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Introduction to the Quantitative Research Process

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Critical Reading Strategies: Overview of Research Process

Andrea M. Landis, PhD, RN UW LEAH

Introduction to Research Methodology

Copyright © 2005 Pearson Education Canada Inc. 1 Psychology as a Science Theory development involves collecting interrelated ideas and observations Taken.

Tips on Critiquing Articles The goal of the educational research is to observe phenomena in the field of education and attempt to explain why these phenomena.

Introduction to Research

Chapter 1 Conducting & Reading Research Baumgartner et al Chapter 1 Nature and Purpose of Research.

Happy semester with best wishes from all nursing staff Dr Naiema Gaber

Topics - Reading a Research Article Brief Overview: Purpose and Process of Empirical Research Standard Format of Research Articles Evaluating/Critiquing.

Personality, 9e Jerry M. Burger

FOUNDATIONS OF NURSING RESEARCH Sixth Edition CHAPTER Copyright ©2012 by Pearson Education, Inc. All rights reserved. Foundations of Nursing Research,

Introduction to Communication Research

Research problem, Purpose, question

Introduction to Nursing Research and Evidence-Based Practice

Chapter One: The Science of Psychology

Elsevier Science (USA) items and derived items copyright © 2003, Elsevier Science (USA). All rights reserved. Chapter 2 Introduction to the Quantitative.

RESEARCH DESIGN.

Fig Theory construction. A good theory will generate a host of testable hypotheses. In a typical study, only one or a few of these hypotheses can.

McGraw-Hill © 2006 The McGraw-Hill Companies, Inc. All rights reserved. The Nature of Research Chapter One.

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Descriptive Research: Methods, Types, and Examples | PPT

In order to understand what descriptive research is, one must first understand the different types of research methods. Descriptive research can be defined as a method used to describe something, usually in great detail. This type of research is often used in the sciences, such as in biology or psychology.

Table of Contents

What is Descriptive Research?

Descriptive research is a type of research that is used to describe a population or phenomenon. This type of research is often used in the social sciences, but can be used in other disciplines as well.

Descriptive research can be either quantitative or qualitative in nature.

Quantitative descriptive research

Qualitative descriptive research, examples of descriptive research, characteristics of descriptive research, quantitative in nature.

In descriptive research, data is systematically and quantitatively collected so that the research problem may be statistically analyzed. It does not involve the manipulation of variables. This type of research is typically quantitative, meaning that it uses numerical data to describe the population or phenomenon.

Observational

Uncontrolled variables, basis for further research.

The data gathered during descriptive research serves as a foundation for subsequent study since it aids in gaining a thorough grasp of the research topic in order to properly respond to it.

Cross-sectional Studies

Pros of descriptive research, comprehensive.

Descriptive research frequently combines quantitative and qualitative methods, giving the research topic a more detailed knowledge.

Various Data Collection Techniques

High external validity, quick and inexpensive, cons of descriptive research, unable to validate or test research question, risk of sampling error.

When choosing a sample group for a descriptive research study, random sampling is typically used. If the sample group isn’t representative of the larger population, chance may cause sampling error. Results from sampling mistake would be unreliable and unreliable.

Absence of Dependability

Possibility of false responses, pros and cons of descriptive research.

Why to use Descriptive Research?

Comparing variables.

Descriptive research can be used to compare various variables and the responses of various demographics to various variables.

Validate the Current Conditions

Analysis of data trends.

The descriptive research approach can be used to track changes in variables over time, enabling the discovery and analysis of trends.

Describe the Features of the Subjects

Methods of descriptive research.

There are three key methods used to carry out descriptive research.

Researchers can develop hypotheses through case studies that can broaden the scope of evaluation when researching the phenomenon.

Observations

In conclusion, Descriptive Research is a type of research used to observe and describe phenomena. It is useful in providing detailed information about a specific event, behavior, or group. Although it cannot be used to draw causal relationships, it can be helpful in generating hypotheses for further research.

Other articles

 Methodology

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Descriptive Statistics | Definitions, Types, Examples

Published on July 9, 2020 by Pritha Bhandari . Revised on June 21, 2023.

Descriptive statistics summarize and organize characteristics of a data set. A data set is a collection of responses or observations from a sample or entire population.

In quantitative research , after collecting data, the first step of statistical analysis is to describe characteristics of the responses, such as the average of one variable (e.g., age), or the relation between two variables (e.g., age and creativity).

The next step is inferential statistics , which help you decide whether your data confirms or refutes your hypothesis and whether it is generalizable to a larger population.

Table of contents

Types of descriptive statistics, frequency distribution, measures of central tendency, measures of variability, univariate descriptive statistics, bivariate descriptive statistics, other interesting articles, frequently asked questions about descriptive statistics.

There are 3 main types of descriptive statistics:

• The distribution concerns the frequency of each value.
• The central tendency concerns the averages of the values.
• The variability or dispersion concerns how spread out the values are.

You can apply these to assess only one variable at a time, in univariate analysis, or to compare two or more, in bivariate and multivariate analysis.

• Go to a library
• Watch a movie at a theater
• Visit a national park

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A data set is made up of a distribution of values, or scores. In tables or graphs, you can summarize the frequency of every possible value of a variable in numbers or percentages. This is called a frequency distribution .

