Mitigating Factors in Teacher Burnout: Methodology

Chapter 3 - Methodology

The methods that will be implemented in this study are designed to investigate the relationship between teacher burnout and the principal’s leadership style, teacher self-efficacy and the principal’s leadership style, teacher burnout and self-efficacy, years of experience and teacher burnout and years of experience and teacher self-efficacy. Appropriate methods for investigation of these questions will be deemed to be an analysis of results from quantitative instruments designed to measure teacher burnout, teacher turnover rates, teacher self-efficacy and leadership styles of principles in the associated schools. In addition, a survey to record demographics will be developed for the purposes of the study.

Participants

Quantitative research can make inferences about phenomena present in a much larger group based on a study of a representative sample, or much smaller group of individuals (Gall, Borg, and Gall, 1996, p. 220). There are a number of different means of studying the phenomenon based on the perceived likelihood that the study’s participants will or will not be exactly what the research requires. The study’s participants will be elementary school teachers who work in a large urban northeast school district. The participants in this study will include at least 40 elementary school teachers who work in a large Northeastern urban school district. Participation in the study is voluntary, and the participants will complete the surveys on an anonymous basis. The study targets a random sampling of teachers who are currently employed in public schools. Monetary compensation will not be offered for participation.

Types of samples. Probability sampling increases the likelihood of obtaining participants that are representative of the population. These representative samples provide credible results because they draw on a very large group of people and reflect the characteristics of the population from which they are selected. When studying a phenomenon among teachers (as is the case here), a researcher may not be able to obtain a random or stratified sample because it could be too time consuming or expensive. Additionally, researchers may not be concerned with generalizing results to a larger population. They may just want to track the likelihood of a particular occurrence among a small segment of the population. Non-probability samples are not very valid but their validity can be increased by using approximate random selection and eliminating as much bias as possible (Phillips, 1998).

As such, there are two types of probability samples: random and stratified. This study will use a random sample which means that the study will consider every individual in the population of interest as being a significant part of the study and as likely to be selected as the next person. However, random samples select participants from the whole number of available participants rather than randomly approaching people to elicit their participation (Phillips, 1998).

According to Phillips (1998), experiments that identify large to moderate correlations should include at least 40 participants and can be seen as significant if they include at least 200 or more, depending on the study. Smaller coefficients may require several hundred participants or thousands of participants. Three hundred participants scattered at 20 schools within the district will be offered the opportunity to participate in the study. The number of respondents will provide an appropriate sample size for an effective analysis of the problem. The study will not be deemed a significant and representative sample unless 40 or more teachers respond to a survey proctored online using Survey Frog.

Instruments

The Maslach Burnout Inventory Educator Survey MBI-ES will be used to measure teacher burnout. The Teacher Self Efficacy Scale TESES will be used to measure teacher self-efficacy. The Multifactor Leadership Questionnaire (MLQ) will be administered to assess principal leadership styles as perceived by the teachers whom they lead. Gall, Borg, and Gall (1996) state that the instruments selected must provide quantifiable data as a requirement for an effective correlation study. The listed instruments are previously validated research tools and meet this requirement. A demographic survey created for the purpose of this study will be used to gather participant data.

Research Goals

In attempting to choose a research approach, it is helpful to consider the study’s goals in investigating behavior. The goal of quantitative research is to “collect facts” on human behavior, which are then analyzed to provide verification and elaboration on theory to allow researchers to find causes and make predictions of human behavior (Bogdan & Biklen, 1998). Other goals might be stated as attempting to show relationships between variables, statistical descriptions, the establishment of facts (Bogdan & Biklen, 1998), control and prediction (Gage, 1989), validation of instruments (Krathwohl, 1998), and testing of hypotheses (Gall, Borg & Gall, 1996). Results from this educational research are expected to provide information that will be helpful in modifying educational environments to provide for greater success in reaching educational goals.

Questions and Hypothesis

In qualitative studies, the research questions or hypotheses are formulated before beginning the research. In order to do this, the researcher investigates the results and findings of other studies and the theories generated and then uses this information to form a new hypothesis to test. The hypothesis is the researcher’s informed prediction of what the results of the study will show before the research is carried out (McMillan, 2000). This type of research design is normally formal, structured and specific, with a detailed outline of operations (Bogdan & Biklen, 1998), as the aims of the study are clearly outlined before the research begins.

