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Quantitative Research Design (part 1) lecture (CC-BY, 2020)

CC BY is the correct license for this work. Ignore the ones in the slides: use this materials as you like, with attribution. (KW, 02-2020)

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Research designs are plans and the procedures for research that span the decisions from broad assumptions to detailed methods of data collection and analysis. This plan involves several decisions, and they need not be taken in the order in which they make sense to me and the order of their presentation here. The overall decision involves which design should be used to study a topic. Informing this decision should be the worldview assumptions the researcher brings to the study; procedures of inquiry (called strategies); and specific methods of data collection, analysis, and interpretation. The selection of a research design is also based on the nature of the research problem or issue being addressed, the researchers’ personal experiences, and the audiences for the study.

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The purpose of this study is to compare and contrast two quantitative scholarly articles, identify and analyze the designs in each one, summarize the rationale of the design for each, briefly state the results of each article, and also critique both of them. The goal is also to show that even though two articles use the same research design they can produce different perspective of their results.

Presented at Academic Business World International Conference. Nashville, Tennessee. May, 2007

Greg L Lowhorn

It is often difficult to choose between quantitative and qualitative research design. At times, a researcher may choose a design because he or she is more familiar with one method or the other or a colleague recommends a particular design. However, our research will be more helpful if we make our decision based on well-considered, suitable design rather than simply choosing a design that is more familiar or comfortable to the researcher. The purpose of this paper is to introduce graduate students and new researchers to quantitative and qualitative research design and to help them choose the best method based on the type of information needed and analytical capability.

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Bostley Asenahabi

For a research to be carried out successfully, it requires suitable research design. This is a plan adopted by a researcher before data collection commences so as to achieve the research objective in a valid way. The essence of research design is to translate a research problem into data for analysis so as to provide relevant answers to research questions at a minimum cost. This paper investigates what research design is, the different kinds of research design and how a researcher can choose the appropriate research design for his/her study. The study reveals that research design choice is guided by a careful analysis of statement of the problem, research questions, conceptual /theoretical framework and analyzing the relevant literature.

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• What Is a Research Design | Types, Guide & Examples

What Is a Research Design | Types, Guide & Examples

Published on June 7, 2021 by Shona McCombes . Revised on November 20, 2023 by Pritha Bhandari.

A research design is a strategy for answering your   research question  using empirical data. Creating a research design means making decisions about:

• Your overall research objectives and approach
• Whether you’ll rely on primary research or secondary research
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research objectives and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, other interesting articles, frequently asked questions about research design.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities—start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed-methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

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Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types.

• Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships
• Descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analyzing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study—plants, animals, organizations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

• Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalize your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study , your aim is to deeply understand a specific context, not to generalize to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question .

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviors, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews .

Observation methods

Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what kinds of data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected—for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

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As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are high in reliability and validity.

Operationalization

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalization means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in—for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced, while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method , you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample—by mail, online, by phone, or in person?

If you’re using a probability sampling method , it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method , how will you avoid research bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organizing and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymize and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well-organized will save time when it comes to analyzing it. It can also help other researchers validate and add to your findings (high replicability ).

On its own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyze the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarize your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarize your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analyzing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

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.

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.

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Understanding Quantitative Research Design

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Quantitative Research

Jan 01, 2020

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Quantitative Research. Counting, and reporting. Quantitative Research. Numbers-based – Quantitative research refers to the manipulation of numbers to make claims, provide evidence, describe phenomena, determine relationships, or determine causation.

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Quantitative Research Counting, and reporting

Quantitative Research • Numbers-based – Quantitative research refers to the manipulation of numbers to make claims, provide evidence, describe phenomena, determine relationships, or determine causation. • Deductive – usually tests a hypothesis based on previous research. Numbers are important to determine when a hypothesis has been confirmed or not. You are looking FOR something. • Generalizable – through statistical or mathematical modeling, can make predictions about future events.

