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QUANTITATIVE RESEARCH METHODS AND DESIGN

AN OVERVIEW OF THE QUANTITIATIVE RESEARCH METHODS. EXPERIMENTAL RESEARCH. SINGE SUBJECT RESEARCH. CORRELATIONAL RESEARCH. CAUSAL COMPARATIVE RESEARCH. DESCRIPTIVE RESEARCH. RESEARCH TOOL AND OBJECTIVES

Related Papers

Regina Oliver , Joseph Wehby

Disruptive behavior in schools has been a source of concern for school systems for several years. Indeed, the single most common request for assistance from teachers is related to behavior and classroom management (Rose & Gallup, 2005). Classrooms with frequent disruptive behaviors have less academic engaged time, and the students in disruptive classrooms tend to have lower grades and do poorer on standardized tests (Shinn, Ramsey, Walker, Stieber, & O’Neill, 1987). Furthermore, attempts to control disruptive behaviors cost considerable teacher time at the expense of academic instruction. Effective classroom management focuses on preventive rather than reactive procedures and establishes a positive classroom environment in which the teacher focuses on students who behave appropriately (Lewis & Sugai, 1999). Rules and routines are powerful preventative components to classroom organization and management plans because they establish the behavioral context of the classroom by specifying what is expected, what will be reinforced, and what will be retaught if inappropriate behavior occurs (Colvin, Kame’enui, & Sugai, 1993). This prevents problem behavior by giving students specific, appropriate behaviors to engage in. Monitoring student behavior allows the teacher to acknowledge students who are engaging in appropriate behavior and prevent misbehavior from escalating (Colvin et al., 1993). Research on classroom management has typically focused on the identification of individual practices that have some level of evidence to support their adoption within classrooms. These practices are then combined under the assumption that, if individual practices are effective, combining these practices into a package will be equally, if not more, effective. Textbooks are written and policies and guidelines are disseminated to school personnel based on these assumptions. Without research that examines classroom management as an efficient package of effective practices, a significant gap in our current knowledge base still exists. Understanding the components that make up the most effective and efficient classroom management system as well as identifying the effects teachers and administrators can expect from implementing effective classroom management strategies represent some of these gaps. A meta-analysis of classroom management which identifies more and less effective approaches to universal, whole-class, classroom management as a set of practices is needed to provide the field with clear research-based standards. This review examines the effects of teachers’ universal classroom management practices in reducing disruptive, aggressive, and inappropriate behaviors. The specific research questions addressed are: Do teacher’s universal classroom management practices reduce problem behavior in classrooms with students in kindergarten through 12th grade? What components make up the most effective and efficient classroom management programs? Do differences in effectiveness exist between grade levels? Do differences in classroom management components exist between grade levels? Does treatment fidelity affect the outcomes observed? These questions were addressed through a systematic review of the classroom management literature and a meta-analysis of the effects of classroom management on disruptive or aggressive student behavior. Twelve studies of universal classroom management programs were included in the review. The classroom-level mean effect size for the 12 programs was positive and statistically significant (d=.80 with an ICC=.05; d=.71 with an ICC=.10; p<.05). Note that cluster adjustments were required due to differences in reporting measures between classroom level outcomes and individual student level outcomes. The resulting effect sizes index classroom-level differences and cannot be compared to the typical student-level effect sizes commonly reported in the literature. Due to a lack of power to detect heterogeneity and lack of information reported in the studies reviewed, only the first research question could be addressed. Teacher’s classroom management practices have a significant, positive effect on decreasing problem behavior in the classroom. Students in the treatment classrooms in all 12 studies located for the review showed less disruptive, inappropriate, and aggressive behavior in the classroom compared to untreated students in the control classrooms. The overall mean classroom effect size of either .71 or .80 indicates a positive effect that significantly impacts the classroom environment. To put our classroom-level mean effect sizes into a comparable format with the more typical effect sizes, we back-transformed our mean effect sizes using the original adjustment formulas (Hedges, 2007). Thus, the classroom-level mean effect sizes of .80 and .71 are roughly comparable to student level effect sizes of .18 and .22 for ICC=.05 and ICC=.10, respectively. Teachers who use effective classroom management can expect to experience improvements in student behavior and improvements that establish the context for effective instructional practices to occur.

