Module 1: Why Study Educational Research? Importance: Educational research is a Feld of inquiry aimed at advancing knowledge or education and learning processes and development of the tools and methods necessary to support this endeavor. Educational researchers aim to describe, understand, and explain how learning takes place throughout the lifecycle and how formal and informal processes of education affect learning, attainment, and the capacity to lead productive lives. One significant benefit of educational research is the development of evaluation and critical thinking skills. Primary Objectives of Educational research: Attempting to learn about and generate ideas about specific and unique phenomena. Descriptive Research: An attempt to describe the characteristics of a phenomenon. Explanatory Research: An attempt to show how and why a phenomenon operates as it does. Predictive Research: Seeks to forecast a phenomenon, wherein there is an association made between two points in time. Integration: Consolidating insights from multiple investigators, theories/perspectives, sets of data, methods in order to understand a given entity of study. Emic and Etic: Five general TYPES of research: Evaluation includes: Formative evaluation is concerned with developing judgments of how a program can be improved and aids developers and staff design and implement programs. Summative evaluation focuses on cultivating judgments of a program’s effectiveness and any decisions regarding continuation. Summative evaluation is especially helpful for policymakers to appraise previous futurefunding decisions and make future ones. Module 2: Knowledge Generation and Justification: Different sources of knowledge: Epistemology: The “theory of knowledge and its justification.” Epistemology involves studying knowledge itself — including its nature, process of generation, how it is necessary, and the standards that are used to judge its adequacy. Experience or Empiricism: The idea that all knowledge comes from experience. Reasoning or Rationalism: The philosophical idea that reason is the primary source of knowledge. Deductive Reasoning: The process of drawing a conclusion that is essentially true if the underlying premises are true. Inductive Reasoning: Holds that the foundational premises act as helpful, but not decisive reasons towards acceptance of a conclusion. Problem of Induction: The future might not resemble the past. Characterize Inductive and deductive approaches to knowledge generation. List the Dynamics of Science: Basic Assumptions of Science: Factors Related to educational issues: Psychological factors. Characteristics of individuals and individual-level phenomena. Example: Learning disabilities. Social psychological factors. Examining how individuals interact and relate to one another and how groups and individuals affect one another. Example: Middle school cliques. Sociological factors. Examining how groups form and change; documenting the characteristics of groups; studying intergroup relations; and studying group-level phenomena, such as cultural, social, political, familial, and economic institutions. Example: High school student government relations. General norms and practices of good researchers: Selection of educational and social problems in need of attention Collection of empirical data Open discussion of findings, integrity, honesty, competence, systematic inquiry, empathic neutrality Respect toward research participants A healthy skepticism toward results and explanations A sense of curiosity and openness to discovery The active search for negative evidence (e.g., instances that do not fit your emerging or current explanation of a phenomenon) The careful examination of alternative explanations for the findings An adherence to the principle of evidence. Recognize scientific methods: Exploratory and Confirmatory: Apply the criteria used to determine the quality of a theory/explanation: Rule of Parsimony: Simple, concise, and succinct. Criterion of Falsifiability: Has it survived numerous attempts by researchers to falsify it? Researchers should not selectively search for confirming evidence for their beliefs and explanations and then stop with only that so-called evidence. The principle of evidence: Other researchers conduct replication studies, examining the same variables with different participants in different techniques, thereby adding confidence to a research finding because the resulting evidence is much stronger. But even in the face of replication, strong evidence rather than proof is all that is obtained because researchers always leave open the possibility that future researchers will come up with new theories and new conclusions. Module 3: Quantitative Research: Purpose: Research with the specific purpose of answering research questions that lend themselves to study through the collection of numerical data. 9 Components: Define: Ontology: Research’s inherent understanding of reality and truth as perceived by the researchers themselves. Epistemology: The overall understanding of how knowledge is created or shared. Sampling: Random: Each individual in the population has an equal chance of being included in the sample. Representative: The population that it came from on all characteristics (the proportions of males and females, teachers and non-teachers, young and old people, and so forth) except total size. In other words, a representative sample is like the population except that it is smaller. Biased: Nonrandom samples are said to be biased samples because they are almost always systematically different from the population on certain characteristics. Data Collection: 6 Types of Data: Know the who, what, when, where