Recent draft :Topic- A draft proposal for a small-scale investigative study..

Topic: A draft proposal for a small-scale investigative study……

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Pages:7, Double spaced( 1925 words)

Sources:25

Order type:Research Proposal
Style: APA

Language:English (U.S.)

Order Description

Think of yourself as a teacher-researcher in the area of instrumental / vocal music performance. Using an instrumental or vocal music teaching scenario related to your performance area (a single instrument or family of instruments or a mixed instrument group), develop a proposal for a small-scale piece of research that you as an instrumental / vocal music teacher might undertake to better inform your own teaching practice. Historical and philosophical research are not suitable for this project.

Topic: A draft proposal for a small-scale investigative study

Details: Think of yourself as a teacher-researcher in the area of instrumental / vocal music performance. Using an instrumental or vocal music teaching scenario related to your performance area (a single instrument or family of instruments or a mixed instrument group), develop a proposal for a small-scale piece of research that you as an instrumental / vocal music teacher might undertake to better inform your own teaching practice. Historical and philosophical research are not suitable for this project.

Assessment criteria:

• Author Information.
• A short description of the background and context of the proposed research (e.g. details of your music performance and teaching background, your career direction, etc.)
• Introduction
• What is the purpose of your study?
• What is your research problem (what problem would you like to fix or understand further)?
• Why is your study needed?
• What is your research question? What are your hypotheses (if applicable)?
• Literature Review
• A summary of pre-existing research on this topic.
• Has your proposed topic been investigated previously? If so, were the findings consistent?
• How is your topic different from the previously researched topics?
• Methods
• What type of study will you conduct (quantitative or qualitative, what type)? Describe your study design.
• What is the research setting?
• What is the population you want to study?
• How will you choose and draw your sample?
• What sample size would be suitable for the project?
• How will you contact the potential participants?
• What kind of research instrument(s) will you use and what types of questions or measures will it include?
• How will you follow up the main study participants (if applicable)?
• Data Analysis / Results
• How would you analyse the data if you were to conduct the study? (Show understanding of the different analysis techniques appropriate for quantitative and qualitative studies.)
• Conclusion / Discussion
• Discuss the potential applications of the findings. How would the findings assist your teaching?
• How could the findings be applied more generally to music education practice?

In addition, students must:

• Reference prescribed and recommended reading for this subject as well as additional readings. Minimum of 15 peer-reviewed sources (e.g. chapters from textbooks, article from scholarly and professional journals, curriculum documents, resource materials) is required. For your references, you may use articles prescribed for the lectures, but you CANNOT use my lectures as the sources.
• Use appropriate terminology and literary style.
• Use APA (6th ed.) format in both the body of the text and in the reference list. See http://www.lib.unimelb.edu.au/recite/citations/apa6/generalNotes.html for more information.
Proofread the paper.

Quantitative Research: Data

NON-PARAMETRIC = Categorical – naming something
Nominal – only measured in whole amounts, cannot compare quality – assign numbers to code data, not compare (grade, school, gender). Dichotomous variables – male/female, 1st/2nd grade.
Ordinal – can rank, compare but not measure the quality of difference between the items. Order of participants, ranking, fail/pass. How much faster was the winning competitor compared to the 2nd, 3rd place?

PARAMETRIC = Numerical – counting something
Discrete – can only be measured in distinct and separate numbers. Can count them. Whole numbers – not ratios. Number of students, number of pieces, number of instruments in the classroom

Continuous – measure data. Height, % for the grade, average of the grades in class. Can use fractions, portions – not like in discrete data

Independent and Dependent Variables

IV (experimental variable, treatment)  DV (criterion variable, posttest).

One or many IVs, in this class – only one DV

Multiple DVs – MANOVA

Measures of Central Tendency
Represent data from a group by one score -> compare to other groups’ scores.

Mean – average. Use: normal distributions, parametric/numeric data.
Predict that an average person from a group would most likely get a mean score.
Deviation – difference between a score and the mean
1, 2, 3, 3, 4, 5 – Mean: 18/6=3. 1,1,5,5 – Mean: 12/4 = 3

Median – middle point of all scores – 50% of scores are above, 50% are below. Use: highly skewed distributions, ordinal (ranking) data. How did students score on the test out of 5 points, with 3 being a pass? 2, 2, 2, 2, 3, 5, 5
Mean: 3 – all passed. Median: 2, meaning that at least 50% of sts failed. Use when talking about house prices, income.