Quantitative Research Ideas and Controversy

Goals • The intention of the “empirical/quantitative” part of the research tradition course goal is to consider the rhetorical and research benefits (and drawbacks) of using numbers as evidence. • It is less about advanced quantitative reasoning and more about teaching an appreciation of what it means to produce, analyze, and report numbers in support of an argument or to answer a research question.

Quantitative research • Descriptive Research – describe a phenomenon using numbers • Quantitative research is good at this because it shows aggregates (combined instances) in an easy to see way. • Inferential research – make a prediction or comparison about what you found using numbers • Quantitative research is good at this because numbers are precise, they can be used for comparison better than qualitative research (cold v. cool OR 45 v 55)

Experimental Research • Experimental – testing whether a “thing” (independent variable) applied to a subject/group has an effect (dependant variable) • Two equal groups, one control, one experimental • Pre-test, post-test • Quasi-experimental – testing whether a “thing” applied to a subject/group has an effect but without being able to • Actively apply the “thing” • Control for other variables

Top 4 things • Numbers are a semiotic system. As such, they are arbitrary. They aren’t inherently more “true” than any other sign. • Numbers are precise, which makes them really good for specificity and measuring. • Numbers are mutually exclusive, so they are good for comparisons. • Numbers can be manipulated using formula to discover causes, make predictions, and generalize from samples to populations.

Quantitative Controversy • Some scholars believe that human experience, attitudes and beliefs cannot be quantified. • A person’s “feelings” can change from day to day with little conscious thought of the fact • Some scholars believe that quantitative research is trusted more than it should be • Bridges still collapse, spaceships still get lost, new cars still break down, pharmaceutical companies still produce harmful drugs, computers still crash • Some scholars believe that quantitative research is reductive • Statistically speaking, your SAT has already predicted what your final college GPA is going to be. Will you only ever be 1400 or 3.6 smart? • Some scholars believe that quantitative research misses important nuances • In a ChangeWave survey, the iPhone had the highest customer satisfaction with 79% of the sample “very satisfied.”

Fieldwork methods • Observations • count the number of people who hold the door open for another at Driscoll or the library • count the number of people looking at their phones while with other people in the dining halls • count the number of people who return shopping carts to the stalls. • Surveys • hand out paper or online surveys related to a research question. • Experiments • use online personality test to find a Meyers-Briggs type and then ask participants to complete a word search, keeping track of how long they take and what words they find first, second, third… • blind, soda taste test or pizza test after giving them different descriptions about what they are about to taste.

Designing studies

Research Plan • As we follow this scientific method, recognize that it really is just a research plan, but in a more focused manner. You would still benefit much from working out the following BEFORE you conduct your study • Research Question • Method • Plan • Timeline

Define the Question • Defining the question, often called your research question, determines the scope of what you are able to research. A good research question should be (FINER): • Feasible – is it a realistic question to ask? • Interesting – will we learn something from it? • Novel – have very few people done it? • Ethical – does it respect the participants? • Relevant – will we be able to do something with the findings? Hulley S, Cummings S. (Eds ) Designing Clinical Research. Willimas & Wilkins: Baltimore, 1988

Defining the Question • To create a Quantitative Research Question • Define your participants • Define your issue • Define the variables of that issue • Ask a question of the participants, issue, and variable • Do DU students have part-time jobs that they enjoy? • Are college major and writing anxiety in undergraduatewriters correlated?

Gather Information and Resources • Text-Based Research is useful in helping you define your expectation (hypothesis). You want to find what has come before in the topic or related topics. You will rarely find your exact study (if you do, then your research question isn’t novel). You are looking for elements, pieces of your topic that have come before. • Previous studies have determined that experienced female MMORPG players have a primary motivation to play for social reasons (Yee, 2007). Other studies have shown no gender differences when looking at more than a single primary motivation (Tychsen, Hitchens, & Brolund, 2008). In these studies, the intention was to look at motivation in experienced RPG/MMORPG players—in my current study, I intend to look at motivation to play MMORPG World of Warcraft by non-experienced gamers.