• Simple frequency distribution table
• Grouped frequency distribution table

From this table, you can see that more women than men or people with another gender identity took part in the study. In a grouped frequency distribution, you can group numerical response values and add up the number of responses for each group. You can also convert each of these numbers to percentages.

Measures of central tendency estimate the center, or average, of a data set. The mean, median and mode are 3 ways of finding the average.

Here we will demonstrate how to calculate the mean, median, and mode using the first 6 responses of our survey.

The mean , or M , is the most commonly used method for finding the average.

To find the mean, simply add up all response values and divide the sum by the total number of responses. The total number of responses or observations is called N .

The median is the value that’s exactly in the middle of a data set.

To find the median, order each response value from the smallest to the biggest. Then , the median is the number in the middle. If there are two numbers in the middle, find their mean.

The mode is the simply the most popular or most frequent response value. A data set can have no mode, one mode, or more than one mode.

To find the mode, order your data set from lowest to highest and find the response that occurs most frequently.

Measures of variability give you a sense of how spread out the response values are. The range, standard deviation and variance each reflect different aspects of spread.

The range gives you an idea of how far apart the most extreme response scores are. To find the range , simply subtract the lowest value from the highest value.

Standard deviation

The standard deviation ( s or SD ) is the average amount of variability in your dataset. It tells you, on average, how far each score lies from the mean. The larger the standard deviation, the more variable the data set is.

There are six steps for finding the standard deviation:

• List each score and find their mean.
• Subtract the mean from each score to get the deviation from the mean.
• Square each of these deviations.
• Add up all of the squared deviations.
• Divide the sum of the squared deviations by N – 1.
• Find the square root of the number you found.

Step 5: 421.5/5 = 84.3

Step 6: √84.3 = 9.18

The variance is the average of squared deviations from the mean. Variance reflects the degree of spread in the data set. The more spread the data, the larger the variance is in relation to the mean.

To find the variance, simply square the standard deviation. The symbol for variance is s 2 .

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Univariate descriptive statistics focus on only one variable at a time. It’s important to examine data from each variable separately using multiple measures of distribution, central tendency and spread. Programs like SPSS and Excel can be used to easily calculate these.

If you were to only consider the mean as a measure of central tendency, your impression of the “middle” of the data set can be skewed by outliers, unlike the median or mode.

Likewise, while the range is sensitive to outliers , you should also consider the standard deviation and variance to get easily comparable measures of spread.

If you’ve collected data on more than one variable, you can use bivariate or multivariate descriptive statistics to explore whether there are relationships between them.

In bivariate analysis, you simultaneously study the frequency and variability of two variables to see if they vary together. You can also compare the central tendency of the two variables before performing further statistical tests .

Multivariate analysis is the same as bivariate analysis but with more than two variables.

Contingency table

In a contingency table, each cell represents the intersection of two variables. Usually, an independent variable (e.g., gender) appears along the vertical axis and a dependent one appears along the horizontal axis (e.g., activities). You read “across” the table to see how the independent and dependent variables relate to each other.

Interpreting a contingency table is easier when the raw data is converted to percentages. Percentages make each row comparable to the other by making it seem as if each group had only 100 observations or participants. When creating a percentage-based contingency table, you add the N for each independent variable on the end.

From this table, it is more clear that similar proportions of children and adults go to the library over 17 times a year. Additionally, children most commonly went to the library between 5 and 8 times, while for adults, this number was between 13 and 16.

Scatter plots

A scatter plot is a chart that shows you the relationship between two or three variables . It’s a visual representation of the strength of a relationship.

In a scatter plot, you plot one variable along the x-axis and another one along the y-axis. Each data point is represented by a point in the chart.

From your scatter plot, you see that as the number of movies seen at movie theaters increases, the number of visits to the library decreases. Based on your visual assessment of a possible linear relationship, you perform further tests of correlation and regression.

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.

• Statistical power
• Pearson correlation
• Degrees of freedom
• Statistical significance

Methodology

• Cluster sampling
• Stratified sampling
• Focus group
• Systematic review
• Ethnography
• Double-Barreled Question

Research bias

• Implicit bias
• Publication bias
• Cognitive bias
• Placebo effect
• Pygmalion effect
• Hindsight bias
• Overconfidence bias

Descriptive statistics summarize the characteristics of a data set. Inferential statistics allow you to test a hypothesis or assess whether your data is generalizable to the broader population.

The 3 main types of descriptive statistics concern the frequency distribution, central tendency, and variability of a dataset.

• Distribution refers to the frequencies of different responses.
• Measures of central tendency give you the average for each response.
• Measures of variability show you the spread or dispersion of your dataset.
• Univariate statistics summarize only one variable  at a time.
• Bivariate statistics compare two variables .
• Multivariate statistics compare more than two variables .

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Descriptive Research Design: Survey and Observation

Jan 01, 2020

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Descriptive Research Design: Survey and Observation. MKTG 3350: MARKETING RESEARCH Yacheng Sun Leeds School of Business. 1. Dr. Yacheng Sun, UC Boulder. Figure 7.2 Survey and Observation: An Overview. Opening Vignette. Survey Methods: Advantages and Disadvantages. Fig 7.3.