Data and Data Analysis

Quantitative studies require the collection of data that can be analyzed by quantitative methods. This includes the results from questionnaires, surveys, tests, and similar instruments, which are collected in numerical or related forms that can be easily tabulated to produce numerical data. The data must be quantitative, including quantifiable coding with measures, counts and operationalized variables (Bogdan & Biklen, 1998). Pre-investigated theories and concepts are used to determine what data will be useful for the research, and thus what should be collected. The numerical data provides a representation of the educational environment which can then be analyzed to show relationships among the variables.

Methods of data analysis for quantitative studies include statistical analysis and the use of deductive reasoning to solve problems related to the analysis. Statistical inference procedures may then be used to generalize the findings from the sample to a previously defined population. The results of quantitative research are normally presented in an objective summary (Gall, Borg & Gall, 1996).

Procedure

Research Design. Moreover, on the eve of the full implementation of the Common Core Curriculum, the members of this workforce are pressed to develop strategies and relationships which will result in professional success. Participation in the study is voluntary. The participants will take the study surveys on an anonymous basis. The study will target a random sampling of teachers who are currently employed in public elementary schools.

Compensation will not be offered for participation. According to Phillips (1998) experiments that attempt to identify large to moderate correlations among a general population this size should include at least 40 participants while accurate identification of smaller coefficients may require several hundred participants. Three hundred participants will be offered the opportunity to participate in the study. This amount of respondents will provide ample data for effective analysis of the problem. The participants will be scattered at 20 schools within the district.

If less than 40 respondents participate in the study, it cannot be deemed or truly representative. While this non-probability sample is not ideal, a non-probability sample can be improved by eliminating bias and using a “quota sample” which allows the researcher to deliberately insure the inclusion of the population one wishes to study in the sample size. The proportion of individuals one desires to study versus those one does not desire to study may be skewed in favor of the phenomena in question and this may cause a shift in the validity of the results, however, a quota sample gives researchers an opportunity to ensure that the population correlates with the issue in question.

The study will use a correlation design to analyze relationships between variables. Correlation studies may show positive, negative or no correlation, with the correlation coefficient a measure of the correlation strength. Correlation research is typically used because it can be highly predictive. Although correlation cannot be used to infer causation between variables, it does suggest a potential relationship that may then be investigated in future studies.

Gall, Borg, and Gall (1996) note that the predictive aspect of the correlation design is useful in that it allows for the development of cause and effect relationship norms. In other words, analysis of the possible correlation between teacher burnout, self-efficacy and principal leadership styles in this study is expected to suggest how these factors might influence behavior in the future over a larger geographic area. More importantly, the research can be used to shape systems in the future which create positive environments for these factors within organizations. Phillips (1998) states that conducting studies that attempt to find correlation patterns is not a trivial task if performed effectively.

Data collection. Each school will be given a color code for data processing, eliminating the use of school names. Permission will be obtained from the Office of Academic Achievement of the school district to conduct the study. Upon receipt of approval, school principals within the district will be contacted with requests for permission to distribute surveys to 15 teachers at their location. The surveys will be sent to a designated school site representative for placement in teacher mailboxes. Teachers received a letter of introduction, the four surveys, a self-addressed stamped envelope for return to the researcher and a candy bar as a reward for participation. The letter of introduction asked for a turnaround date of two weeks for return to the researcher, and the survey questionnaires will be returned anonymously, with only the school color coding for identification.

Data analysis. The researcher will utilize the Microsoft Excel Data Analysis Pack to analyze the coded data. Descriptive statistics will be calculated to show the percentages and frequency distributions of the demographics collected in the demographic survey. For the correlation analysis of the principal’s leadership style, teacher burnout, and teacher efficacy surveys, leadership style will be treated as the predictor while teacher burnout and self-efficacy will be treated as the criterion variables. The Microsoft Excel Data Analysis Pack will also be used to generate appropriate charts and tables for presentation of the data.

Research Design. Moreover, on the eve of the full implementation of the Common Core Curriculum, the members of this workforce are pressed to develop strategies and relationships which will result in professional success. Participation in the study is voluntary. The participants will take the study surveys on an anonymous basis. The study will target a random sampling of teachers who are currently employed in public elementary schools.

Compensation will not be offered for participation. According to Phillips (1998) experiments that attempt to identify large to moderate correlations among a general population this size should include at least 40 participants while accurate identification of smaller coefficients may require several hundred participants. Three hundred participants will be offered the opportunity to participate in the study.