and how of observations: Discuss research methods: Experimental: Manipulating variables in a controlled environment to isolate the causal effects of a particular variable or set of variables. Manipulation, an intervention studied by an experimenter, is the key defining characteristic of experimental research as it is based on the activity theory of causation. Quasi-Experimental: Experiment without randomization. Non-Experimental: Correlation: Studies that look at relationships between variables. Correlation Coefficient (Define): A numerical index that provides information about the strength and direction of the relationship between two variables. It provides information about how two variables are associated. More specifically, a correlation coefficient is a number that can range from –1 to 1, with zero standing for no correlation at all. Positive Correlation: Present when scores on two variables tend to move in the same direction. Negative Correlation: Present when the scores on two variables tend to move in opposite directions. Predicative: Studies that examine prediction of future levels of a variable. Casual Comparative: Studies that examine how different pre-existing groups vary on a variable or variables. Explain the three elements of Quality Criteria in QN Research Manipulation of the independent variable for casual conclusions: In order to draw a conclusion about cause, the independent variable has to occur before the dependent variable, the independent and dependent variable need to be related statistically, and we need to be able to rule out other causes. Control of the study to maximize internal validity: Appropriate statistical analysis allows us to determine that variables are related. We have to ask whether we have carried out the appropriate statistical techniques in order to state that there are relationships between variables (statistical conclusion validity). Construct Validity and the Dependent Variable: Have we chosen to measure a construct that is a valid one (one that we have evidence to support--construct validity)? In addition to looking internally at the study, we need to examine whether the study results have external validity. We do this by examining the sampling of people and situations. Do we have a sample of people and situations that can be seen as representative of the populations we are interested in claiming that the study’s results describe? We also examine the context of the study to see how well it lines up with real world educational contexts (both in terms of their features and the timelines they follow). We are interested in the generalizability of the study outside of the chosen treatment populations and educational contexts. Therefore, the study must have some relevance to the ways in which the phenomena of interest actually work in the real world. Lastly, a study has more credibility if it replicates some results from other studies. Variables in Quantitative Research: Variables in General: A condition or characteristic that can take on different values or categories such as age, grade point average, test scores, and gender. Be able to identify what is a QL and what is a QN variable from a list QN: Varies in degree or amount of a given variable within a given phenomenon, usually provided in numerical form. Height, Weight, Grade Point Average, Test Scores, ETC. Constant: Something that does not change, but takes on a single value. Independent, Dependent, Mediating, Moderator, Extraneous: Data Analysis: be able to discuss: Estimation: Estimate the characteristics of populations based on their sample data. Hypothesis Testing: Test specific hypotheses about populations based on their sample data. Inferential Statistics: Quantitative researchers use the laws of probability to make inferences about populations based on sample data. Reporting: Know the 7 major sections required in QL Reports: Strengths and Limitations in QN: Quantitative research, especially experimental research, is very useful for establishing cause-and-effect relationships (strength). When based on random samples (such as in survey research), quantitative research is very useful for making statistical generalizations about populations (strength). Quantitative research is less useful for exploring new phenomena or for documenting participants’ personal perspectives and personal meanings about phenomena in their lives (weakness). Module 4: Qualitative Research: Purpose: Focused on studying particular phenomena through the collection of non-numerical data such as words, images, pictures, and interpretive categories. Qualitative research is used to describe and understand what occurs locally (rather than globally), but it is nevertheless used at times to come up with or generate new hypotheses and new theories. Qualitative research can be used when little is known about a topic or phenomenon, but more generally, it is used whenever one wants to discover or learn more about something in our world that is too complex for numerical data to capture. Naturalistic Inquiry: Studying real-world situations as they unfold naturally in a nonmanipulative and non-controlling way (without predetermined constraints on findings). Holistic Dimensions of ‘Community’: To understand people, groups, and settings in all of their complexity. This includes developing an understanding of multiple dimensions and layers of reality, such as the subgroups in a group, how they think, how they interact, what kinds of agreements or norms are present, and how these dimensions come together holistically as group members interact as a “community.” The influence of historical intellectual movements that inspire QL research: Nine Components of QL