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• survey methods
• telephone interviewing
• computer assisted
• survey methods advantages
• improving survey response rate

Presentation Transcript

Descriptive Research Design: Survey and Observation MKTG 3350:MARKETING RESEARCH Yacheng Sun Leeds School of Business 1 Dr. Yacheng Sun, UC Boulder

Figure 7.2 Survey and Observation: An Overview Opening Vignette Survey Methods: Advantages and Disadvantages Fig 7.3 Survey Methods Classified by Mode of Administration Fig 7.4 Tables 7.1, 7.2, 7.3 What Would You Do? Telephone Mail Personal Electronic Be a DM! Be an MR! Experiential Learning Improving Survey Response Rate Fig 7.5 Observation Methods Table 7.4 Personal Mechanical A Comparison of Survey and Observation Methods Application to Contemporary Issues (Fig 7.6) International (Table 7.5) Technology Ethics

Figure 7.3 Methods of Obtaining Quantitative Data in Descriptive Research OBSERVATION Information Obtained by Observing Behavior or Phenomena SURVEY Information Obtained by Questioning Respondents Figure 7.3 Methods of Obtaining Quantitative Data in Descriptive Research Quantitative Descriptive Research

Survey and Consumer Research Information of consumers can be classified into the following three categories: State of Being Behavior State of Mind Gender, Age, Income, Ethnicity, Occupation, Zip Code, Marital Status, etc.

Four Entities Involved In Survey • Researcher • chooses the survey method, designs the questionnaire, and decides the sampling plan • Interviewer • Conducts survey in the field • Respondent • The Interview Environment

Survey errors Non-response error Phenotypic (caused by survey) Genotypic (caused by respondents) Response error Ignorance / poor memory Ambiguous questions Social desirability bias Interviewer error

Other Errors in Survey Research Error from sampling e.g., in a brand image survey, only the buyers of that brand are contacted. Measurement error e.g., use interval measures for ages, incomes Data processing error e.g., enter wrong numbers into computer

1948 Presidential Election “Dewey Defeats Truman”  Chicago Daily Tribune Nov. 2, 1948

The Survey Method • The survey method of obtaining information is based on questioning respondents. • Perhaps the biggest issue researchers face is how to motivate respondents to candidly answer their questions. • Questions regarding behavior, intentions, attitudes, awareness, motivations, and demographic and lifestyle characteristics all lend themselves to survey research.

Advantages of Survey Research • Ease: Questionnaires are relatively easy to administer. • Reliability: Using fixed-response (multiple-choice) questions reduces variability in the results that may be caused by differences in interviewers and enhances reliability of the responses. • Simplicity: It also simplifies coding, analysis, and interpretation of data.

Disadvantages of Survey Research • Respondents may be unable or unwilling to provide the desired information. • Structured data collection involving a questionnaire with fixed-response choices may result in loss of validity for certain types of data, such as beliefs and feelings. • Properly wording questions is not easy.

Figure 7.4 Classification of Survey Methods Survey Methods Telephone Personal Mail Electronic In-Home Mail/Fax Interview E-Mail Traditional Telephone Mall Intercept Mail Panel Computer-Assisted Telephone Interviewing Computer-Assisted Personal Interviewing Internet

Traditional Telephone Interviews • Involve phoning a sample of respondents and asking them a series of questions. • The interviewer uses a paper questionnaire and records the responses with a pencil.

Computer-Assisted Telephone Interviewing • Uses a computerized questionnaire administered to respondents over the telephone. • The interviewer sits in front of a computer screen and wears a mini-headset.

Personal In-Home Interviews • Respondents are interviewed face-to-face in their homes. • The interviewer's task is to contact the respondents, ask the questions, and record the responses. • In recent years, the use of personal in-home interviews has declined.

Mall-Intercept Personal Interviews • Respondents are intercepted in shopping in malls. • The process involves stopping the shoppers, screening them for appropriateness, and either administering the survey on the spot or inviting them to a research facility located in the mall to complete the interview. • While not representative of the population in general, shopping mall customers do constitute a major share of the market for many products.

Computer-Assisted Personal Interviewing (CAPI) • The respondent sits in front of a computer terminal and answers a questionnaire on the screen by using the keyboard or a mouse. • Help screens and courteous error messages are provided. • The colorful screens and on- and off-screen stimuli add to the respondent's interest and involvement in the task.

Mail Interviews • A typical mail interview package consists of the outgoing envelope, cover letter, questionnaire, postage-paid return envelope, and possibly an incentive. • Those individuals motivated to do so complete and return the questionnaire through the mail. • There is no verbal interaction between the researcher and the respondent.

Mail Interviews (Cont.) • Individuals are selected for cold surveys through mailing lists the client maintains internally or has purchased commercially. • The type of envelope, the cover letter, the length of the questionnaire, and the incentive (if one is offered) all affect response rates.

Mail Panels • Mail panels consist of a large and nationally representative sample of individuals who have agreed to participate in periodic survey research. • Incentives in the form of cash or gifts are often offered to the individuals who agree to participate.

E-mail Surveys • If the addresses are known, the survey can simply be mailed electronically to respondents included in the sample. • Respondents key in their answers and send an e-mail reply. • Typically, a computer program is used to prepare the questionnaire and email address list, and to prepare the data for analysis.

Internet Surveys • An Internet survey is a questionnaire posted on a Web site that is self administered by the respondent. • The questions are displayed on the screen and the respondents provide answers by clicking an icon, keying in an answer, or highlighting a phrase. • Web survey systems are available for constructing and posting Internet surveys.

