Group+B


 * Group B's Wikispace**

**Action Research** Action Research is a term used to describe when a professional explores questions related to and studies the data that emerge from one’s own //actions// in a professional context. Action Research is most notably used in the Education field. For example, if a teacher wants to examine what classroom practices lead to improved reading comprehension in her struggling third-grade readers, an exploration of research-based interventions and an analysis of the resulting data would be achieved through action research. Action Research has applicability outside of the field of education as well; it can be applied to any field where application of ideas needs to be studied. An example from the world of Business would be if a Marketing Manager wanted to explore the most effective methods for motivating his sales staff to sell beyond their previous marks, he could deliver different methods of incentive as well as developing intrinsic motivation in his employees, and he could examine the resulting sales data after motivational techniques have been applied. Action Research still relies on research and the collection of data, yet it allows this research to be applied and this data to be culled from real-world actions with which the researcher has a direct connection.

**Causal Inferences** Causal inferences suggest that the relationship between goals and goal-directed behaviors benefits most from knowing the results of a particular action. Inferences based on research and factual information has more substantial results than those that are based on assumptions. If told that putting your hand in hot water will get you hurt, the best deterrent for keeping your hand out of the water is to get burnt (1994). Causal inferences are assumptions based on a particular cause.

**Correlations** Correlations are relationships in data trends. The Encarta Dictionary defines a correlation as the relatedness of variables where variables are related and change together, or a relationship in which two or more things are complimentary. An example of correlations in data can be something like the relationship between intelligence and reading achievement. In a study such as this one done by Vellutino, intelligence correlates to reading achievement; when students’ intelligence is high in IQ testing then their reading achievement will also be high. They are two variables that change together.

**Correlation Coefficient** Correlation coefficient is a statistic measuring the degree of correlation between two linear variables. To get this statistic according to the World English Dictionary, you must divide the variables covariance by the square root of the product of their variances. The closer to 1 or –1 the correlation coefficient is the closer the correlation between variables. If the correlation reaches 0 it is a random correlation. A correlation coefficient of + 1 is considered a positive linear coefficient; it is a linear relationship that increases together. An example of a positive correlation coefficient would be when IQ increases so does reading achievement. A correlation coefficient of –1 is considered a negative correlation; both values decrease at the same rate. An example of a negative correlation coefficient would be when attendance decreases so does academic achievement.

**Formative research methods ** Formative research will enable you to better understand your students and their needs and wants. It will also allow you to get useful information from the right people, make decisions with an audience-focused mindset and refine your plan to ensure success of your lesson. Poorly conducted formative research can lead to either too much information or the wrong kind of information, both of which hinder your ability to develop an effective lesson or assessment. The information you collect during the formative research phase should help you to narrow and describe the target audience. Select a specific behavior or attitude for the students to change. Identify the factors which influence the student's behavior and based on this, develop a preliminary intervention.

Implications are the conclusions that we make after analyzing data. They are something that is drawn in as a consequence of something else or a relationship between two things. In education, teachers come to these conclusions on a daily basis, most likely not even realizing it. After giving a benchmark on plot, a teacher might review their results, and deduce that their students understand how to recognize the exposition, rising action, and resolution, but need more instruction on the climax.
 * Implications**

**Inference** An inference is a drawing a conclusion from evidence in data or information given. It is related to deductive reasoning or implied meaning. In an article on data and mathematics it is referred to as drawing conclusions based on logical rules derived from given information. (Ayalon & Even 2008) The article further goes on to argue that if information given is valid then the conclusion also must be valid if it is based on that information. Making an inference about data also relates to logical reasoning. A statement often used in this kind of reasoning is “if this is true than that must also be true.” It is the logic process of reasoning from a premise to a conclusion. (2011)

**Multiple methods** Multiple methods refer to differentiated instruction. It follows the model that no one is identical and therefore cannot learn in the exact same way. As educators, we are faced with the complicated task of ensuring that all students in a class learn effectively, (2005). This method ensures that the learning style of students is tested and then lessons are created to suit them. In any classroom environment, the same topic is taught to multiple students, and each student may prefer to learn a certain way. Multiple methods ensure that educators change their teaching methods in order to provide students with a range of options dealing with learning new ideas.