Research: The Assumptions of QL Research: Linguistic-relatively hypothesis: Qualitative researchers often contend that “reality is socially constructed” (e.g., Guba & Lincoln, 1989). For example, social behavior follows socially constructed values, beliefs, and norms. Language also can influence our views of the world. For example, it has been suggested that the Inuit “see” many types of snow, whereas the average U.S. American probably only sees a few types of snow. Inuits’ local languages might allow them to see distinctions that you do not notice. Empathetic Understanding: The qualitative researcher constantly tries to understand the people he or she is observing from the participants’ or natives’ or actors’ viewpoints. Ontology: The research’s inherent understanding of reality and truth as perceived by the researchers themselves. Epistemology: Acts as the overall understanding of how knowledge is created or shared. Understand the sampling methods of QL research: Qualitative sampling is often referred to as criterion-based or purposive sampling: The key point is that a researcher should pick a sample that can be used to meet the purpose of the research study and answer research questions while meeting cost and other constraints. 6 Types of Data: Key Principles of QL fieldwork: Personal experience and engagement- Researcher has direct contact with and gets close to the people, situation, and phenomenon under study. The researcher’s personal experiences and insights are an important part of the inquiry and critical to understanding the phenomenon. Empathic neutrality and mindfulness- Researcher adopts an empathic stance in interviewing seeks vicarious understanding without judgment (neutrality) by showing openness, sensitivity, respect, awareness, and responsiveness. In observation this means being fully present (mindful). Dynamic systems- Attention is paid to process. Researcher assumes change is ongoing whether the focus is on an individual, an organization, a community, or an entire culture; therefore, the researcher is mindful of—and attentive to—system and situation dynamics. Theoretical frameworks that constitute QL Research: Define Triangulation and its necessity: The use of multiple forms of data to capture a single phenomenon. As an outcome, “triangulation” is said to occur when your results converge on the same conclusion. Major characteristics of QL Research Analysis: Unique case orientation - The researcher assumes that each case is special and unique. The first level of analysis is being true to, respecting, and capturing the details of the individual cases being studied; cross-case analysis follows from—and depends on—the quality of individual case studies. Inductive analysis and creative synthesis- Researcher seeks immersion in the details and specifics of the data to discover important patterns, themes, and interrelationships. Begins by exploring, then confirming; is guided by analytical principles rather than rules. Study ends with a creative synthesis. Holistic perspective- The whole phenomenon under study is understood as a complex system that is more than the sum of its parts. The focus is on complex interdependencies and system dynamics that cannot meaningfully be reduced to a few discrete variables and linear, cause-effect relationships. Context sensitivity- Researcher places findings in a social, historical, and temporal context and is careful about, even dubious of, the possibility or meaningfulness of generalizations across time and space. Emphasizes instead careful comparative case analyses and extrapolating patterns for possible transferability to and adaptation in new settings. Voice, perspective, and reflexivity- The qualitative analyst owns and is reflective about her or his own voice and perspective; a credible voice conveys authenticity and trustworthiness. Complete objectivity being impossible and pure subjectivity undermining credibility, the researcher’s focus is on balance—understanding and depicting the world authentically in all its complexity while being self-analytical, politically aware, and reflexive in consciousness. Strengths and Limitations of QL: Module 5: Mixed Methods Research: Purpose: Provides a way to investigate the quantitative and the qualitative aspects of human thought and behavior in its various contexts. Rationales: Triangulation- Seeks convergence, correspondence, and corroboration of results from different methods. Complementarity- Seeks elaboration, enhancement, illustration, and clarification of the results from one method with the results from the other method. Development- Seeks to use the results from one method to develop or inform the other method, where development is broadly construed to include sampling and implementation as well as measurement decisions. Initiation- Seeks the discovery of paradox and contradiction, new perspectives and new frameworks, and the recasting of questions or results from one method with questions or results from the other method. Expansion- Seeks to extend the breadth and range of inquiry by using different methods for different inquiry components. Fundamental Principle of Mixed Method Research: Thoughtful mixing of methods, procedures, and other paradigm characteristics is an excellent way to conduct high-quality research. Specifically, researchers should mix in a way that provides multiple (divergent and convergent) and complementary strengths (viewed broadly) and nonoverlapping weaknesses. Warranted Assertability: Present when you have good evidence about your research claim. Assumptions of Mixed Method Research: How the underlying assumptions of both quantitative and qualitative methods contribute to the foundational