Michele Terry Thompson , Gordon S. Gibb , Darlene Anderson

Research suggests a compelling correlation between teacher behavior and effective learning environments. Focusing on the evidence-based teaching skill of offering behavior-specific praise (BSP), the researchers worked with three elementary-level general educators in a tiered model of training generally known as response to intervention (RtI). Although RtI commonly provides targeted instructional support to students, this study used the RtI framework to provide professional development instruction to teachers. The researchers also tracked the behavior of three students identified by the teachers as having behavioral difficulties, who became the focus of each teacher’s BSP. Results showed increases in rates of BSP following the Tier 2 and Tier 3 interventions (video self-monitoring and peer coaching), but not following the Tier 1 intervention (school-wide in-service training). Averagesfor all three students’ on-task behavior increased with increased teacher BSP.

Journal of Education for Students Placed at Risk (JESPAR)

Raymond Burke

Michelle Parker-Tomlin

Abigail Camus

Paul Caldarella , Krystine Jolstead , Leslie Williams

Challenging behavior in preschool is a serious concern for teachers. Positive behavior interventions and supports (PBIS) has been shown to be effective in reducing such behaviors. Class-Wide Function-Related Intervention Teams (CW-FIT) is a specific multi-tiered intervention for implementing effective classroom management strategies using PBIS practices. CW-FIT has been shown to be effective in elementary classrooms but has not yet been evaluated with younger age groups. CW-FIT Tier 1 is a group contingency utilizing social skills training, teacher praise, and positive reinforcement to improve student behavior. The present study examined the effects of CW-FIT Tier 1 implementation on student group on-task behavior and on teacher praise and reprimand rates in four preschool classrooms. A single-subject delayed multiple-baseline design with embedded reversals was used to evaluate impact. Results indicated the intervention increased student group on-task behavior and teacher praise to reprimand ratios. Both teachers and children found CW-FIT Tier 1 to be socially valid. Limitations and implications of this study for researchers and practitioners are discussed.

amira lamira

Paul Caldarella , Leslie Williams

Comprehensive evidence-based interventions are needed to help early childhood educators manage challenging student behaviors. One such intervention, class-wide function-related intervention teams (CW-FIT), is a multi-tiered behavioral intervention program based on positive behavior support principles, including four main elements: (a) teaching socially appropriate communication skills, (b) using differential reinforcement with an interdependent group contingency, (c) extinguishing or eliminating potential reinforcement for problem behavior, and (d) implementing individual interventions using self-management, help cards, and/or functional assessment. This study implemented CW-FIT with five kindergarten through second grade teachers. Results demonstrated efficacy of the program as early childhood teachers increased their praise rates and decreased their reprimand rates, and students responded with increased on-task behavior and fewer classroom disruptions. Study implications and limitations are addressed.

IOSR Journal of Nursing and Health Science

Hassanat R A M A D A N Abdel-Aziz

Background: Poor sleep is reported in 50% of older adults, calling for an important need for identifying safe and effective alternatives to improve their sleep quality. Aim of the study: The present study aimed to evaluate the effect of cognitive behavioral (CB) intervention on improving sleep quality in older adults. Design: A quasi-experimental pretest-and-posttest design was utilized to conduct this study. Setting: The study was conducted in the geriatric social club in Zagazig City. Sample: A purposive sample of 75 elderly subjects who fulfilled the study inclusion criteria. Tools: Three tools were used in the present study; the first tool was a structured interview questionnaire consisted of two parts, the second tool was The Sleep Hygiene Awareness and Practice Scale, and the third tool was The Pittsburgh Sleep Quality Index (PSQI). Results: The results revealed post-intervention statistically significant improvements in PSQI components scores, PSQI global score, sleep hygiene knowledge, caffeine knowledge, sleep hygiene practice, and other sleep parameters as sleep latency and sleep efficiency. Conclusion: CB intervention is effective in improving elderly's sleep quality, and can be considered a safe and effective non-pharmacological approach to improve elderly's sleep quality. Recommendations: The developed CB intervention should be implemented in the study setting on a long term basis to test its sustainability, and in similar settings to confirm its effectiveness and for further improvements.