**Ordinal Scale** A scale used for data wherein items are ranked not according to inherent order (i.e. one comes first, two comes second, three comes third...), but instead according to their relationship to each other such as their ranking order. According to Lutz and Keenan (1999), an ordinal scale is, "A measurement scale in which numbers indicate rank (from highest to lowest)." This term applies to research results insofar as students' test scores and performance data are valuable in how they relate to each other in terms of class rankings or score groups (i.e. six A's, three B's; or 20% Advanced, 60% Proficient; etc.). An ordinal scale becomes valuable for researchers in instances when a clear standard for performance is not articulated (like on the SAT where a 2400 is a perfect score) and instead data such as scores must be related to each other and ranked for meaning to be uncovered.

**Participant** For the purposes of research, a participant is one who is directly connected to the area which he or she is researching. For Educational research, participant observations often become the preferred method of collecting data. By default, teachers “are full participants in the everyday life and practices of their classrooms” (Lankshear and Knobel 2004, 225); as a result, teachers are invested and active in their collection of data instead of being detached and staid as they observe and report. Participant observation contrasts sharply with non-participant observation insofar as the latter requires physical removal from the observation (such as seclusion behind a one-way mirror); participant observers make themselves known to the subjects and make their purposes clear. Because participant researchers such as teachers within a same school building can “rightfully claim insider perspective” (ibid), their insights can lend seriousness and weight to their research findings; however, because of human nature to get involved, sometimes participants can create flaws in their data through too much involvement.

**Practitioner Research** Practitioner Research is the act of researching and collecting data for the purpose of advancing one’s own practice within his or her professional domain. Usually applied in the context of the social sciences such as Social Work, Counseling, Criminal Justice, and Education, practitioner research, like Action Research, allows a professional to study his or her own practice and arrive at solutions that would improve his or her actions and the reactions of his or her clients and/or students. Practitioner research can involve literature review, structured and unstructured observations, and data mining as well as other activities that enable one to delve into his or her own professional practice and also provide opportunity for one to //improve// and //advance// his or her professional domains. When embarking on practitioner research, one should be clear about the types of data he or she is collecting as well as the specific questions he or she is endeavoring to answer through research.

**Procedural Models** Procedural Models are usually research based instructions on how to go about operating in some fashion. These models may give an overall example on how some form of organization, program or act should be carried out. Procedural models are often seen in the social sciences as they may guide some form of social interaction. Lind (1978) suggests that a procedural model is an overall dimension of procedural variations specified for a specific discipline (i.e. adjudication or education). Procedural models are important to education because much of the pedagogy in education is based off of some research based model or method. Teachers and administrators all rely in models in order to get the best response from students in terms of performing academically, socially, etc. Providing best practice instruction can be applied across the board. In the classroom this can be beneficial for the teacher as well as the students. Teachers can have a foundation to work from in terms of instruction and students can be provided with simple procedural models that can help them to stay organized or complete task for assignments.

**Qualitative Research** Qualitative Research is investigative methodologies which emphasize the importance of looking at variables in the natural setting in which they are found (615 Glossary) "Situating Technology Professional Development in Urban Schools," (Meier, 2005) shared a qualitative research study that was completed with four university facilitators and 100 New York teachers. The purpose of the study was to "identify factors that strengthen the integration of technology in classrooms in ways that are consistent with larger school improvement themes" (Meier, 2005 pg. 396). The data collected during the study included many different qualitative techniques including the following: teacher questionnaires that asked about curriculum topics and concerns, teachers comfort with technology and project-based learning, informal interviews with teachers to learn, for example, how the professional development was helping them meet the needs of their classroom, workshop observations, regular meetings between the teachers and university facilitators, and e-mail conversations between the facilitators (Meier, 2005 pg. 400).

**Quantitative Research** <span style="font-family: 'Times New Roman','serif'; margin: 0in 0in 0pt;">Quantitative Research is the use of sampling techniques (such as surveys) whose findings may be expressed numerically, and are amenable to statistical manipulation enabling the researcher to estimate future events or quantities (615 Glossary). In the article, "Learning with Laptops: Implementation and Outcomes in an Urban, Under-Privileged School," Mouza shares qualitative research that supports the implementation of laptops in the classroom (2008). In combination with quantitative strategies, the researchers in this study used student surveys to analyze "whether grade level and having laptops in the classroom influenced student attitudes" (Mouza, 2008 pg. 459). They used MANOVA and a t-test, "which assesses whether the means of two groups are statistically different from each other" (Trochim, 2006), to determine that "fourth graders who had laptops were significantly more likely than comparison students to have positive attitudes toward school" (Mouza, 2008 pg. 460).