assumptions of mixed methods research. Incompatibly thesis: Some research methodologists insist that one must conduct either a qualitative or a quantitative study because the assumptions underlying these two major research approaches cannot be mixed. Either-or position. Compatibility thesis: Quantitative and qualitative approaches can be used together in a single research study as long as researchers respect the assumptions associated with quantitative and qualitative research and construct a thoughtful combination that will help to address their research question(s). Pragmatism: What is ultimately important and justified or “valid” is what works in particular situations in practice and what promotes social justice. Focused on consequences and the ends that researchers value. Ontology: Ontology is about what is real. Epistemology: Epistemology focuses on how knowledge is created, discovered, and justified or warranted. 9 Components of Mixed Method Research: Understanding sampling schemes for Mixed Method Research: Paradigm/research-approach emphasis: Refers to whether the qualitative and quantitative parts of the study are given approximately equal emphasis (i.e., equal-emphasis or interactive design) or if one part is considered primary and more strongly emphasized (resulting in either a qualitatively driven design or a quantitatively driven design). Time orientation criterion asks whether quantitative and qualitative data collection occur concurrently or sequentially. Sample relationship criterion: The two criteria just discussed—time orientation (which has two types) and sample relationship (which has four types - above)—result in eight mixed methods sampling designs identical concurrent identical sequential parallel concurrent parallel sequential nested concurrent nested sequential multilevel concurrent multilevel sequential Data Collection Methods: Inter-method mixing: Two or more of the methods of data collection are used in a research study. Intra-method mixing: Both quantitative and qualitative data are obtained through the creative use of a single method of data collection. Types of Validity: Types of Analysis: Analysis Types Data Types Monoanalysis Multianalysis Monodat a Monodata-monoanalysis Quantitative analysis of quantitative data OR Qualitative analysis of qualitative data *This is not a type of mixed data analysis. Multidata-monoanalysis Only quantitative analysis of both quantitative and qualitative data OR Only qualitative analysis of both qualitative and quantitative data *This type is not frequently used. Monodata-multianalysis (a) For quantitative data: Quantitative analysis (QUAN) and qualitative analysis of quantitative data (QUALITIZE). OR (b) For qualitative data: Qualitative analysis (QUAL) and quantitative analysis of qualitative data (QUANTITIZE) Multidata-multianalysis This is a combination of “(a)” AND “(b)” from Monodata-multianalysis cell Multidata Qualitizing data – One way of qualitizing data is by forming narrative profiles (e.g., modal profiles, average profiles, holistic profiles, comparative profiles, normative profiles), in which narrative descriptions are constructed from statistical data. Quantitizing data – When researchers quantitize data, qualitative ‘themes’ are numerically represented, in scores, scales, or clusters in order to provide a comprehensive description of the studied phenomena. This technique allows for researchers to understand how often various categories or statements occurred in qualitative data. Strengths and Limitations: Strengths: Can answer a broader and more complete range of research questions. Words, pictures, and narrative can add meaning to numbers. Numbers can add precision to words, pictures, and narrative. Can provide fuller, deeper, more meaningful answers to a single research question. Insights and understanding that might be missed when only a single method is used. Quantitative sampling approaches can be used to increase the generalizability of qualitative results. Qualitative data can help a researcher identify and rectify quantitative measurement problems. Can strategically employ the principle of complementary strengths. A researcher can use the strengths of an additional method to overcome the weaknesses in another method (principle of nonoverlapping weaknesses.) Can concurrently study nomothetic (general) and idiographic (particularistic or local) causation and produce “practical theory.” Can provide stronger evidence for a conclusion through convergence and corroboration of findings (principle of triangulation). Quantitative data can add understanding of amount and frequency to otherwise qualitative studies. Combining qualitative and quantitative research produces integrated knowledge that best informs theory and practice. Limitations: Methodological purists contend that one should always work within either a qualitative or quantitative paradigm. It is difficult for a single researcher to carry out both qualitative and quantitative research, especially if two or more approaches are expected to be done concurrently. Researcher has to learn about multiple methods and approaches and understand how to mix them appropriately. It is more expensive, and time-consuming. Some of the details of mixed research remain to be worked out by research methodologists. Module 6: Action Research: Purpose: Focused on addressing and solving specific problems that educational professionals face in their local schools and communities. Origin: Kurt Lewin (1890–1947) first coined the term action research and he practiced applied social research during the 1930s and 1940s until his untimely death in 1947. Kurt Lewin was also a well-known social psychologist. He