Journal of Early and Intensive Behavioral Intervention,

Tracy L Morris

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Introduction to Quantitative Research - PowerPoint PPT Presentation

Introduction to Quantitative Research

Quantitative research is explaining phenomena by collecting numerical data that are analyzed using mathematically based methods ... survey research to ... – powerpoint ppt presentation.

• Elin Driana, Ph.D
• a systematic attempt to provide answers to questions (Tuckman, 1999, p. 4)
• the more formal, systematic, and intensive process of carrying on a scientific method of analysis (Best and Kahn, 1986, p. 18).
• Quantitative research is Explaining phenomena by collecting numerical
• data that are analyzed using mathematically based methods (in
• particular statistics).
• (Aliaga and Gunderson (2002) in Muijs (2004))
• EXPERIMENTAL RESEARCH
• Type of research that allows the researcher to establish a causal relationship between independent and dependent variables by controlling extraneous variables.
• Correlational Research
• to determine relationship among two or more variables.
• Causal-Comparative Research
• to determine the cause for or the consequences of differences between groups of people
• Survey Research
• to describe characteristics of a group by means of such instruments as interview questions, questionnaires, and tests.
• Selecting a Problem
• Reviewing the literature
• Formulating Research Question
• Constructing Hypotheses
• 5. Identifying and Labeling Variables
• 6. Constructing Operational definitions of Variables
• 7. Constructing a Research Design
• 8. Identifying population and sample
• 9. Designing instruments
• 10. Selecting statistical test for testing the hypothesis
• 11. Collecting Data
• 12. Analyzing Data
• 13. Writing the Report
• The entire elements of interest in a particular study
• A subset of the population
• Numerical measures of a population
• Numerical measures of a sample
• A characteristic that varies
• Independent Variable
• the factor that is measured, manipulated, or selected by the experimenter to determine its relationship with to an observed phenomenon (Tuckman, 1999, p.93)
• 2. Dependent Variable
• a response variable or output (Tuckman, 1999, p. 93)
• 3. Moderator Variable
• a special type of independent variable, a secondary independent variable selected to determine if it affects the relationship between the studys primary independent variable and it dependent variable (Tuckman, 1999, p. 97).
• 4. Control Variables
• Factors controlled by the experimenter to cancel out or neutralized any effect they might otherwise have on observed phenomen(Tuckman, 1999, p. 100)
• 5. Intervening Variable
• a factor that theoretically affects observed phenomena but cannot be seen, measured, or manipulated its effect must be inferred from the effects of the independent and moderator variable on the observed phenomenon (Tuckman, 1999, p. 101)
• Different values that are associated with variable.
• Qualitative or Categorical Data
• are classified in categories and not numerically measured.
• B. Quantitative or Numerical Data
• are obtained either from a counting process (discrete data) or from a measuring process (continuous data)
• A scale is a tool or mechanism to distinguish individuals on the variables of interest to our study (Sekaran, 1992).
• There are four types of scales.
• 1. Nominal Scale
• is not intended to measure a variable, but to assign data into certain categories or groups.
• does not imply order or rank
• Example gender
• 2. Ordinal Scale
• Contains the properties of nominal scale
• rank the categories in some meaningful way.
• Example level of education.
• 3. Interval Scale
• contains the properties of a nominal and an ordinal scale
• assumes the same interval among the points of scale
• Example attitude scale.
• D. Ratio Scale
• contains the properties of nominal, ordinal, and interval scales.
• the ratio between two values is meaningful
• has an absolut zero point.
• Example Salary

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Quantitative Research

Mar 12, 2019

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Quantitative Research. Experimental. Experimental Research. Cause and effect relationships are established by manipulating the INDEPENDENT variable(s) and observing the effect on the DEPENDENT variable.