Reliability is the extent to which your measuring tool is consistently measuring what is being measured. This term is often seen in association with the word validity. Reliability is important because it lets you know how well phenomenon is being measured. If students are taking a test, the test would be considered reliable if the results are similar each time the test is taken. This is important in education because of state standardized test. Having an understanding of reliability will ensure that teachers and educational officials are using the right testing formats and tools to make sure that the students are taking the right assessments.
 * Reliability**

**Replicability** <span style="font-family: 'Times New Roman','serif'; margin: 0in 0in 0pt;">Replicability is often a term related to reliability. In relation to data, replicability is being able to repeat data patterns or have reoccurring results. In an article about competence in students to understand scientific processes they define replicability as an understanding in the variability with measuring data to seek reliability. (Jeong, Songer, & Lee 2006) Other sources say that replicability, when in relation to data about people, should not only be whether the same data is seen for the same person but should be replicable cross-person analysis. (Bath, Daly, & Nesselroade 1976) <span style="font-family: 'Times New Roman','serif'; margin: 0in 0in 0pt;"> <span style="font-family: 'Times New Roman','serif'; margin: 0in 0in 0pt;"> ** <span style="font-family: 'Times New Roman','serif'; font-size: 12pt; line-height: 115%;">Standard score **<span style="font-family: 'Times New Roman','serif'; font-size: 12pt; line-height: 115%;"> – <span style="font-family: 'Times New Roman','serif'; font-size: 10pt; line-height: 115%; margin: 0in 0in 10pt;">The term standard score is utilized to draw a comparison between individuals taking the identical test. The result of this test is used to compare and contrast the results within that group. Results taken from the test is closely reviewed by looking at its representation of the standard score. Using the results, students who score below the standard score are defined as being below average. The students that score within the standard score are considered average and the students who score above the standard score are considered above average. Within the school systems, standard scores are extremely important, and are used to determine information that needs to be taught. It also helps in indicating the method of teaching that seems more beneficial to a student based on their ranking on the standard score. Currently, there are numerous tests standardized tastings that formally utilized to allow educators to understand the level at which students are located. This knowledge works as a source of implementing necessary changes in the classroom that can improve student performance. <span style="font-family: 'Times New Roman','serif'; margin: 0in 0in 0pt;">

Statistical analysis is when large amounts of quantitative data is collected, examined, summarized, manipulated and interpreted to determine patterns and relationships. With the amount of data that we receive from the results of standardized testing, statistical analysis is something we encounter often in the classroom. When we receive the data from the state, it is not organized. Through statistical analysis, we can find trends in the data that will help us improve our scores on the next test. For example, through statistical analysis, we might find that students with low socio-economic status might be struggling on the tests. Knowing this data, will help us focus on those students by providing tutoring opportunities in their communities, or we might reach out to the parents to see how we can help their child succeed.
 * Statistical Analysis**

**Subjective Data** <span style="font-family: 'Times New Roman','serif'; margin: 0in 0in 0pt;">Subjective data is data that is based solely on opinion and not backed by empirical data. Subjective data is often looked at as being qualitative in nature as opposed to its counterpart which is quantitative data. Manuel (1973) conducted a study on the evaluation of affective experiences on programs of the arts in Minneapolis public schools. The data used in this study was subjective in nature as it consisted of journal entries from participants who ranked their daily feelings after art based activities. Participants were also allowed to include additional information on feelings aside from the scale required for measurement of feelings towards art programs (Manuel, 1973). Subjective data has been the foundation of education for as long as the discipline has been around. Today more objective or empirical based data is required as there is much research that supports the idea that more empirical data may result in more efficient practice in terms of teaching and learning. Subjective data is important because it focuses on the individual quality of persons. Students love to express themselves and using subjective data is the best avenue to take in terms of collecting this type of data from children.