Table 7.1 Relative Advantages of Different Survey Methods

Table 7.1 (Cont.) Relative Advantages of Different Survey Methods

Table 7.2 Some Decisions Related to the Mail Interview Package Outgoing Envelope Outgoing Envelope: size, color, return address Postage Method of Addressing Cover Letter Sponsorship Signature Personalization Postscript Type of appeal Questionnaire Length Layout Content Color Size Format Reproduction Respondent anonymity Return Envelope Type of envelope Postage

Criteria for Selecting a Survey Method • When evaluating the various survey methods within the context of a specific research project, one has to consider the salient factors relevant to data collection. • Often, certain factors dominate, leading to a particular survey method as the natural choice. • If no method is clearly superior, the choice must be based on an overall consideration of the advantages and disadvantages of the various methods. • Often, in large projects these methods are combined to enhance the quality of data in a cost-effective manner.

Criteria for Selecting a Survey Method (Cont.) 1. If complex and diverse questions have to be asked, one of the personal methods (in-home, mall intercept, or CAPI) is preferable. Internet surveys are an option as well. 2. From the perspective of the use of physical stimuli, personal methods (in-home, mall intercept, or CAPI) are preferable. 3. If sample control is an issue, cold mail (but not mail panel), fax, and electronic methods might not be appropriate.

Criteria for Selecting a Survey Method (Cont.) 4. Control of the data collection environment favors the use of central location (mall intercept and CAPI) interviewing. 5. High quantity of data favors the use of in-home and mail panels and makes the use of telephone interviewing inappropriate. 6. Low response rates make the use of cold mail and electronic methods disadvantageous.

Criteria for Selecting a Survey Method (Cont.) 7. If social desirability is an issue, mail, mail-panel, fax, and Internet surveys are best. 8. If interviewer bias is an issue, the use of mail (cold and panels), fax, and electronic interviewing (e-mail and Internet) is favored. 9. Speed favors Internet, e-mail, telephone, and fax methods. 10.Costs favor cold mail, fax, electronic (e-mail and Internet), mail panels, telephone, mall intercept, CAPI, and in-home methods, in that order (most favorable to least favorable).

Figure 7.5 Improving Response Rates Methods of Improving Response Rates Prior Notification Incentives Follow-up Other Facilitators Monetary Nonmonetary Prepaid Promised

Improving Survey Response Rates • Prior notification consists of sending a letter or e-mail, or making a telephone call to potential respondents, thereby notifying them of the imminent mail, telephone, personal, or electronic survey. • Offering monetary as well as nonmonetary incentives to potential respondents can increase response rates. The prepaid incentive is included with the survey or questionnaire. The promised incentive is sent to only those respondents who complete the survey. Prepaid incentives have been shown to increase response rates to a greater extent than promised incentives.

Improving Survey Response Rates (Cont.) • Follow-up, or contacting the nonrespondents periodically after the initial contact, is particularly effective in decreasing refusals in mail surveys. Follow-up can also be done by telephone, e-mail, or personal contact. • Personalization, or sending letters addressed to specific individuals, is effective in increasing response rates.

Observation MethodsStructured Versus Unstructured Observation • For structured observation, the researcher specifies in detail what is to be observed and how the measurements are to be recorded, e.g., an auditor performing inventory analysis in a store. • In unstructured observation, the observer monitors all aspects of the phenomenon that seem relevant to the problem at hand, e.g., observing children playing with new toys.

Observation MethodsDisguised Versus Undisguised Observation • In disguised observation, the respondents are unaware that they are being observed. Disguise may be accomplished by using one-way mirrors, hidden cameras, or inconspicuous mechanical devices. Observers may be disguised as shoppers or sales clerks. • In undisguised observation, the respondents are aware that they are under observation.

Observation MethodsNatural Versus Contrived Observation • Naturalobservation involves observing behavior as it takes places in the environment. For example, one could observe the behavior of respondents eating fast food in Burger King. • In contrived observation, respondents' behavior is observed in an artificial environment, such as a test kitchen.

Observation MethodsPersonal Observation • A researcher observes actual behavior as it occurs. • The observer does not attempt to manipulate the phenomenon being observed but merely records what takes place. • For example, a researcher might record traffic counts and observe traffic flows in a department store.

Observation MethodsMechanical Observation Do not require respondents' direct participation. • the AC Nielsen audimeter • turnstiles that record the number of people entering or leaving a building. • on-site cameras (still, motion picture, or video) • optical scanners in supermarkets Do require respondent involvement. • eye-tracking monitors, pupilometers • psychogalvanometers • voice pitch analyzers • devices measuring response latency

Relative Advantages of Observation • They permit measurement of actual behavior rather than reports of intended or preferred behavior. • There is no reporting bias, and potential bias caused by the interviewer and the interviewing process is eliminated or reduced. • Certain types of data can be collected only by observation. • If the observed phenomenon occurs frequently or is of short duration, observational methods may be cheaper and faster than survey methods.

Relative Disadvantages of Observation • The reasons for the observed behavior may not be determined, since little is known about the underlying motives, beliefs, attitudes, and preferences. • Selective perception (bias in the researcher's perception) can bias the data. • Observational data are often time-consuming and expensive, and it is difficult to observe certain forms of behavior. • In some cases, the use of observational methods may be unethical, as in observing people without their knowledge or consent. • It is best to view observation as a complement to survey methods, rather than as being in competition with them.

