Handbook of Quantitative Methods for Educational Research
- © 2013
- Timothy Teo 0
University of Auckland, New Zealand
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- This handbook has something for both the beginner and advanced educational researchers.
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Structural Equation Modeling Approaches in Educational Research and Practice
Final Remarks
Perplexing Times in Educational Research and the Prospects for a New Platinum Standard
- educational research
- quantitative methods
Table of contents (17 chapters)
Front matter, measurement theory, psychometrics.
- Mark Wilson, Perman Gochyyev
Classical Test Theory
- Ze Wang, Steven J. Osterlind
Item Response Theory
- Xitao Fan, Shaojing Sun
Methods of Analysis
Multiple regression.
- Ken Kelley, Jocelyn H. H. Bolin
Cluster Analysis
- Christine Distefano, Diana Mindrila
Multivariate Analysis of Variance
- Lisa L. Harlow, Sunny R. Duerr
Logistic Regression
- Brian F. French, Hason C. Immekus, Hsiao-Ju Yen
Exploratory Factor Analysis
- W. Holmes finch
A Brief Introduction to Hierarchical Linear Modeling
- Jason W. Osborne, Shevaun D. Neupert
Longitudinal Data Analysis
- D. Betsy mccoach, John P. Madura, Karen E. Rambohernandez, Ann A. O’connell, Megan E. Welsh
Meta-Analysis
- Spyros Konstantopoulos
Agent Based Modelling
- Mauricio Salgado, Nigel Gilbert
Mediation, Moderation & Interaction
- James Hall, Pamela Sammons
Structural Equation Models
Introduction to confirmatory factor analysis and structural equation modeling.
- Matthew W. Gallagher, Timothy A. Brown
Testing Measurement and Structural Invariance
- Daniel A. Sass, Thomas A. Schmitt
Mixture Models in Education
- George A. Marcoulides, Ronald H. Heck
Editors and Affiliations
Timothy Teo
Bibliographic Information
Book Title : Handbook of Quantitative Methods for Educational Research
Editors : Timothy Teo
DOI : https://doi.org/10.1007/978-94-6209-404-8
Publisher : SensePublishers Rotterdam
eBook Packages : Humanities, Social Sciences and Law , Education (R0)
Copyright Information : SensePublishers 2013
eBook ISBN : 978-94-6209-404-8 Published: 07 February 2014
Edition Number : 1
Number of Pages : VIII, 404
Topics : Education, general
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Methodology
- What Is Quantitative Research? | Definition, Uses & Methods
What Is Quantitative Research? | Definition, Uses & Methods
Published on June 12, 2020 by Pritha Bhandari . Revised on June 22, 2023.
Quantitative research is the process of collecting and analyzing numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalize results to wider populations.
Quantitative research is the opposite of qualitative research , which involves collecting and analyzing non-numerical data (e.g., text, video, or audio).
Quantitative research is widely used in the natural and social sciences: biology, chemistry, psychology, economics, sociology, marketing, etc.
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Table of contents
Quantitative research methods, quantitative data analysis, advantages of quantitative research, disadvantages of quantitative research, other interesting articles, frequently asked questions about quantitative research.
You can use quantitative research methods for descriptive, correlational or experimental research.
- In descriptive research , you simply seek an overall summary of your study variables.
- In correlational research , you investigate relationships between your study variables.
- In experimental research , you systematically examine whether there is a cause-and-effect relationship between variables.
Correlational and experimental research can both be used to formally test hypotheses , or predictions, using statistics. The results may be generalized to broader populations based on the sampling method used.
To collect quantitative data, you will often need to use operational definitions that translate abstract concepts (e.g., mood) into observable and quantifiable measures (e.g., self-ratings of feelings and energy levels).
| Research method | How to use | Example |
|---|---|---|
| Control or manipulate an to measure its effect on a dependent variable. | To test whether an intervention can reduce procrastination in college students, you give equal-sized groups either a procrastination intervention or a comparable task. You compare self-ratings of procrastination behaviors between the groups after the intervention. | |
| Ask questions of a group of people in-person, over-the-phone or online. | You distribute with rating scales to first-year international college students to investigate their experiences of culture shock. | |
| (Systematic) observation | Identify a behavior or occurrence of interest and monitor it in its natural setting. | To study college classroom participation, you sit in on classes to observe them, counting and recording the prevalence of active and passive behaviors by students from different backgrounds. |
| Secondary research | Collect data that has been gathered for other purposes e.g., national surveys or historical records. | To assess whether attitudes towards climate change have changed since the 1980s, you collect relevant questionnaire data from widely available . |
Note that quantitative research is at risk for certain research biases , including information bias , omitted variable bias , sampling bias , or selection bias . Be sure that you’re aware of potential biases as you collect and analyze your data to prevent them from impacting your work too much.
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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 .
Descriptive statistics will give you a summary of your data and include measures of averages and variability. You can also use graphs, scatter plots and frequency tables to visualize your data and check for any trends or outliers.
Using inferential statistics , you can make predictions or generalizations based on your data. You can test your hypothesis or use your sample data to estimate the population parameter .
First, you use descriptive statistics to get a summary of the data. You find the mean (average) and the mode (most frequent rating) of procrastination of the two groups, and plot the data to see if there are any outliers.
You can also assess the reliability and validity of your data collection methods to indicate how consistently and accurately your methods actually measured what you wanted them to.
Quantitative research is often used to standardize data collection and generalize findings . Strengths of this approach include:
- Replication
Repeating the study is possible because of standardized data collection protocols and tangible definitions of abstract concepts.
- Direct comparisons of results
The study can be reproduced in other cultural settings, times or with different groups of participants. Results can be compared statistically.
- Large samples
Data from large samples can be processed and analyzed using reliable and consistent procedures through quantitative data analysis.
- Hypothesis testing
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.
Despite the benefits of quantitative research, it is sometimes inadequate in explaining complex research topics. Its limitations include:
- Superficiality
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.
- Narrow focus
Predetermined variables and measurement procedures can mean that you ignore other relevant observations.
- Structural bias
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.
- Lack of context
Quantitative research often uses unnatural settings like laboratories or fails to consider historical and cultural contexts that may affect data collection and results.
If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.
- Chi square goodness of fit test
- Degrees of freedom
- Null hypothesis
- Discourse analysis
- Control groups
- Mixed methods research
- Non-probability sampling
- Inclusion and exclusion criteria
Research bias
- Rosenthal effect
- Implicit bias
- Cognitive bias
- Selection bias
- Negativity bias
- Status quo bias
Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.
Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.
In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .
Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.
Operationalization means turning abstract conceptual ideas into measurable observations.
For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.
Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.
Reliability and validity are both about how well a method measures something:
- Reliability refers to the consistency of a measure (whether the results can be reproduced under the same conditions).
- Validity refers to the accuracy of a measure (whether the results really do represent what they are supposed to measure).
If you are doing experimental research, you also have to consider the internal and external validity of your experiment.
Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.
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