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William Franco
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Quantitative And Statistical Research Methods :...

This user-friendly textbook teaches students to understand and apply procedural steps in completing quantitative studies. It explains statistics while progressing through the steps of the hypothesis-testing process from hypothesis to results. The research problems used in the book reflect statistical applications related to interesting and important topics. In addition, the book provides a Research Analysis and Interpretation Guide to help students analyze research articles.

Quantitative and statistical research methods :...


William E. Martin, EdD, is a professor and past chair of educational psychology in the College of Education, Northern Arizona University. He also served as senior scholar in the university's Faculty Research Center. His research areas are psychometric assessment, counselor development, psychosocial adjustment, and person-environment-based prevention and intervention methods.

Simply put, descriptive statistics are really important, even though the statistical techniques used are fairly basic. All too often at Grad Coach, we see students skimming over the descriptives in their eagerness to get to the more exciting inferential methods, and then landing up with some very flawed results.

The next thing you need to consider is your specific research questions, as well as your hypotheses (if you have some). The nature of your research questions and research hypotheses will heavily influence which statistical methods and techniques you should use.

  • Table of contentsQuantitative research methods

  • Quantitative data analysis

  • Advantages of quantitative research

  • Disadvantages of quantitative research

  • Frequently asked questions about quantitative research

Once data is collected, you may need to process it before it can be analyzed. For example, survey and test data may need to be transformed from words to numbers. Then, you can use statistical analysis to answer your research questions.

Using formalized and established hypothesis testing procedures means that you have to carefully consider and report your research variables, predictions, data collection and testing methods before coming to a conclusion.

Using precise and restrictive operational definitions may inadequately represent complex concepts. For example, the concept of mood may be represented with just a number in quantitative research, but explained with elaboration in qualitative research.

Despite standardized procedures, structural biases can still affect quantitative research. Missing data, imprecise measurements or inappropriate sampling methods are biases that can lead to the wrong conclusions.

Quantitative data is the value of data in the form of counts or numbers where each data set has a unique numerical value. This data is any quantifiable information that researchers can use for mathematical calculations and statistical analysis to make real-life decisions based on these mathematical derivations.

Data collection forms a major part of the research process. This data, however, has to be analyzed to make sense of. There are multiple methods of analyzing quantitative data collected in surveys. They are:

For Quantitative Data, raw information has to presented in a meaningful manner using data analysis methods. This data should be analyzed to find evidential data that would help in the research process.

Using quantitative data in an investigation is one of the best strategies to guarantee reliable results that allow better decisions. In summary, quantitative data is the basis of statistical analysis.

a method of research that relies on measuring variables using a numerical system, analyzing these measurements using any of a variety of statistical models, and reporting relationships and associations among the studied variables. For example, these variables may be test scores or measurements of reaction time. The goal of gathering this quantitative data is to understand, describe, and predict the nature of a phenomenon, particularly through the development of models and theories. Quantitative research techniques include experiments and surveys.

When collecting and analyzing data, quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings. Both are important for gaining different kinds of knowledge.

  • Qualitative research is also at risk for certain research biases including the Hawthorne effect, observer bias, recall bias, and social desirability bias.Table of contentsThe differences between quantitative and qualitative research

  • Data collection methods

  • When to use qualitative vs. quantitative research

  • How to analyze qualitative and quantitative data

  • Frequently asked questions about qualitative and quantitative research

Many data collection methods can be either qualitative or quantitative. For example, in surveys, observational studies or case studies, your data can be represented as numbers (e.g., using rating scales or counting frequencies) or as words (e.g., with open-ended questions or descriptions of what you observe).

A research project is an academic, scientific, or professional undertaking to answer a research question. Research projects can take many forms, such as qualitative or quantitative, descriptive, longitudinal, experimental, or correlational. What kind of research approach you choose will depend on your topic.

Statistical analysis means investigating trends, patterns, and relationships using quantitative data. It is an important research tool used by scientists, governments, businesses, and other organizations.

To draw valid conclusions, statistical analysis requires careful planning from the very start of the research process. You need to specify your hypotheses and make decisions about your research design, sample size, and sampling procedure.

A statistical hypothesis is a formal way of writing a prediction about a population. Every research prediction is rephrased into null and alternative hypotheses that can be tested using sample data.

In theory, for highly generalizable findings, you should use a probability sampling method. Random selection reduces several types of research bias, like sampling bias, and ensures that data from your sample is actually typical of the population. Parametric tests can be used to make strong statistical inferences when data are collected using probability sampling.

Knowing whether what you notice in your results is statistically significant or not gives you the green light to confidently make decisions and present findings based on your results, since statistical methods provide a degree of certainty that most people recognize as valid. So having results that are statistically significant is a hugely important detail for businesses as well as academics and researchers.

Descriptive Statistics are used to present quantitative descriptions in a manageable form. In a research study we may have lots of measures. Or we may measure a large number of people on any measure. Descriptive statistics help us to simplify large amounts of data in a sensible way. Each descriptive statistic reduces lots of data into a simpler summary. For instance, consider a simple number used to summarize how well a batter is performing in baseball, the batting average. This single number is simply the number of hits divided by the number of times at bat (reported to three significant digits). A batter who is hitting .333 is getting a hit one time in every three at bats. One batting .250 is hitting one time in four. The single number describes a large number of discrete events. Or, consider the scourge of many students, the Grade Point Average (GPA). This single number describes the general performance of a student across a potentially wide range of course experiences.

Conjointly is an all-in-one survey research platform, with easy-to-use advanced tools and expert support. It gives you access to millions of survey respondents and sophisticated product and pricing research methods.

Interpretations related to mixed (sometimes called merged) methods vary; be wary of jargon! Gery Ryan, of the Kaiser Permanente School of Medicine, gives these definitions, while arguing that we should be thinking of the purposes of the research rather than the methodological labels:

The most labor-intensive version of persona creation involves collecting data via a survey sent to a large sample of your user base and then using statistical analysis to find clusters of similar responses. While I describe these as statistical personas, really, they are mixed-method personas, because they are based on both qualitative and quantitative research.

Data analysis is how researchers go from a mass of data to meaningful insights. There are many different data analysis methods, depending on the type of research. Here are a few methods you can use to analyze quantitative and qualitative data.

Qualitative data analysis works a little differently from quantitative data, primarily because qualitative data is made up of words, observations, images, and even symbols. Deriving absolute meaning from such data is nearly impossible; hence, it is mostly used for exploratory research. While in quantitative research there is a clear distinction between the data preparation and data analysis stage, analysis for qualitative research often begins as soon as the data is available.

Associated with the natural, applied, formal, and social sciences this research strategy promotes the objective empirical investigation of observable phenomena to test and understand relationships. This is done through a range of quantifying methods and techniques, reflecting on its broad utilization as a research strategy across differing academic disciplines.[2][3][4]

The objective of quantitative research is to develop and employ mathematical models, theories, and hypotheses pertaining to phenomena. The process of measurement is central to quantitative research because it provides the fundamental connection between empirical observation and mathematical expression of quantitative relationships. 041b061a72


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