Gather Information and Resources • Your experiences as well as those experiences of your friends can also be useful in helping you gather information and resources.

Form Hypothesis • What is your best guess as to the outcome of your Research Question • The hypothesis is based on your Research Question, but it is not phrased as a question – it is phrased as your best guess as to the outcome of that question • DU students do not enjoy their part-time work • College major and writing anxiety in undergraduate writers is not related. • You might create sub-hypotheses to account for other variables that you might consider relevant (e.g. gender, class-standing). You tag these on to the end of your initial hypothesis. • Seniors tend to enjoy their part-time work more than first-year students enjoy their work. • Female undergraduates experience more anxiety than males.

Design Experiment • Determine what best would address the research question you are asking. In quantitative studies, a survey works the best because you can control responses. • Determine triangulation questions or observations for two reasons: • You don’t want it to be obvious what you are asking • Other variables may be affecting the outcome. • Refer to Chapter 8 in Situating Research (or the handout Conducting Surveys) when coming up with your Survey Questions • It’s a good idea to playtest your survey with one or more people so that they can give you feedback about what questions might be confusing.

Design Experiment • Likert-type scale (1-5) will allow you to “quantify” human beliefs, attitudes, and experiences. Likert-type scales are used often in social science, descriptive studies. • Usually ask positive questions, and then follow with whether the person agrees or disagrees. • Usually scaled so that higher numbers are positive/agreement, lower numbers negative/disagreement “A good writing class should consist of lectures on grammar” 1-strongly disagree, 2-disagree, 3-agree, 4-strongly agree “How satisfied are you with University of Denver’s dorms” 1-very unsatisfied, 2-unsatisfied, 3-satisfied, 4-very satisfied

Design Experiment • Planning your survey, your hypothesis, your plan is vital BEFORE you conduct your survey because you only get one shot at the survey.

The Final Four • Collect Data (Perform Experiment, conduct survey, conduct observation) – remember to be professional, take notes (you never know what might effect your results), and ethical. • Analyze Data – Keep track of your data, put it in a spreadsheet, and work the numbers. We will be talking a bit about statistics here, but really, all you will be expected to do is descriptive analysis • Interpret Data and Draw Conclusions • Publish Results (see IMRAD PowerPoint)

Very Short Guide to Stats for Quantitative Research Papers Basics of aggregate and statistical data

Inferential v. Descriptive • Descriptive statistics “describe” the data of a sample or population. They are usually aggregate data • Average (Mean) GPA • Standard Deviation of SAT score • Inferential statistics “infer” (i.e. conclude) relationships between a sample AND a population, or “infer” past, present or future results of a sample/population based on its data. • Regression/correlation analysis of GPA and SAT (relationship between SAT and GPA, and SAT can be used to predict GPA)

N = number of participants • In inferential statistics, you would refer to the number of participants in your survey as N. If it is a sample or part of a whole, it is n (lowercase), and if it is a total population, it is N (uppercase). • Population: N = 4,432 • Sample: n = 100 • In descriptive studies and descriptive statistics, it is common to refer to participants as N, subgroups of those participants as n • Of the total students surveyed (N = 100), only 10% (n = 10) were male.

Measures of central tendency • Central Tendency measures common “middles” • Mean is the arithmetic average of items or values • Mode is the most occurring item or value • Median is the item or value of which 50% are greater and 50% are less. • Sometimes GPA or time can be used as a measure, but another measure is one of attitudes and beliefs using a Likert-type scale. • Standard Deviation is a measure of the spread of items or values in a series. Understanding the variation can help you see how close a particular item or value is to other numbers. • Distribution (Histogram) is a visual representation of the number of a particular result in an array of numbers. In this series (number of hours I played WoW over break):8, 0, 0, 3, 2, 10, 0 • Mean = 3.29, Mode = 0, Median = 2, SD = 4.11 In this series (number of hours I worked this week):8, 8, 8, 8, 6, 6, 5 • Mean = 7, Mode = 8, Median = 8, SD = 1.29

Correlations and comparisons Student’s t-test The “basic” comparison test between a sample and a population. Designed at Guinness beer in 1908. T Tests are reported like, t(degrees of freedom) = t statistic P = significance level. The t-statistic is less important than the significance level. Pearson Correlation The “basic” correlation test between two samples. Designed by publisher Pearson to compare test scores. Pearson correlations are reported like, r = correlation score between 1.00 (positive correlation) and -1.00 (negative correlation).

