al., 2021).



Part II

I am considering embarking on a research study that delves into the intricate relationship between possessing a higher education degree in business and/or project management, project management certifications, and the success of large-scale information technology (IT) projects. The study aims to provide a comprehensive and detailed insight into how these qualifications impact the outcome of substantial IT projects. It will focus on the United States as the research setting. By exploring the interplay between academic credentials, industry certifications, and project outcomes, this research seeks to uncover valuable knowledge that can inform best practices and decision-making in the field of IT project management.

Population Sample

For my research, I will select a representative sample of individuals in the United States currently employed in a full-time information technology (IT) project management role and are 18 years of age or older. This sample will encompass individuals of all genders, ethnicities, educational backgrounds, certification statuses, and years of experience working in IT project management. By including a diverse range of individuals, I hope to gain a comprehensive understanding of the current state of IT project management in the United States. In addition, as I embark on my research, I am eager to delve deeper into the intricate relationships between various factors, such as education and certification, years of experience, and salary. My ultimate goal is to gain comprehensive insights and a more nuanced understanding of these relationships.

Inclusion Criteria:

Over 18 years of age

Employed as full-time IT Project Manager

Any gender, ethnicity, educational background

No certification requirement

At least 2 years of project management experience

No education requirement

Must be employed in the private sector

Exclusion Criteria:

Employed in the Public Sector (any level of government, as they have different levels of project management certification requirements)

IT Project implementation costs <$ 1 million

Sampling Procedures

To ensure that I gather the most accurate and reliable data, I plan to utilize the services of an online tool such as Survey Monkey or Qualtrics to create a comprehensive survey. The survey will be distributed through Cloud Research, which will help to ensure that the data collected is of the highest quality.

To ensure my results are statistically significant and representative of the population, I will need to determine the appropriate sample size. To achieve this, I will calculate the Z-scores, which will determine my findings’ margin of error and confidence level (Gliner et al., 2016).

I will be conducting a statistical analysis using an ordinal logistic regression test. This test is commonly used when the dependent variable has three or more ordered categories, and the independent variables can be either continuous or categorical (Gliner et al, 2016). By using this test, I will be able to determine the relationship between the dependent variable and multiple independent variables and the probability of the dependent variable belonging to a specific category.

Part III

Inferential statistics

Inferential statistics allow researchers to study a smaller group of participants and make inferences about the larger population (Wienclaw, 2021). By analyzing a sample group, researchers can determine patterns and relationships that can be applied to the larger population to generalize or draw conclusions (Gliner et al., 2016). This enables researchers to better understand the population’s characteristics and behaviors and make informed decisions based on their findings (Wienclaw, 2021). To ensure that the results obtained from sample methods are unbiased and accurate, it is crucial to employ statistical techniques such as hypothesis testing, confidence intervals, and regression analysis. These methods provide a means to test the validity of data and determine the likelihood that the results obtained are representative of the population being studied (Gliner et al., 2016).To verify that the sample methods used are unbiased and impartial, it is crucial to employ statistical tools and techniques such as hypothesis testing, confidence intervals, and/or regression analysis (Gliner et al., 2016). By utilizing these techniques, researchers can ensure that personal biases or outside factors do not influence their findings and that the conclusions drawn are based solely on the data obtained from the samples.

Inferential statistics present multiple benefits over other statistical methods. By analyzing a representative sample of the population, they enable predictions to be made about the entire population while saving time and resources. Sampling a smaller portion of the population allows for a more efficient and cost-effective analysis while still providing reliable insights into future values (Weinclaw, 2021). For example, suppose you must estimate the average salary of female IT project managers in Maryland. In that case, collecting a sample is much more efficient than studying the entire population.

Inferential statistics are an essential tool in various applications. However, they have limitations that should be considered. One of the significant drawbacks is sampling errors, which may happen if the sample size is inaccurate or not representative of the intended population (Gliner et al., 2016). These errors can lead to incorrect conclusions and affect the accuracy of the results. Therefore, when conducting inferential statistics, it is crucial to ensure that the sample size is appropriate and adequately represents the population of interest. For example, if you want to estimate the desire for a school redistricting and only survey your close friends and neighbors, your sample may be limited and reflect bias. In addition, predictions based on non-representative samples may be inaccurate. Finally, hypothesis errors, such as Type 1 and Type 2, can also occur when using inferential statistics.

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