Validity is the strength of your results or conclusion. The term is often seen in association with the word reliability. Validity is important in all disciplines, especially when it comes to research and writing. Validity will determine how valid your results or point of view is. In education this is important because of state standardized test that students have to take. In the classroom teachers should be knowledgeable of validity for the sake of teacher produced assessments.
 * Validity**

<span style="display: block; font-family: 'Times New Roman','serif'; margin: 0in 0in 0pt; text-align: center;">References

<span style="font-family: 'Times New Roman','serif'; margin: 0in 0in 0pt;">Bath, K. E., Daly, D. L., & Nesselroade, J. R. (1976). REPLICABILITY OF FACTORS DERIVED FROM INDIVIDUAL P-TECHNIQUE ANALYSES. Multivariate Behavioral Research, 11(2), 147. Retrieved from EBSCOhost.

//Dictionary.com//.(2011) Retrieved from [], on March 23, 2011.

<span style="font-family: 'Times New Roman','serif'; margin: 0in 0in 0pt;">Graesser, A. C., Singer, M., & Trabasso, T. (1994). A theory of inference generation during text comprehension. Psychological Review, 101, 371–395.

<span style="font-family: 'Times New Roman','serif'; margin: 0in 0in 0pt;">Jeong, H., Songer, N. B., & Lee, S. (2007). Evidentiary Competence: Sixth Graders' Understanding for Gathering and Interpreting Evidence in Scientific Investigations. Research in Science Education, 37(1), 75-97. Retrieved from EBSCOhost

<span style="font-family: 'Times New Roman','serif'; margin: 0in 0in 0pt;">Lankshear, C. and M. Knobel. (2004). //A Handbook for Teacher Research: from design to implementation//. Berkshire: Open University Press.

<span style="font-family: 'Times New Roman','serif'; margin: 0in 0in 0pt;">Lind, A. (1978) Reactions to procedural models for adjudicative conflict resolution: a cross national study. The journal of conflict resolution 22 (2), pp. 318-341 Retrieved from []

<span style="font-family: 'Times New Roman','serif'; margin: 0in 0in 0pt;">Lutz, W.J., S.A. Keenan. (1999) Research Glossary. Ohio Department of Mental Health. Retrieved from [].

<span style="font-family: 'Times New Roman','serif'; margin: 0in 0in 0pt;">Manuel, J. (1973). The quantification of subjective data for evaluation of affective experiences. Retrieved from []

<span style="font-family: 'Times New Roman','serif'; margin: 0in 0in 0pt;">Meier, E.B. (2005). Situating technology professional development in urban schools. Journal of Educational Computing Research, 32(4) 395-407. Retrieved from []

<span style="font-family: 'Times New Roman','serif'; margin: 0in 0in 0pt;">Mouza, C. (2008). Learning with laptops: implementation and outcomes in an urban, under-privileged school. Journal of Research on Technology in Education, 40(4), 447–472. Retrieved from []

St. Martins Press. //Encarta World English Dictionar//y. Retrived from encarta.msn.com on March 22, 2011. <span style="font-family: 'Times New Roman','serif'; margin: 0in 0in 0pt;">Trochim, W.M.K. (2006). The t-test. Retrieved from [] on February 24, 2011.

Vellutino, F. R. (2001). Further Analysis of the Relationship Between Reading Achievement and Intelligence: Response to Naglieri. //Journal of Learning Disabilities//, 34(4), 306. Retrieved from EBSCO//host//.

<span style="font-family: 'Times New Roman','serif'; margin: 0in 0in 0pt;">Vondracek, M. 2005. Improving student comprehension by thinking multiple ways about a topic. The Physics Teacher 43: November.

<span style="font-family: 'Times New Roman','serif'; margin: 0in 0in 0pt;">Group B Members: <span style="font-family: 'Times New Roman','serif'; margin: 0in 0in 0pt;">Megan Dunivant <span style="font-family: 'Times New Roman','serif'; margin: 0in 0in 0pt;">Delonta Davis <span style="font-family: 'Times New Roman','serif'; margin: 0in 0in 0pt;">Amanda Cerny <span style="font-family: 'Times New Roman','serif'; margin: 0in 0in 0pt;">Naomi Ratz <span style="font-family: 'Times New Roman','serif'; margin: 0in 0in 0pt;">Sharlyn Robin