Table 7.4 Relative Advantages of Observation Methods

Table 7.5 Impact of Cultural and Environmental Factors on Survey Methods •A survey that takes 20 minutes in the United States could take more than twice as long in Germany. The German language is not as concise as English, and Germans like to talk more than Americans do. For similar reasons, the interviewing time could be longer in other countries as well, such as in Brazil. • Telephone directories are unreliable in some countries (e.g., some African nations, such as Sierra Leone), because they are updated infrequently. • The incidence of unlisted telephones can vary widely across countries and across segments. For example, in Colombia, the numbers of some members of the elite and upper classes are never listed. • In some countries, such as Japan, China, Thailand, Malaysia, and those in Southeast Asia, telephone interviews are considered rude. In contrast, in some South American countries, such as Argentina and Peru, the response rates to telephone surveys is high given the low levels of telemarketing and the element of surprise in receiving an unexpected long-distance or local call. • Traditional personal interviewing methods remain popular in some European countries (e.g., Switzerland, Sweden, France), Asian countries (e.g., China, India, Hong Kong), African countries (e.g., Nigeria, Kenya), and South American countries (e.g., Colombia, Mexico) due to the prevalence of face-to-culture.

Table 7.5 Impact of Cultural and Environmental Factors on Survey Methods (Cont.)

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Quantitative Research Methods For Descriptive Analysis

This slide showcases the quantitative research methods for descriptive analysis which helps an organization to present data with less subjectivity and reduce errors. It include details such as experiment, survey, systematic observation, secondary research.

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This slide showcases the quantitative research methods for descriptive analysis which helps an organization to present data with less subjectivity and reduce errors. It include details such as experiment, survey, systematic observation, secondary research. Introducing our Quantitative Research Methods For Descriptive Analysis set of slides. The topics discussed in these slides are Qualitative Research Methods, Experiment. This is an immediately available PowerPoint presentation that can be conveniently customized. Download it and convince your audience.

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Research Methodologies: Descriptive Research

Cannot compute correlation at different age levels. longitudinal study ... record responses verbatim. keep reactions to yourself. not necessarily getting the facts ... – powerpoint ppt presentation.

• What is descriptive research?
• Types of descriptive studies
• Guidelines for interviews/questionnaires
• Population sampling
• Quantitative methodology
• Examines situation as it is
• Identify characteristics of observed phenomenon
• Explore possible correlations among phenomenon
• NOT to determine cause-and-effect relationships
• Observation studies
• Developmental designs
• Correlation studies
• Survey research
• Focus on particular aspect of behavior
• Must be quantifiable
• Maintain objectivity
• Define behavior in precise manner
• Segmented observation periods
• Rating scale to evaluate behavior
• Two or more independent raters
• Raters follow specific criteria
• How do particular characteristics change with age?
• Cross-sectional study
• Sample of different age groups
• Different environmental conditions
• Cannot compute correlation at different age levels
• Longitudinal study
• Single group followed over time
• Responses likely to improve because of practice
• Examine the relationship between multiple characteristics or variables
• Correlation
• When one variable increases, another variable increases or decreases consistently
• Regression Line predict perfect correlation
• What can we describe?
• Homogeneity/heterogeneity of variables
• Degree of correlation between variables
• Correlation Coefficient
• Meaning and Interpretation
• Income improves with experience
• Experience can help estimate income
• Watch out!!
• Children from lower socioeconomic groups have lower GPAs than children from higher socioeconomic groups
• Does this mean socioeconomic status affects academic achievement?
• What about other factors?
• Parents education levels, racial/ethnic discrimination, etc.
• Correlation does not indicate causation!
• A.k.a. descriptive survey, normative survey
• Learn about a large population by surveying a sample of that population
• Moment in time
• Extrapolate over a longer period
• Rely on self-report data
• Truth as respondent believes it
• Tailor response to what researcher wants to hear
• Interviewees representative of group
• Suitable location
• Written permission
• Establish rapport
• Focus on actual, not hypothetical
• Dont put words in peoples mouths
• Record responses verbatim
• Keep reactions to yourself
• Not necessarily getting the facts!!
• Truth as they believe it
• Saying what you want to hear
• Write questions with quantifiable responses
• Consider eliciting limited qualitative info
• Pilot-test the questionnaire
• Restrict each question to single idea
• Save controversial until the end
• Seek clarifying information for vague responses
• Facilitate both evaluation and quantification
• List of characteristics or behaviors
• Check items that are observed or present
• Rating scale (Likert scale)
• Evaluate attitude or behavior on a continuum
• inadequate to excellent
• never to always
• strongly disapprove to strongly approve
• Mixed opinions about neutral responses
• Keep it short
• Keep it simple
• Precise, unambiguous language
• Check your assumptions
• how many cigarettes do you smoke each day?
• Does this assume the person is a smoker?
• Does this assume the person smokes the same amount every day?
• Do not give clues to desired response
• Check for consistency
• Determine in advance how to code responses
• Should look attractive and professional
• Every question should address research problem
• Many situations where not possible to study the entire population
• Subset of population can be used to make generalizations
• Must be representative of total population
• See all characteristics of total population in same relationship
• Microcosm of total population
• Specify in advance that each segment of population will be represented
• Random selection
• Is a random number generator truly random?
• What is a seed? What good is it?
• Are the following strings random?
• 1-5-2-6-3-7-4
• 3-8-6-6-1-9-2
• 4-5-8-3-7-9-1
• Simple random sampling
• Stratified random sampling
• Proportional stratified sampling
• Cluster sampling
• Systematic sampling
• Layers (strata) of distinctly different types
• Sample equally from each strata
• Guarantee equal representation of each strata
• Appropriate when strata equal in size
• Proportions of each strata are different
• Random sample from each strata
• Proportion from each strata same as total population
• Subdivide total population into clusters
• Clusters must be equally heterogeneous
• Choose random subset of clusters
• Select individuals according to predetermined sequence
• Originate by chance
• ex. every 10 units
• Convenience sampling
• No identification of representative subsets
• Quota sampling
• Same proportions as total population
• Purposive sampling
• ex. typical representatives of group
• ex. representatives with diverse perspectives
• The larger the sample, the better
• lt 100 survey entire population
• 500 survey 50 of population
• 1,500 survey 20 of population
• gt 5,000 survey 400
• Larger sample may be necessary for heterogeneous population
• Influence disturbing randomness of sample population
• Sample selection
• Response rate

