Quantitative reasoning becomes self-evident when actively participating in
the overall learning experience. A research oriented learning experience
includes a formal and informal process of gaining, utilizing and
systematically applying knowledge to an area of interest in order to make
sense of the interrelationships between what one knows and what one learns.
With quantitative reasoning skills, one can integrate deductive logic
aspects from multiple knowledge dimensions into program evaluation and
research. There is a beginning, middle and an end to this cyclical process
which allows for the adjustment of additional information. When approaching
evaluation questions, within a particular context, it is important to keep
in mind that a scientific, linear model is but one method of organizing
information. The purpose of this home page assignment is to provide a
framework for constructing relevant quantitative interrelationships
grounded on William Trochim's Program Evaluation and Research Design course
offered through Human Service Studies (HSS), Planning and Evaluation.
Technological advances in computer use will allow you, the new research
student, to follow and apply this information to your specific research
project. This html assignment will introduce you to the world of
quantitative research methods, as well as open your eyes to the
possibilities that exist for applying similar concepts to qualitative
studies.
There is tremendous value in understanding the plural dimensions of both quantitative and qualitative approaches to evaluation methodologies. Wolcott, Guba, and Lincoln, advocate the necessity of becoming familiar with all other methods in order to appropriately select the method that best fits your area of research and design. The context, purpose, and types of research questions asked will define the methodological foundation of a study. Keeping this caveat in mind will eliminate mismatched efforts and results that can only frustrate a beginning student in research.
The quality of one's research will establish the foundation for the entire inquiry process and is based on qualitative judgments. Judgments can be applied or transferred to quantitative terms with both inductive and deductive reasoning abilities. "Quantitative reasoning...refers to a wide range of mental abilities" (Wolfe, p. 3) that facilitates deductive reasoning in a variety of settings. This article demonstrates the value of quantitative reasoning across many disciplines using learned and intuitive skills that most individuals in graduate programs already possess. However, the burden is on the researcher to justify the methodology and validity of their particular evaluation or research area of interest. As a student in both quantitative and qualitative courses in the field of HSS, I find it necessary to emphasize the importance of gaining a working knowledge of both methodologies within meaningful contexts. Throughout these collective courses, I have been provided opportunities to apply theoretical concepts to field assignments that made more sense than simply reading about experiences.
After considering the context and nature of your project, then select the appropriate method of inquiry to help direct the development of specific research questions. The objective of your inquiry is to ask questions in order to retrieve the data or information that is salient to your project. Collecting and analyzing data with quantitative strategies includes understanding the relationships among variables utilizing descriptive and inferential statistics. This process will require a serious research student to gain a fuller knowledge base by undertaking courses in statistics or regression analysis.
Briefly, descriptive statistics are theoretical postulates used to draw inferences about populationsand to estimate the parameters of those populations. Measures of central tendency and dispersion summarize the information contained in a sample and are usually provided in summary form, such as distributions, graphical and or numerical methods (Applied Regression Analysis for Business and Economics, 1996). Inferential statistics are based on descriptive statistics and assumptions that generalize to the population from a selected sample. These assumptions focus on the use of continuous data and that the sample is a random representation of the population. Inferences made at large use probabilities and probability distributions. Statistical evidence is especially important to policy makers or other stakeholders that have a vested interest in research/evaluation projects. Patton, Guba & Lincoln concur that stakeholders use extrapolated information as the basis of decision making.
Again, as a reminder, consider the context of your own research and focus on the hypothesis generated by your interests. Asking empirical questions in testable forms will involve the traditional use of the Null hypothesis versus the Alternative hypothesis. Test statistics for significance are used to determine if the null or alternative is to be accepted or rejected. The null hypothesis tests for the differences between population means. Inferential logic will establish the standards of your study based on theory and application to reality.
To effectively express quantitative concepts requires familiarity with the language. Wolfe (1993, p. 3) emphasizes the importance of understanding and participating in the entire process at all levels of cognitive reasoning. Reading, writing, and interacting with the research process will promote true learning by integrating critical thinking skills. With quantitative analysis, it is especially important to understand the units of measurement in comprehensible formats, such as visual representations. Graphs, charts, plots, and histograms adequately display raw data for a given context and chances of remembering visuals are greater than remembering numbers or text. Acquiring this working knowledge also includes skills in understanding scales and distributions. I highly recommend students enroll in the HSS Measurement and Design course for an in-depth "awakening" to the world of nominal, ordinal, interval and ratio scales.
Now, the big question is how does one arrive at the "approximation to the Truth based on conclusions from research?" (Trochim, Home Page, Knowledge Base; Validity). Truth and inquiry are a process related to logic, evidence, and argument as Trochim will elaborate on in his course presentations and Knowledge Base. Human beings interpret raw data and there are general guidelines presented to evaluate assertions and to assess validity. Quantitative strategies use normal distributions based on statistical or regression analysis. This approximation to normality (truth) is tangible evidence that assertions may be true. However, if the constructs used to establish causality are not clearly operationalized to begin with, then those inferences about relationships and variables may not be valid or reliable. It is logical to improve construct validity in order to strengthen internal and conclusion validity.
A resource to address conclusion and internal validity issues for quantitative methods is Trochim's paper on Statistical Power and Statistical Tests (1984). This article describes "four interrelated components that influence conclusions you might reach from a statistical test in a research project "(p. 1) utilizing a 2 x 2 decision matrix. The null and alternative hypotheses "describe all possible outcomes with respect to the inference" and is dependent upon the researcher determining which hypothesis "allows the maximum level of power to detect an effect if one exists" (Ibid. p.1). Types I and II errors are firmly established in probability theory that one or the other hypothesis is the incorrect conclusion arrived at in the research. Basically, a researcher wants to stat istically demonstrate that their program did have an effect in order to accept the alternative hypothesis.
