No Child Left Behind: Mixed Methods Research Essay

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¶ … Strengths/Weaknesses

Mixed Methods

Research for NCLB

Study Definition and Issues

Current Trends/Problem Correction

Use of Research on AYP Ratings

Case Studies and Examples

Adaptive Approaches to Research

Hourglass Approach to Research

Steps to the Scientific Method

Diagram of Mixed Research

NAEP Score Equivalents

NCLB Issues

Comparison/Contrast Research Methods

Strengths & Weaknesses of Methodologies

In all aspects of science, whether it be the so-called "hard" sciences like chemistry and physics, or the "soft" sciences like marketing, educational psychology, and sociology, research methodology is critical to the veracity of the data interpretation and the ability to have a shared standard that translates languages, cultures, and chronology. Typically, there has been a gulf between these methods, not just in actual tactical methods, but in the approach to hypothesis testing, data collection, definition of program sets, and even reporting of data. The two sides, quantitative and qualitative, grew ever further apart in the 1950s and 1960s, until the scope and complexity of the data, along with more synergism between disciplines, caused a realization that, in fact, a merger of the two methods into a "mixed methods" category, likely served all sides in a more encompassing manner.

Given the thrust in tracking that the No Child Left Behind initiative requires, it is not surprising that there remain quantifiers and qualifiers who criticize each others' data and interpretations. Simply because of the cultural complexity of the data surrounding children's academic performance, combined with educator's rankings and abilities, means the subject is rife for the mixed approach.

This paper provides an overview of quantitative and qualitative methods, compares and contrasts the two methods; reviews literature on the subject, and provides a case study that uses a mixed methods approach to an educational problem in a unique manner.

Chapter 1 -- Definition

1.1 Overview of Research Methods -- the general purpose of research is to advance knowledge in a particular area or areas. However, in the academic sense, research means more than simply collecting facts, it must be a systematic and robust approach to not only data collection, but data analysis and interpretation. For this reason, scholars typically adhere to a set of empirical standards in research, whether that is the scientific study of a particular data set, or the historical method of research.

Typically, scientific research, regardless of the discipline, utilizes the hypothesis-testing rubric. A hypothesis is used to make a prediction (academic guess) or set of predictions about certain data and/or the outcome of an experiment. The methodology follows a rigorous model, and the hypothesis is tested -- then verified or rejected, likely bringing up numerous other sub-hypotheses to test. The process, though, of scientific research is to utilize observation, data analysis, and allow new hypotheses to replace outmoded ideas (Bordens and Abbott, 2007).

The historical method utilizes evidence to research and interpret history. This is problematical, of course, because the very nature of historical evidence is circumspect -- and the inclusion/exclusion, or even availability of certain documents changes the outcome of the conclusions. For historians, then the guidelines are a strong criticism of the data, the source, and the text; bias, evidence, authorship, integrity, and attribution all play a part in this method of inquiry (Creswell, 2008).

Within the paradigm of research there are three major types, and then several subgroups within those types. Quantitative research is the systematic scientific investigation of the interrelationship between events and actions that use mathematical models and theories to connect measurement of data with empirical observation. Qualitative research aims to understand behavior, attitudes, how and why decisions are made with less approach to numerical measurement of data. Mixed method research is, as might be expected, a natural synthesis of both qualitative and quantitative methods and utilizes the appropriate combination of data collection and analysis for the specific hypothesis or data set (Graziano and Raulin, 2006).

Within these methods, research takes three main forms, although the gray area between is variable; in addition, each of those three forms may utilize two types of research:

Figure 1.1 -- Adaptive Approaches to Research

Primary research is, by definition; source docs or material collected at the observable site (fieldwork) and is typically quantitative, although source docs may be actual letters or historical documents. Secondary research is collecting and summarizing existing research on a topic, perhaps forming new conclusions, but not collecting the actual data. In order, however, to maintain standards of research, expectations are made primarily about the vetting of data -- its source, validity, duplication under other observation, etc. This, plus a rather basic understanding of taking a similar structure -- regardless of the project -- from general, organizing to more specific, to the analysis of the data, reaching of conclusions, and then again, a generalization back to the original questions and appropriate measures for further research (Booth, et.al., 2008).