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Quantitative Research Design

A comprehensive discussion on the different designs used in conducting quantitative researches in various fields of discipline. Read less

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• 1. JEFFREY A. LUCEROMPMG, MAEd, MAN, RN, CSE, SHNC, FRIN, FRIEDr, FIIER Healthcare Provider, Educator, Research Generalist RESEARCH DESIGN
• 2. What is research design? What is its significance in writing a research paper? ©JALucero, MPMG, MAEd, MAN, RN, CSE, SHNC, FRIN, FRIEdr, FIIER
• 3. During the session, the participants are expected to: 1. Familiarize with the definition, purpose and nature of different research designs 2. Identify the different research designs in quantitative method; and 3. Select what research design can be applicable for a given research problem. ©JALucero, MPMG, MAEd, MAN, RN, CSE, SHNC, FRIN, FRIEdr, FIIER
• 4. Research Design Definition “Research design is a master plan specifying the methods and procedures for collection and analyzing the needed information” -William Zikmund “ Research design is the plan, structure and strategy of investigation conceived so as to obtain answers to research questions and to control variance” -Kerlinger Methods Questions ©JALucero, MPMG, MAEd, MAN, RN, CSE, SHNC, FRIN, FRIEdr, FIIER
• 5. Identifying the appropriate method of gathering information Ideal to solve the problem identified ©JALucero, MPMG, MAEd, MAN, RN, CSE, SHNC, FRIN, FRIEdr, FIIER
• 6. Approaches in Research Qualitative Mixed Quantitative ©JALucero, MPMG, MAEd, MAN, RN, CSE, SHNC, FRIN, FRIEdr, FIIER
• 7. Significant relationship among variables through the use of numbers Meaning of responses, verbal or non-verbal ©JALucero, MPMG, MAEd, MAN, RN, CSE, SHNC, FRIN, FRIEdr, FIIER
• 8. Quantitative Research Designs •Describes •Relates variables Non- Experimental •Cause and effect Experimental ©JALucero, MPMG, MAEd, MAN, RN, CSE, SHNC, FRIN, FRIEdr, FIIER
• 9. Quantitative Research Designs Non-Experimental Descriptive Research Design Longitudinal Research Design Correlational Research Design Experimental True Experimental Design Quasi-Experimental Design ©JALucero, MPMG, MAEd, MAN, RN, CSE, SHNC, FRIN, FRIEdr, FIIER
• 10. Non-Experimental Research Design (1) • Descriptive Research Design • A design used to describe a certain condition or phenomenon in a given sample using quantifiable descriptors. • It involves the use frequency count, percentage, Likert scale, mean, and standard deviation in answering the research questions. • Example: • A teacher wants to determine the number of her students, grouped according to their sex, who are still non-readers. • A teacher wants to determine the general academic performance of her students in mathematics. ©JALucero, MPMG, MAEd, MAN, RN, CSE, SHNC, FRIN, FRIEdr, FIIER
• 11. Non-Experimental Research Design (2) •Longitudinal Research Design • A design used to describe a certain condition or phenomenon in a given population using quantifiable descriptors. • It involves the use frequency count, percentage, Likert scale, mean, and standard deviation in answering the research questions. • It is the prolonged process of a descriptive research. ©JALucero, MPMG, MAEd, MAN, RN, CSE, SHNC, FRIN, FRIEdr, FIIER
• 12. Non-Experimental Research Design (3) •Correlational Research Design •A design used to seek significant relationship between identified variables. • Example: • A teacher wants to find out if sex can be a predictor of performance in English in his class. • A teacher wants to determine if belonging to a broken family has a relationship to the students’ attitude towards attending classes. ©JALucero, MPMG, MAEd, MAN, RN, CSE, SHNC, FRIN, FRIEdr, FIIER
• 13. Experimental Research Design (1) •True Experimental •Characteristics: •Randomization •Manipulation •Control The absence of ONE characteristic then makes the research quasi-experimental. ©JALucero, MPMG, MAEd, MAN, RN, CSE, SHNC, FRIN, FRIEdr, FIIER
• 14. Experimental Research Design (2) •Quasi-Experimental •Pre-experimental •Time series design ©JALucero, MPMG, MAEd, MAN, RN, CSE, SHNC, FRIN, FRIEdr, FIIER
• 15. ©JALucero, MPMG, MAEd, MAN, RN, CSE, SHNC, FRIN, FRIEdr, FIIER
• 16. ©JALucero, MPMG, MAEd, MAN, RN, CSE, SHNC, FRIN, FRIEdr, FIIER
• 17. Definitions • Experimental Treatments • Alternative manipulations/intervention of the independent variable being investigated • Experimental Group • Group of subjects exposed to the experimental treatment • Control Group • Group of subjects exposed to the control condition • Not exposed to the experimental treatment ©JALucero, MPMG, MAEd, MAN, RN, CSE, SHNC, FRIN, FRIEdr, FIIER