is often considered the father of academic social psychology in the United States. Lewin tried to link theory with action, and he spent his career attempting to solve social problems. He wanted to connect national problems with local problems, such as racism, sexism and poverty. Lewin’s Change Theory: Quasi-stationary equilibrium - The result of forces for change (driving forces) and forces against change (restraining forces) being about equal. Driving Forces: Forces for change. Three phases: John Dewey’s approach to inquiry: The thinking human organism is always embedded in and part of a dynamic, local, and complex ecology. Humans are adaptive organisms, continuously trying to improve their world. Transactional Theory: We are not separate from, but rather are part of, our environments. Our environments affect us and we affect our environments, continuously. 9 Components of Action Research: Assumptions of Action Research: Applied Research: Action research falls on the applied end of the basic-versus-applied research continuum. Furthermore, in basic or regular scientific research, the primary goal is to produce knowledge. Pragmatism: Action research follows the philosophy of pragmatism, where we are concerned about acting in ways to solve problems and produce desired consequences. Research Attitude: This attitude involves continuously identifying new problems to work on and trying new strategies and actions to see what improves the situation. Many practitioners find action research helpful because it helps them to integrate theory and research with practice. Researchers are said to have an action research attitude when they take on the attitude of a practitioner and a researcher in order to think about: Circle of Knowledge for the Enterprise of Education Science: On the one hand, the top-down arrow shows that local practice should be informed by academic research about best practices; translational research is important for this endeavor by translating scientific research into easily understood language and procedures of practice. On the other hand, the bottom-up arrow shows that “best practices” also should be informed by what practitioners find works well at the local level. Each of these two levels needs to learn from the other, sometimes collaboratively (e.g., when university researchers and local teacher researchers work together). Sampling and data collection are contingent on the problem to be solved: QN – numeric data (i.e. improving student performance as assessed by tests. QL – text data (i.e. gaining insight on the perceptions of existing processes. Data Analysis for Action Researchers: Reflective Practitioner: They think about, analyze, and debate what is the most effective for their given situation. Describe the Cycle of Action Research: Depending on the situation, an action researcher might start at the reflection phase, another at the planning phase, another at the action phase, and yet others at the observation phase. It depends on where they are, and most go through this cycle many times. For example, acting (at your workplace), observing outcomes, reflecting, and planning are all fine starting points. This cyclical process is similar to Dewey’s idea of learning and growing over a lifetime. Lifelong learning: In Education, many action research projects require multiple cycles in which a researcher plans and tries something small, observes and reflects (e.g., makes a formative evaluation and adjusts their theory), and then plans a new cycle of improvement. Continuous Quality Improvement: Same as above, but in business. Four Types of Action Research: Strengths and Limitations of Action Research: Strengths: Can be conducted by local practitioners. Produces lifelong learners. Integrates theory and practice. Is committed to democratic social change. Empowers practitioners to contribute to knowledge. Describes the complexities of local situations. Improves practice at the local level. Limitations: Often involves a small-scale study that produces a limited and delimited amount of information and knowledge. Produces small-scale results that are difficult to generalize to different and larger contexts. Has less scientific objectivity compared to regular education science. Is often based on weaker research designs compared to regular education science. Does not lend itself to making strong statements of cause and effect. Lacks rigor in terms of traditional measurement and research validity criteria. Presents difficulties for institutional review boards (IRBs), who evaluate the ethical practice of the research, because multiple people might be involved and the researcher cannot foresee many possible actions due to the study’s fluid nature and continual development. Module 7: Comparing the Four Major Approaches to Research: Based on: Methodological Paradigm and Research Method: Scientific Methods: QN - Quantitative/ numerical description, causal explanation, and prediction. QL - Qualitative/subjective description, empathetic understanding, and exploration. Mixed - Multiple objectives; provide complex and fuller explanation and understanding; understand multiple perspectives. Action - Identify and solve pressing issues within the educational environment and community using quantitative, qualitative, or both approaches. Research Objectives: While quantitative and qualitative research is considered with describing situations via numerical or subjective data, mixed research seeks to explain a phenomenon using all available and relevant data. Action research, on the other hand, seeks to determine and resolve a critical problem within the real-world educational environment. Observation Natures: QN - Study behavior under