Figure 1.2 -- Hourglass Approach to Research (Trochim, 2006).

1.2 Qualitative Research -- Qualitative research has been traditionally used in the so-called "soft sciences": sociology, anthropology, history, education, business, psychology, and especially market research, etc., but is not necessarily confined to those disciplines. Indeed, since the 1960s most fields have opted for a swing to the other side of the paradigm and performing more quantitative research. Qualitative research is often exploratory, and is often used to evaluate trends, thoughts, opinions, and, in a more organized manner, establish outcomes, unintended effects and impacts on broad research questions (Hara, 1995).

1.3 Quantitative Research - Quantitative research has a more traditional history and typically is comprised of the use of the scientific method, an accepted six-step process that allows researchers to duplicate experiments, provide stability of veracity regardless of discipline, and a global understanding of methodology (Ibid):

Figure 1.3 Steps to the Scientific Method

1.4 Comparison/Contrast - Differences between the two forms of research are clearly not absolute, but can be generalized as the difference between collecting, analyzing, and interpreting data by observing what people do and say (qualitative) as opposed to measurements, concepts, meanings, definitions, and numerical data:

Table 1.1 -- Comparison of Quantitative and Qualitative Methods

Quantitative

Qualitative

Issue

Objective

Subjective

In general, what is actually observable and quantifiable?

Research Questions; How many? What are they? How do they relate?

Research Questions; What? Why?

Comparative data, use of what data means to individual.

"Hard" Science

"Soft" Science

Interpretation of mathematical modeling

Test hypotheses

Develops hypotheses

Part of process

Experiment is a one reality; concise and narrow

Focus is broad, complex, interrelated, and inseparable

The whole universe vs. A tiny portion

Facts are value free and unbiased

Facts are value laden and biased

Interpretation of data key

Reduction, control, and prevision

Discovery, Description, understanding, and sharing of interpretations

General purpose of data set, asks so what?

Measurable

Interpretive

Depends on the interpreter, but tiny version always expandable to large version?

Mechanistic: parts equal the whole

Organismic: whole is greater than the parts

Again, approach and pre-bias

Report statistical data

Report narratives, individual interpretation

Are numbers or words/ideas more valid in certain situations?

Researcher is separate

Researcher is part of process

Position within data structure

Context Free

Context Dependent

What do we hope to glean?

Generalizations lead to specifics -- prediction, explanation and understanding

Patterns lead to generalized understandings

What role does result play in overall issue?

Highly controlled, experimental setting -- outcome oriented

Flexible, natural setting, and process oriented

Interpretation of data sets

Sample size vital

Samples size not so important, "seeks informal rich" data

Again, what is the validity vs. The assumptions?

"Counts the beans"

Provides initial data to decide "Which beans are worth counting"

Synergism specifically needed

(Source: http://uk.geocities.com/balihar_sanghera/ipsrmehrigiulqualitativequantitativeresearch.html)

Thus, one of the reasons that taking one approach over the other has become dissatisfying for the academic community is this plurality of focus and method. As even the social sciences become more robust in their need for a deeper view of data and data processes, a synergism proves necessary.

1.5 Strengths and Weaknesses -- One way to explore the differences between qualitative and quantitative research is to realize the manner in which they have been used historical, and the resultant general bias, not in data, but in results that has become an academic argument. Process of trial and evaluation to determine which methods will be retained and which discarded. While Table 1.1. outlined the basic comparisons between qualitative and quantitative research, it does not explain in detail the actual differences between the two forms of research. For generations, many generalized profiles have been used in the different types, all of which leads us to the synthesis of mixed type:

Table 1.2 Features of Qualitative and Quantitative Research; Strengths and Weaknesses Dependent Upon Data:

Quantitative

Qualitative

Strength/Weakness/Use

The aim is to classify features, count them, and construct statistical models in an attempt to explain what is observed.

The aim is a complete, detailed description.

Not all data sets fit either manner

Researcher knows clearly in advance what he/she is looking for.

Researcher may only know roughly in advance what he/she is looking for.

Some data needs discovered, some validated, some simply… [END OF PREVIEW]

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