• 18. •Extraneous Variables •Variables other than the manipulated variables that affect the results of the experiment •Can potentially invalidate the results ©JALucero, MPMG, MAEd, MAN, RN, CSE, SHNC, FRIN, FRIEdr, FIIER
• 19. Symbolism for Diagramming Experimental Designs X = exposure of a group to an experimental treatment O = observation or measurement of the dependent variable If multiple observations or measurements are taken, subscripts indicate temporal order – I.e., O1, O2, etc. = random assignment of test units; individuals selected as subjects for the experiment are randomly assigned to the experimental groups R
• 20. Pre-Experimental Designs • Do not adequately control for the problems associated with loss of external or internal validity • Cannot be classified as true experiments • Often used in exploratory research • Three Examples of Pre-Experimental Designs • One-Shot Design • One-Group Pretest-Posttest Design • Static Group Design ©JALucero, MPMG, MAEd, MAN, RN, CSE, SHNC, FRIN, FRIEdr, FIIER
• 21. 1. One-Shot Design • A.K.A. – after-only design • A single measure is recorded after the treatment is administered • Study lacks any comparison or control of extraneous influences • No measure of test units not exposed to the experimental treatment • May be the only viable choice in taste tests • Diagrammed as: X O1 ©JALucero, MPMG, MAEd, MAN, RN, CSE, SHNC, FRIN, FRIEdr, FIIER
• 22. SAMPLE PROBLEM One-Shot Design A group of students were given a lecture about sentence patterns in English and then were given a some exercises about it. Diagrammed as: X O1 ©JALucero, MPMG, MAEd, MAN, RN, CSE, SHNC, FRIN, FRIEdr, FIIER
• 23. 2. One-Group Pretest-Posttest Design • Subjects in the experimental group are measured before and after the treatment is administered. • No control group • Offers comparison of the same individuals before and after the treatment (e.g., training) • If time between 1st & 2nd measurements is extended, may suffer maturation • Can also suffer from history, mortality, and testing effects • Diagrammed as O1 X O2 ©JALucero, MPMG, MAEd, MAN, RN, CSE, SHNC, FRIN, FRIEdr, FIIER
• 24. SAMPLE PROBLEM One-Group Pretest-Posttest Design Students in a homogenous section were given a diagnostic test in Mathematics. Then, they designed a software to improve learning outcomes in this subject. Afterwards, they were given an achievement test to show how technology can be successfully implemented in schools. • Diagrammed as O1 X O2 ©JALucero, MPMG, MAEd, MAN, RN, CSE, SHNC, FRIN, FRIEdr, FIIER
• 25. 3. Static Group Design •A.K.A., after-only design with control group •Experimental group is measured after being exposed to the experimental treatment •Control group is measured without having been exposed to the experimental treatment •No pre-measure is taken •Major weakness is lack of assurance that the groups were equal on variables of interest prior to the treatment •Diagrammed as: Experimental Group X O1 Control Group O2 ©JALucero, MPMG, MAEd, MAN, RN, CSE, SHNC, FRIN, FRIEdr, FIIER
• 26. SAMPLE PROBLEM Static Group Design A teacher is handling two groups of non-readers. In order to find out the effectiveness of her intervention, she applied it to one group while applying a traditional approach to the other. • Diagrammed as: • Experimental Group X O1 Control Group O2 ©JALucero, MPMG, MAEd, MAN, RN, CSE, SHNC, FRIN, FRIEdr, FIIER
• 27. True-Experimental Designs • It can establish cause and effect relationships • Supports or refutes a hypothesis using statistical analysis • There are three criteria that must be met in a true experiment Control group and experimental group Researcher-manipulated variable Random assignment • Three Examples of True-Experimental Designs • Pretest-Posttest Control Group Design • Posttest-Only Control Group Design • Solomon Four-Group Design ©JALucero, MPMG, MAEd, MAN, RN, CSE, SHNC, FRIN, FRIEdr, FIIER
• 28. 1. Pretest-Posttest Control Group Design • A.K.A., Before-After with Control • True experimental design • Experimental group tested before and after treatment exposure • Control group tested at same two times without exposure to experimental treatment • Includes random assignment to groups • Effect of all extraneous variables assumed to be the same on both groups • Do run the risk of a testing effect ©JALucero, MPMG, MAEd, MAN, RN, CSE, SHNC, FRIN, FRIEdr, FIIER
• 29. Pretest-Posttest Control Group Design • Diagrammed as • Experimental Group: O1 X O2 • Control Group: O3 O4 • Effect of the experimental treatment equals (O2 – O1) -- (O4 – O3) R R ©JALucero, MPMG, MAEd, MAN, RN, CSE, SHNC, FRIN, FRIEdr, FIIER