This number of respondents will provide ample data for an effective analysis of the problem. The participants will be scattered at 20 schools within the district. If less than 40 respondents participate in the study, it cannot be deemed or truly representative. While this non-probability sample is not ideal, a non-probability sample can be improved by eliminating bias and using a “quota sample” which allows the researcher to deliberately ensure the inclusion of the population one wishes to study in the sample size. The proportion of individuals one desires to study versus those one does not desire to study may be skewed in favor of the phenomena in question and this may cause a shift in the validity of the results, however, a quota sample gives researchers an opportunity to ensure that the population correlates with the issue in question.

Assumptions

The quantitative research paradigm comes with a set of assumptions. These include the fact that quantitative research attempts to understand the causes or facts related to phenomena and excludes the subjective factors related to individuals or situations (Reichardt & Cook, 1979).

The method tends to exclude values (Stanfield, 2006) and leans heavily to positivism and trust in the benefits of hypothetical-deductive procedures (Morales, 1995). Quantitative researchers expect that their data collection and analysis will produce repeatable scientific results, even though the method fails to account for subjective decisions that take place in the various stages of the design and research process (Onwuegbuzie & Leech, 2005).

Another assumption of quantitative research methods includes the expectation that what happens in the environment under study can be generalized to other environments (Gall, Gall & Borg, 2003). Quantitative methods also assume that science will produce a superior result in understanding and predicting human behavior and that failures of the scientific method will produce inferior results (Stanfield, 2006). These assumptions mean users of the paradigm expect that experience, reality, and situations are quantifiable and measurable and that what is not measurable should be discarded. If the situation under study is measured, validated and generalizable, then users of the method expect it can be generalized to all similar populations, as it is independent of personal experience (Gall, Gall & Borg, 2003).

Quantitative methods include the expectation that the researcher is separated from the process of data collection and so will have less effect or influence on responses than researchers using qualitative methods (Gall, Gall & Borg, 2003). Quantitative study has provided much of the body of published research data, and it is expected that the processes, rules and guiding principles are well-known and available for use in research designs. This provides consistency in procedures and processes for research studies, whether they are causal-comparative or experimental. Quantitative studies are often expected to be replicable and the instruments, once validated, are used in further studies based on the rigor of creating effective measures. These instruments can often be used in different educational or social contexts where they remain valid and reliable (Creswell, 2003).

Other assumptions are that quantitative studies can test and validate theories that are already constructed about how and why events like behavior happen. Data collection in quantitative studies may be quickly collected, and the data is precise and numerical. The results of quantitative studies are easily generalizable when appropriately collected through random sampling (Johnson & Onwuegbuzie, 2004). Quantitative data is also expected to provide solid, reliable answers, unlike opinions or other subjective interpretations (Ratnesar & Mackenzie, 2006).

Limitations

The main limitation of correlational studies is that they may suggest relationships between variables, but they can not prove a causal relationship. This means that the correlations coefficients found through data analysis in this research study only indicate that there might be a relationship, but can not show that teacher burnout, for example, is caused by the leadership style of the principals in the sampled schools. In the same way, the correlation coefficients can not show that teacher self-efficacy is caused by the leadership style of the principal in their school, as other variables, such as utilizing leadership development resources, might also play a role. Or, for example, stress caused by the adoption of Common Core Standards.

Also, it would be virtually impossible to get every teacher in this large public-school system to voluntarily participate in the study, although 100 percent participation would be ideal. Additionally, the number of available elementary school teachers across the district who might participate versus the number of PK-12 teachers who might participate poses a numerical limitation that greatly impacts the study’s efficiency. Because the study focuses solely on elementary school teachers at the exclusion of middle and high school teachers, it will be more difficult to get participants.

Another limitation is that the research will include limitations on the size and composition of the sample. Although teachers will be chosen randomly to participate, many will not respond, making the respondents, to a certain extent, self-selecting. The study focuses solely on elementary school teachers at the exclusion of middle and high school teachers, and the sample used only non-charter, public school teachers. Data from charter schools and private school employees will be not collected or analyzed during this study. There is a great risk that the sample group will be too large or small for the effective analysis of the issues at hand.

Correlation is an empirical type of study, seeking to describe data. The results can be used to theorize why a phenomenon occurs in the way it does (Sechrest & Sidani, 1995), but it lacks any narrative explanation of the situations that generated the data. Sometimes these explanations might be useful in understanding the context of the data and in suggesting additional directions of research. Although quantitative research claims to be value and bias-free, some scholars argue that complete elimination of bias is impossible because researchers can never be completely neutral or value-free in any circumstances (Stanfield, 2006).

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