Introduction to Quantitative Reasoning
When you hear the term statistics, what do you think about? Does this term bring good experiences to mind or does this term conjure up anxiety and apprehension? Chances are, you might be one of many students who fears statistics. Think back to when you learned to ride a bike. You were excited to remove the training wheels but were apprehensive about the balance challenge. Similar to the fear of falling from riding your bike, the fear of failing statistics is quite terrifying. But once you practice and develop your new learned skills, WOW! The thought of being taken to new heights when you reach proficiency is a great feeling.
Mastering statistics does not have to be an anxiety-driven process. This course allows you to use real world data not only to apply basic understanding of statistical methods used in quantitative research but also to test, analyze, and draw conclusions to develop your own knowledge and skill set. Your new knowledge and skills will allow you to take real world data to answer questions you always wanted to know more about but did not know the right way of getting there.
This first week of the course introduces you to quantitative reasoning and sets the stage for the rest of the course. You might have vast experience and knowledge in statistics or you might not have experienced a formal statistics course in quite some time; nonetheless, you begin this week with developing your basic understanding of statistics and quantitative reasoning in small increments.
For this week, you will examine research as it relates inference and a population of study. Also, you will examine a dataset to determine the independent and dependent variables, how they are measured, and whether a social change question can be answered.
Analyze logic of inference related to population and sample
Evaluate quantitative research related to error
Evaluate quantitative research
Analyze independent and dependent variables
Analyze levels of measurement
Analyze topics with social change implications
Describing and explaining social phenomena is a complex task. Box’s quote speaks to the point that it is a near impossible undertaking to fully explain such systems—physical or social—using a set of models. Yet even though these models contain some error, the models nevertheless assist with illuminating how the world works and advancing social change.
The competent quantitative researcher understands the balance between making statements related to theoretical understanding of relationships and recognizing that our social systems are of such complexity that we will always have some error. The key, for the rigorous researcher, is recognizing and mitigating the error as much as possible.
As a graduate student and consumer of research, you must recognize the error that might be present within your research and the research of others.
To prepare for this Discussion:
Use the Walden Library Course Guide and Assignment Help found in this week’s Learning Resources to search for and select a quantitative article that interests you and that has social change implications.
As you read the article, reflect on George Box’s quote in the introduction for this Discussion.
For additional support, review the Skill Builder: Independent and Dependent Variables, which you can find by navigating back to your Blackboard Course Home Page. From there, locate the Skill Builder link in the left navigation pane.
By Day 3
Post a very brief description (1–3 sentences) of the article you found and address the following:
Describe how you think the research in the article is useful (e.g., what population is it helping? What problem is it solving?).
Using Y=f(X) +E notation, identify the independent and dependent variables.
How might the research models presented be wrong? What types of error might be present in the reported research?
Be sure to support your Main Post and Response Post with reference to the week’s Learning Resources and other scholarly evidence in APA Style.
By Day 5
Remembering that all research has some error, respond to at least one colleague’s post and comment on how we as social change agents and critical consumers of research can balance the usefulness with the error in the research. Do we throw the research out because of too much error, or is there something useful that it can tell us?