• 30. SAMPLE PROBLEM Pretest-Posttest Control Group Design Researchers want to monitor the effect of a new teaching method upon two groups of children, both with pretest and posttest. Only the second group has the treatment. Other areas include evaluating the effects of counseling, testing medical treatments, and measuring psychological constructs. The only stipulation is that the subjects must be randomly assigned to groups, in a true experimental design. • Diagrammed as • Experimental Group: O1 X O2 • Control Group: O3 O4 ©JALucero, MPMG, MAEd, MAN, RN, CSE, SHNC, FRIN, FRIEdr, FIIER
• 31. 2. Posttest-Only Control Group Design • A.K.A., After-Only with Control • True experimental design • Experimental group tested after treatment exposure • Control group tested at same time without exposure to experimental treatment • Includes random assignment to groups • Effect of all extraneous variables assumed to be the same on both groups • Do not run the risk of a testing effect • Use in situations when cannot pretest ©JALucero, MPMG, MAEd, MAN, RN, CSE, SHNC, FRIN, FRIEdr, FIIER
• 32. Posttest-Only Control Group Design •Diagrammed as • Experimental Group: X O1 • Control Group: O2 •Effect of the experimental treatment equals (O2 – O1) R R ©JALucero, MPMG, MAEd, MAN, RN, CSE, SHNC, FRIN, FRIEdr, FIIER
• 33. SAMPLE PROBLEM Posttest-Only Control Design A teacher is handling two groups of non- readers. In order to find out the effectiveness of her intervention, she applied it to one group while applying a traditional approach to the other. •Diagrammed as: • Experimental Group: X O1 • Control Group: O2 R R ©JALucero, MPMG, MAEd, MAN, RN, CSE, SHNC, FRIN, FRIEdr, FIIER
• 34. 3. Solomon Four-Group Design • True experimental design • Combines pretest-posttest with control group design and the posttest-only with control group design • Provides means for controlling the interactive testing effect and other sources of extraneous variation • Does include random assignment ©JALucero, MPMG, MAEd, MAN, RN, CSE, SHNC, FRIN, FRIEdr, FIIER
• 35. Solomon Four-Group Design • Diagrammed as • Experimental Group 1: O1 X O2 • Control Group 1: O3 O4 • Experimental Group 2: X O5 • Control Group 2: O6 • Effect of independent variable (O2 – O4) & (O5 – O6) • Effect of pretesting (O4 – O6) • Effect of pretesting & measuring (O2 – O5) • Effect of random assignment (O1 – O3) R R R R ©JALucero, MPMG, MAEd, MAN, RN, CSE, SHNC, FRIN, FRIEdr, FIIER
• 36. SAMPLE PROBLEM Solomon Four Group Design A researcher would like to find out the effect of reading intervention in the student’s English academic grade. All groups undergo randomization. First group, students with intervention, pretest & posttest. Second group, students with pretest & posttest only. Third group, students with intervention & posttest. Fourth group, students were given posttest only. • Diagrammed as • Experimental Group 1: O1 X O2 • Control Group 1: O3 O4 • Experimental Group 2: X O5 • Control Group 2: O6 ©JALucero, MPMG, MAEd, MAN, RN, CSE, SHNC, FRIN, FRIEdr, FIIER
• 37. • Involves periodic measurements on the dependent variable for a group of test units (one group only) • After multiple measurements, experimental treatment is administered (or occurs naturally) • After the treatment, periodic measurements are continued in order to determine the treatment effect • Diagrammed as: O1 O2 O3 O4 X O5 O6 O7 O8 Time Series Designs ©JALucero, MPMG, MAEd, MAN, RN, CSE, SHNC, FRIN, FRIEdr, FIIER
• 38. Time Series Design To examine the effect of using an online classroom on the academic performance of students in biology, The students’ performance is measured once before the program, and then 3 weeks after the program, and at the end of one quarter following program implementation. The outcomes at different time points are compared to assess the program effect. SAMPLE PROBLEM Diagrammed as: O1 O2 O3 O4 X O5 O6 O7 O8 ©JALucero, MPMG, MAEd, MAN, RN, CSE, SHNC, FRIN, FRIEdr, FIIER
• 39. • A series of periodic measurements is taken from two groups of test units (an experimental group and a control). • The experimental group is exposed to a treatment and then another series of periodic measurements is taken from both groups. Diagrammed as: O1 O2 O3 O4 O5 O6 O7 O8 O1 O2 O3 O4 X O5 O6 O7 O8 Multiple Time Series Design ©JALucero, MPMG, MAEd, MAN, RN, CSE, SHNC, FRIN, FRIEdr, FIIER
• 40. Multiple Time Series Design Suppose that a weight loss study used different follow-up procedures for experimental and control group participants. The researchers assess weight data after one year by telephoning control group participants, but they have the intervention participants come in to the clinic to be weighed. Then the weight differences between the groups could be due to differing assessment procedures, rather than to the intervention. SAMPLE PROBLEM Diagrammed as: O1 O2 O3 O4 O5 O6 O7 O8 O1 O2 O3 O4 X O5 O6 O7 O8
• 41. ©JALucero, MPMG, MAEd, MAN, RN, CSE, SHNC, FRIN, FRIEdr, FIIER
• 42. THANK YOU FOR LISTENING!