Simultaneously, understanding the theoretical background within the research construct can help eliminate systematic bias early on. Post-Positivism constructs support the logic of reasoning or decision-making that parallel claims for evidence of relationships. When constructing arguments for validity, it becomes increasingly evident that one should attempt to control for or anticipate as many threats to validity as is humanly possible. Working with an experienced research advisor as a mentor or facilitator in your research area will be of tremendous value. Seek the advise of those experienced in your field of interest and invite them to join your team. There is a continuum of research perspectives available that can provide variety, clarity, or vision to your inquiry.
On this note, design construction for quantitative or qualitative research projects should be routinely considered throughout the research cycle. Good design construction (Trochim & Land, 1982) has several characteristics that are applicable within general and specific contexts. Effective research strategies should focus on individualized designs that are theory grounded, are situational, feasible, redundant, and efficient. As part of the introduction to the HSS 691 course, I would recommend reading the article " Designing designs for research" early in the semester to begin understanding where the above issues fit in the overall scheme of research.
To summarize the material presented, I have offered a brief outline of interrelated concepts supporting quantitative reasoning within the methodology. There is value in learning strategies to systematically strengthen the overall design and conclusions of research projects. This approach has some transferable attributes that may also address qualitative studies through mixed methods. I can only speak from my personal experience that research, in general, is a very rigorous exercise in critical thinking. Developing plausible arguments for inferences, data collection and analysis strategies, and actually writing or presenting research findings is a major effort in any field that will require a commitment and compassion for your particular area of interest. The evolving study one chooses to engage in can provide inestimable results to countless, unseen others.
There are a few issues regarding this outline that have not been addressed that merit some attention. Often I am reminded that "social research occurs in social contexts" and that not all human beings enter social research with the same level of quantitative or qualitative knowledge. Knowing personal strengths and limitations can prepare a student in research methods for the amount of work required to gain competency in quantitative research. As a student masters elementary concepts, an expansion of this knowledge can be applied to broader discussions in assertions and the development of evidence to support those assertions. One must learn to think deductively and inductively in order to view the similarities and differences of both methods which can enhance your methodological approach to research questions. Abstract concepts from both methods are not mutually exclusive, as I have observed in the course of my study.
As mentioned earlier, I am presently researching environmental efforts among indigenous populations in the United States across three ecogeographical regions. A brief contextual description of the Cornell American Indian Program (AIP) research deserves attention. Originally, the Commission for Environmental Cooperation (CEC) solicited the participation of AIP in assessing the extent of environmental initiatives within Native American communities across the country. This entailed defining the constructs of "indigenous groups," "sustainable efforts," and "cultural or indigenous knowledge systems." The research design included a purposive survey within a summative evaluation research plan. Twenty (20) descriptive case studies were selected that best represented nine ecogeographical regions of the country.
Data triangulation methods were integrated throughout the research process involving a review of relevant literature and existing program documents, as well as utilizing technological resources. A preliminary draft of ten of the twenty case studies revealed several inferences regarding motivations for initiating and maintaining environmental efforts within indigenous communities. The predominant theme was that indigenous knowledge systems were the impetus for community-wide responses to environmental concerns.
Within this context, I am beginning to piece together the elements of research and design strategies I have acquired during the course of my graduate program. Grounded theory supports those validity issues I had the most concern with. The internal debate I had about qualitative studies included questions regarding the validity and reliability of my project and the value of such a study in a larger context. I realized that I was seeking evidence to support a relationship between category and environmental initiatives.
An indigenous knowledge system (IK), as a major categorical concept, was identified through each interviewee. This concept encompassed those values that had historical or cultural significance in the continuation of tradition which included the development of an environmental initiative that addressed the needs of that population within a specific region of the country. Specifically, for the northeast region, where the Iroquoia nations reside, corn as a traditional food, was viewed as "an extremely unifying concept and way of life...which defined (native) culture... "(Interview 9). This integrative category extended into family, ceremonial, and community levels which facilitated the continuation of established values.
Patton (1990), Miles & Huberman (1994), Strauss & Corbin (1990), and Guba (1989) provided "trustworthiness" information that validated my research findings. Just as data speaks for itself and emerges into themes and patterns, so did my understanding that "trustworthiness" emerges through the efforts of the researcher to provide credible, confirmable and dependable findings. Using an audit trail offered visible support that the (IK) category was integrated into the overall research findings of my project. Technical literature, comparable interviews, and other existing linkages in the environmental network confirmed that traditional values within specific communities supplied the basis for pursuing sustainable efforts. These multiple sources addressed the internal and external validity issues of qualitative analysis that were my initial concerns.
Wolcott's (1990) article "On seeking - and rejecting - validity in qualitative research" proposed that when one lives through an experience, the experience is validated. The synthesis of personal, professional, and spiritual elements of an experience provides an image of what is Real (capital "R"). These elements are not compartmentalized in order to have a fuller understanding of the complexity of relationships within systems. So it is with the traditional values of native peoples.
In conclusion, I foresee mixing methodological approaches in establishing relationships between the above described variables. To statistically provide evidence of a relationship would require examining separate native populations, based on comparable characteristics. The feasibility of increasing the number of Native communities involved in this study may limit my scope, however I also recognize the importance of determining if there are other factors influencing a community's environmental efforts. Hey, anything is possible when you have a commitment to searching for an approximation to the truth!
Comments/Questions: R.Maldonado rmm8@cornell.edu