Mode – most frequently occurring score. Summarises nominal scores. Use: what is your students’ favourite piece?
1, 2, 2, 2, 2, 2, 3, 4, 5, 6, 6, 7
Standard Deviation
Range – distance between the extreme scores. Highest-lowest=range

Variance – how different scores are from each other, how much they differ. 1,2,2,3,3,3,4,4,4 —- 1,14,19,35,76,92

SD = Variance2. Large – widely distributed data, small – skinny distribution. 1SD = appr. 1/6 of range

Variance and Standard deviation show how much the scores differ from the mean on average

-3 to +3, 0=mean

Correlation
Pearson’s r – linear relationships, interval and ratio scales

Spearman rs – rank-order correlation coefficient. E.g. rankings of performance between judges.

Do not try to describe nonlinear or curvelinear relationships with coefficients (exercise and wellbeing)
Multiple Regression
Multiple regression – several independent variables predict one or more dependent variables.

Independent variables / predictor

Dependent variable / criterion

Multiple R – multiple correlation coef describes the relationship between all IVs together and the DV.

Squared Multiple R – multiple regression coef, predicts the DV score based on all IVs.

How well the IV’s predict the DV, and which one predicts the best.

Example ->
Multiple Regression
The Influences of Teacher Delivery and Student Progress on Preservice Teachers’ Perceptions of Teaching Effectiveness. Jessica Napoles, Rebecca B. MacLeod. Journal of Research in Music Education (2013).
Abstract
The purpose of this study was to examine how teacher delivery and student progress influenced preservice teachers’ perceptions of overall teaching effectiveness. Experienced teachers (n = 6) were videotaped teaching mini applied lessons under four conditions: (a) high teacher delivery and more student progress, (b) high teacher delivery and less student progress, (c) low teacher delivery and more student progress, and (d) low teacher delivery and less student progress. Preservice teachers (n = 75) viewed these teaching excerpts and rated each for teacher delivery, student progress, student musicianship, teacher knowledge of subject matter, and overall teaching effectiveness. Participants rated teachers with high delivery as more effective than teachers with low delivery, irrespective of student progress. There was a moderate positive correlation (r = .53) between perceptions of teacher delivery and student progress. Results of a multiple regression analysis revealed that teacher delivery was the best predictor of perceptions of overall teaching effectiveness, followed closely by student progress.
Nonexperimental Design
Purpose: to describe phenomena or to investigate relationships among variables.

Descriptive – use frequencies, percentages, averages, and other statistics to describe the data.

Survey research.

Correlational studies – use at least 2 measured variables for each participant. Correlation coefficients indicate the direction and magnitude of the relationships.

Survey
Can be used in a variety of research studies – quant and qual
Questionnaires are the principal means of data collection.

Cross-sectional – a few groups surveyed at one time (5th graders, 8th graders, 12th graders etc.) Compare, describe populations
Longitudinal – same subjects on two or more occasions. Time consuming, costly, high attrition rates
Successive independent samples – different groups representative of the population over a period of time. Study how attitudes in population change over time. Same population, different participants

Reliability – the same results under the same circumstances. Measured through test-retest (short period of time)
Internal consistency – how consistently statements measure a variable (Cronbach’s alpha). Review items that ‘jump out’

Ex Post Facto
Causal-comparative research – establish a cause-and-effect relationships among variables. “Experiment” in the past.

Look at the past experience of the subjects that is considered “treatment” and compare them to groups that had no or different “treatment.”

IV has already occurred, it was not manipulated by the researcher.

No random assignment of subjects because the treatment has already occurred.

Ex Post Facto
e.g. Attending small or large high school x College GPA
Important to develop a good theory that would take into consideration other variables that could have affected students’ outcomes (e.g., school size, curriculum offered, socioeconomic status, geographical position, quality of teachers.)

Path analysis – analyses
direct and indirect
relationships among
a set of variables.

Source: Sichivitsa, V. (2007)
http://rsm.sagepub.com/content/29/1/55

Internal and External Validity
Internal validity – are the conclusion of the study valid? Is the treatment causing the change, is there enough evidence to support the claim?

Generalizability = external validity – are the results generalizable to the population?

References
Heiman, G. W. (2011). Basic statistic for the behavioral sciences (7th ed.). Belmont, CA: Wadsworth, Cengage Learning.

Sichivitsa, V. (2007). The influences of parents, teachers, peers and other factors on students’ motivation in music. Research Studies in Music Education, 29(1), 55-68.