Using Excel to do your stats • Mean { =average(range) } • You can compute mode { =mode(range) } or median {=median(range) }, but they might not be as useful in this project. • Standard Deviation { =stdev(range) } • You can also count the number of instances of a value including instances of text: { =countif(range,”value”) } • The following example would count every instance of “male” in the range: =countif(A2:A7,”male”) • Pearson’s correlation { =correl(range1,range2) } • Student’s t-test { =ttest(range1,range2,tails,type) } • You can create frequency distribution histograms by using Tools -> Data Analysis, then Historgram. Histograms count the number of instances of a result in a given array. You can also find these commands by using Insert -> Function. There are also far more complex inferential statistics available in Excel • You can do a complete Descriptive Stats Summary by selecting Tools > Data Analysis (If you don’t see a Data Analysis, then (Excel 2003) Tools > Add-ins > Analysis ToolPak; (Excel 2007) Excel Options > Add-ins > Manage Add-ins > Analysis ToolPak

Writing Stats in APA • Standard Deviation = SD • Mean = M • Descriptive statistics are often written in parentheses after an item that the statistic refers to, and symbols and numbers should be separated by a space • In a survey of DU students, participants (N = 100) responded that money was more important (M = 4.2, SD = .9) than experience (M = 3.5, SD = .76) in selecting a summer job. • In a survey of computer game addicts, females (n = 15) were more likely to be depressed during withdrawal (M = 5.2, SD = .45) than males were (n = 78, M = 3.2, SD = .98) • Chapter 8 in Situating Research has more about this.

Charts and Graphs It’s important in doing graphs that you compute an aggregate (sum, average, SD, something) before graphing information. You cannot just Select All of data and make a graph out of it. • Pie graphs – good for showing distributions of a total population (you will have to compute aggregates first) • Line graphs – good for showing time-based, linear progression • Column/Bar graphs – good for showing distribution of individual responses (you will have to create aggregates first) • Y-Axis (vertical) for variables, X-Axis (horizontal) for participants.

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• Preferences

Quantitative Research Designs - PowerPoint PPT Presentation

Quantitative Research Designs

Quantitative research designs v. darleen opfer conditions for establishing cause-effect relationships: covariation temporal precedence no plausible alternative ... – powerpoint ppt presentation.

• V. Darleen Opfer
• Covariation
• Temporal Precedence
• No Plausible Alternative Explanations
• Instrumentation
• Selection by maturation interaction
• Ambiguity about causal direction
• Diffusion of treatments
• Compensatory equalization of treatments
• Compensatory rivalry
• By argument
• By measurement or observation
• By analysis
• By preventive action
• Program(s) or Treatment (s) X
• Observation(s) or Measure(s) O
• Groups or Individuals
• R Randomly assigned groups
• Expanding across time
• O X O O X O
• Expanding across programs
• Expanding across observations
• O1O2 X O1O2
• Expanding across groups
• ______________________________
• Depict the hypothesized causal relationship
• Identify the possible alternative explanation threats
• Over-expand the basic design by expanding across time, program observations, and groups accounting for as many alternative explanations as possible
• Scale back the design to a manageable plan by considering the effect of eliminating each design component
• Theory-Grounded
• Situational
• A principal/head teacher wants to know whether the provision of professional development will improve the instructional practices of teachers.
• Design a study for answering her question.
• Address the following questions
• What are your variables? (What are will you be measuring?)
• What are potential validity/alternative explanation issues with this study?
• What are the validity issues accounted for in your approach? And how would you minimize those not accounted for in the design?

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