controlled conditions; isolate the causal effect of single variables. QL - Study groups and individuals in natural settings; attempt to understand insiders’ views, meanings, and perspectives. Mixed - Study multiple contexts, perspectives, or conditions; study multiple factors as they operate together. Action - Study behaviors and people in real-world settings before, during, and after the implementation of specific intervention action. Data Type Collection: QN - Collect quantitative data & variables based on precise measurement using structured and validated data-collection instruments. QL - Collect qualitative data (words, images, categories) such as in-depth interviews, participant observations, field notes, and open-ended questions. The researcher is the primary data-collection instrument. Mixed - Collect multiple kinds of data: mixture of variables, words, categories, and images. Action - Collect forms of data based on the specific situation studied and action taken: this can be strictly quantitative, but often it consists of a mixture of numerical data, descriptive/ language data, etc. Data Analysis: QN - Identify statistical relationships among variables. QL - Use descriptive/language data; search for patterns, themes, and holistic features; and appreciate difference/variation. Mixed - Quantitative and qualitative analysis used separately and in combination. Action - Matches approach to local need, typically utilizing quantitative or mixed approaches. Reported Results: QN - Generalizable findings providing representation of objective outsider viewpoint of populations. QL - Particularistic findings; provision and integration of insider viewpoints on the phenomena under study. Mixed - Provision of “subjective insider” and “objective outsider” viewpoints; presentation and integration of multiple dimensions and perspectives. Action - Mix of specific findings related to explaining the environment and generalizable conclusions for larger educational community. Module 8: Literature Reviews Purpose: Comprehensive evaluation and summarization of scholarly research which addresses a particular research topic. Process: Relevance of a lit review for: QN: QL: Mixed: Literature reviews for mixed methods studies must certainly address the relevant objectives of both qualitative and quantitative studies. However, literature review that is conducted must support methodological decision making. In other words, the literature review must itself lead to mixed methods research questions. As literature is identified and reviewed, researchers must pay particular attention to the methods employed in cited studies: Did they use one method or mixed methods? What advantages did each approach offer for understanding the topic/problem at hand? How did each approach advance our understanding of the topic/problem? What are the implications of the methods used in the cited studies for mixed approaches? Action: Literature reviews are helpful for seeing what has worked for other action researchers, and applying previous utilized quantitative and qualitative methods within new and/or different contexts. From there, action researchers are able to develop their research topic down to a narrowed research question which applies to the given practitioner’s situation. The literature review assists action researchers in anchoring their practices in a research base so that they are more likely to select strategies based upon evidence and simultaneously generate high quality evidence to support both their findings and practices. Differentiate between an annotated bibliography and lit review: Annotated bibliography: Key features of annotated bibliographies include a reference list with summaries and annotations, which can be used to help highlight characteristics or critical analyses of the source. They are often done primarily for educational assignments or as an aid for those writing reviews to keep track of sources. Occasionally, they end up as a useful way to keep a master reference list on a particular topic. No set guidelines for their creation or design, although many university libraries have information on them. Lit Review: The APA provides guidelines for writing and formatting a variety of written reports, including literature reviews. APA-styled literature reviews tend to be more focused on a specific research question, although the length and content might be different depending upon the type of report (e.g., dissertations tend to have more broadly defined literature reviews). A literature review can be part of a study, part of a proposal, or a standalone paper that critically analyzes a topic. APA literature reviews are often published in journals and online databases as a valuable source of information for researchers to help develop their own research questions, as well as provide students and practitioners with valuable insight into a developing problem or educational issue. Meta-analysis: A technique to integrate and describe results from large amount of quantitative studies, and it tends to focus on a very specific question that can be quantified in a large number of similarly designed studied. Module 9: Planning a Research Study: Purpose/Necessity of having research questions: Research questions (and, in the case of many quantitative studies, research hypotheses) act as the driving force behind research study design and execution, providing researchers with invaluable guidance and direction. Different types of questions for QL and QN QN: The emphasis is on the need to explain, predict, or statistically describe some outcome or event. QL: Focuses on understanding the inner world of a particular group or exploring some process, event, or