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• dependent variable
• independent variable
• internal validity
• internal external validity
• independent variable exercise regimen

Presentation Transcript

Quantitative Research Experimental

Experimental Research • Cause and effect relationships are established by manipulating the INDEPENDENT variable(s) and observing the effect on the DEPENDENTvariable. • Research design must control for the possible effects of extraneous variables that could mask, enhance, or in some way alter the effect of the independent variable on the dependent variable.

Example: General study description: Recruited obese participants will spend 3 weeks in a tightly controlled laboratory setting Dependent Variable: Weight Loss Independent variable: food intake Independent variable: exercise regimen

Internal & External Validity • Internal Validity: determined by the degree to which the observed effects of the independent variable (IV) are REAL and not caused by extraneous factors • Alternative explanations for the effect of the independent variable (IV) on the dependent variable (DV) threaten internal validity • KEY: controlling for the possible effects of extraneous variables

Internal & External Validity • External Validity: determined by the ability to generalize the study results beyond the study sample

Threats to Internal Validity alternate explanations • History • Maturation(children) • Testing • Instrumentation • Selection bias • Mortality/attrition • Hawthorne • Placebo • blind vs. double blind • Implementation • fidelity

Control StrategiesThreats to Internal Validity • Randomly select participants from a well-defined study population • Randomly assign selected participants to groups • Include non-treatment control groups in the research design

Final Point on Int/Ext Validity • External validity can not exist without internal validity • If the results of the study are not internally valid, there is nothing to generalize. • Researchers should be always be concerned about ensuring internal validity first.

Choosing a Design • Identify and use a design that… • Controls as many extraneous variable as possible • Will still be practical and feasible to implement

Experimental Designs • X =independent variable (the treatment) • X2 or Y = additional treatments • O = measurement of the dependent variable (an observation) • Each observation or measurement is numbered indicating order (O1, O2, O3 ) • R = random assignment • Hawthorne effect

Examples of Types of Randomization (Jacobsen, 2012, figure 13-6)

Non-experimental Designs • Survey research designs • Cross –sectional • Longitudinal • Trend studies –track population changes over time • Youth Risk Behavior Survey (YRBS) http://www.cdc.gov/HealthyYouth/yrbs/pdf/us_injury_trend_yrbs.pdf • Cohort study – follow a particular group or subgroup over time • National Longitudinal Study of Adolescent Health (Add Health) http://www.cpc.unc.edu/projects/addhealth/design • Panel study – examine the same group of people over time at the individual level • Panel Study of American Religion and Ethnicity (PS-ARE) http://www.ps-are.org/index.asp

Framework for a Cohort Study (Jacobsen, 2012, figure 12-2)

Non-experimental Designs • Correlational study • Identifies relationships and the degree or closeness of those relationships • A correlation exits if, when one variable increases another variable either increases or decreases in a somewhat predictable way. • What is the relationship between participation in intramural sports and BMI among WOU students? • What is the relationship between religiosity and age of sexual initiation in seventh grade students?

Types of Relationships • Linear relationships • Positive: both variables move in the same direction (one variable increases as the other increases) • Negative: one variable moves in the opposite direction of the other (one variable increases while the other decreases) • Curvilinear relationships

Assessing correlation • Rough measure = scatter plot • Statistic = correlation coefficient or r (describes a sample of paired values from two different variables) • Measures the closeness with which the pairs of values fit a straight line • Range of values for r = +1.0 to -1.0 • When r = 0, there is no correlation • 1.0 = perfect correlation

Interpreting a Scatter Plot • Line of best fit • http://staff.argyll.epsb.ca/jreed/math9/strand4/scatterPlot.htm

Relationships cause & effect • Correlation of ice cream sales and death by drowning (r = +.86) • In the months when ice cream sales go up, so do deaths by drowning and likewise when ice cream sales go down, so do deaths by drowning • A.) Does ice cream consumption cause drowning deaths to increase? or B.) Do drowning deaths cause surviving family members and friends to eat more ice cream?

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