phenomenon. The necessity of formulating a hypothesis: To guide research. The need for a Statement of Purpose: To expresses the researcher’s intent or objective in conducting their particular study. Difference between declarative statement in QN and statement of intent in QL: QN: The purpose statement in a quantitative study is a declarative statement that identifies the type of relationship investigated between a set of variables. QL: Should indicate that the intent of the study is to explore or understand some phenomenon experienced by certain individuals at a specific research site. Action Research: Statements of purposes are very much driven by the specific environments the study is conducted within. Additionally, a major difference from other approaches is that action research purposes are more idiosyncratic and local. Define: Hypothesis: The formal statement of the researcher’s prediction of the relationship that exists among the variables under investigation. Hypothesis Testing: Researchers conduct the study to determine whether the predicted relationship among the variables exists in the data collected. Module 10: Research Proposals and Ethics: Framework of the Research Proposal (Key sections of a Research Proposal Report): Title Page Table of Contents Abstract Introduction 1. Introduction to the research topic 2. Statement of the research problem 3. Summary of prior literature 4. Statement of the purpose of the study 5. Research question(s) 6. Research hypotheses (if a quantitative study is being proposed) Method 1. Research participants 2. Apparatus and/or instruments 3. Research design 4. Procedure Data Analysis References 3 Approaches in Research Ethics: Ethical Concerns: 5 AREA Code of Ethics: Informed Consent: Before a person can participate in a research study, the researcher must give the prospective participant a description of all the features of the study that might reasonably influence his or her willingness to participate. Describe: Deception: Appropriate when providing full disclosure of the nature and purpose of a study will alter the outcome and invalidate the study. Debriefing: An interview conducted with each research participant after he or she has completed the study. Dehoaxing: Informing the participants about any deception that was used and explaining the reasons for its use. Desensitizing: Helping participants, during the debriefing interview, deal with and eliminate any stress or other undesirable feelings that the study might have created in them, as might exist if you are studying cheating behavior or failure. Freedom to withdraw: Participants have the right to withdraw from a study at any time, unless otherwise constrained by their official capacity or roles. Difference between confidentiality vs anonymity and Concept of Privacy: Module 11: Standardized Measurement in Educational Research: Define measurement: The act of measuring, which involves identifying the dimensions, quantity, capacity, or degree of something. Measurement Scales: Be able to Discuss: Measurement Reliability (and its coefficient): Reliability refers to the consistency or stability of the test scores. A reliability coefficient of zero stands for no reliability at all. (A negative correlation is treated as meaning no reliability and that the test is faulty.) A reliability coefficient of +1.00 stands for perfect reliability. Researchers want reliability coefficients to be strong and positive (i.e., as close to +1.00 as possible) because this indicates high reliability. Types: Measurement Validity: The appropriateness of the interpretations, inferences, and actions made based on test scores. Validity Evidence: The empirical evidence and theoretical rationales that support the interpretations and actions that taken on the basis of the score(s) obtained from an assessment procedure. Validation: The inquiry process of gathering validity evidence that supports score interpretations or inferences. Module 12: Data Collection: 6 Types or Methods – Data Collection Methods Tests Purpose/Intention Measures attitudes, personality, self-perceptions, aptitude, and performance. Questionnaires Obtain information about the thoughts, feelings, attitudes, beliefs, values, perceptions, personality, and behavioral intentions. Interviewer impartially collects the data from the interviewee, who provides the data. Interviews Focus Groups Examine, in detail, how the group members think and feel about a topic. Observation Obtain information about the phenomenon by watching behavioral patterns of people in certain situations. Constructed and secondary or existing data Constructed- information produced by your research participants during the research study. Secondary/existing- information collected, recorded, or left behind at an earlier time, usually by a different person and often for an entirely different purpose. Varieties Standardized intelligence and personality, Achievement, Preschool, Aptitude, Diagnostic, Experimental In-person (face-to-face), Over the phone Quantitative (closed) Qualitative Standardized open-ended Interview guide approach Informal conversational Two to four homogeneous groups of 6-12 participants per research study Researcher may include some heterogeneity, depending on the purpose Quantitative (structured) standardization of all observational procedures Qualitative (naturalistic) exploratory without advance specification Constructed Drawings/Paintings Diaries Recordings Videos Newly produced personal documents Secondary/existing Personal documents Official documents Physical data Archived research data Difference between Population and Sample: Difference between Random Sample and Random Assignment: Module 13: Validity of Research Results: Different types of validity in QN research: Trustworthiness: Describe the types of legitimation in Mixed (Repeat from Mixed Module): Module 14: Research Methods: (Most of this chapter is repetitive from previous modules) Select the appropriate research method for any given scenario. Examples: 1. A researcher seeks to investigate the effect a reading readiness program has on reading comprehension scores for 5th grade students. Additionally, the researcher intends to interview 5th grade teachers to gain the teachers’ perspectives and experiences on whether this reading readiness program is effective for their students. Mixed methods research would be the most appropriate choice for this researcher. A key element for this choice is the dual focus on quantitative data (reading comprehension scores) and qualitative data (teacher interviews). Also, the researcher is combining a confirmatory and exploratory approach by evaluating the expected consequence of the reading readiness program alongside the open-ended exploration of multiple teacher perspectives on the program’s effectiveness. 2. A researcher wants to examine the effect that a reading readiness program has on reading comprehension scores of 5th grade students. Quantitative research would be the most appropriate choice for this researcher. An element which supports this choice includes the focus of examining an expected effect of the reading program. This would typically include collecting and analyzing quantitative data (reading comprehension scores), with an emphasis on testing them against an expect effect (increase in scores), thereby performing a confirmatory study. 3. A researcher wants to address the consistently low reading scores in his local elementary schools by integrating a new reading readiness program with a chosen group of 5th grade students. Action research would be the most appropriate choice for this researcher. A key element to support this choice is the focus on the researcher’s goal of addressing a critical issue within the local educational community. Additionally, by acting on and observing a specific intervention in a representative learning environment, with a mix of quantitative and qualitative data methods, the research is creating an applied mix of confirmatory and exploratory research approaches. 4. A researcher wants to investigate and collect 5th grade teachers’ perspectives and experiences on the effectiveness of a recently implemented reading readiness program aimed at elementary school students. Qualitative research would be the best choice for this researcher. The biggest element for indicating a qualitative approach is the emphasis on collecting data such as the teachers’ personal experiences with the reading readiness program. The exploratory nature of the study’s focus, collecting teacher perspectives to generate new knowledge and understanding, is reflective of a qualitative approach. Module 15: Data Analysis: Purpose of Descriptive Statistics: The goal is to describe, summarize, or make sense of a particular set of data. The focus is more on interpretation than on calculation. Data set: Descriptive statistics starts with a data set, and the researcher attempts to convey the essential characteristics of the data by arranging it into a more interpretable form (e.g., by forming frequency distributions and generating graphical displays) and by calculating numerical indexes, such as averages, percentile ranks, and measures of spread. Visual Representation of Data. Bar Graphs: A bar graph is a graph that uses vertical bars to represent the data. Histograms: A graphic presentation of a frequency distribution. Line Graphs: A format for illustrating data that relies on the drawing of one or more lines. Scatter Plots: A way to visualize the relationship between two quantitative variables. Describe the measures of central tendency: The single numerical value that is considered the most typical of the values of a quantitative variable. Define: The mode is the most frequently occurring number. The median, or 50th percentile, is the middle point in a set of numbers that has been arranged in order of magnitude (either ascending order or descending order). The mean is the arithmetic average. Understand Distributions: The normal distribution, or normal curve, is a unimodal, symmetrical distribution that is the theoretical model used to describe many physical, psychological, and educational variables. Normal Postively Skewed: (Mean is largest) Negatively Skewed: (Mean is smallest) Purpose of Inferential Statistics: Researchers use the laws of probability to make inferences about populations based on sample data. Define Estimation: Researchers want to estimate the characteristics of populations based on their sample data. Point estimation involves using a single number as the estimate of the population parameter. Interval estimation, on the other hand, includes a range of numbers believed to contain the population parameter. Discuss hypothesis testing: Branch of inferential statistics that is concerned with how well the sample data support a particular hypothesis, called the null hypothesis, and when the null hypothesis can be rejected. Null hypothesis: Predicts no difference or no relationship in the population. Alternative hypothesis: States that the population parameter is some value other than the value stated by the null hypothesis. Significance testing: When researchers engage in hypothesis testing, they are also checking for statistical significance. Different types: t test for independent samples, the t test for correlation coefficients, the analysis of variance, and the chi-square test for contingency tables.