Practical Statistics for Educators - User contributions [en]

Main Page

2019-12-17T04:08:07Z

Sullivan.joseph2050: /* Modules */

<big>'''Practical Statistics for Educators'''</big>
edited and maintained by Frank LaBanca, EdD

----

== Philosophy ==

Quantitative statistical analyses can be intimidating for many educators pursing an advanced academic degree. The thought of computational math can sometimes trigger unwarranted fears.

Here, we approach statistics from a straightforward conceptually-based perspective. Our goal is to collaborate and provide insight for statistics that make them meaningful tools in the educational arena.

Each "module" corresponds with the topics presented each week, and will expand as the course progresses. A topical outline can be found @ [http://docs.google.com/Doc?id=dfqvtcqp_46hhzzcsgt ]

Comments and edits are welcome and encouraged! Please give yourself credit as you contribute. At the end of a section you insert please add the following in italics:
''contributed by <your name>''
If you are modifying content, add the following under the contribution line:
''modified by <your name>'' We are glad to accept as many modifications as necessary to give the most meaning to each section. As we asynchronously socially construct knowledge together, we can recognize the accomplishments and contributions of each writer.

''contributed by Frank LaBanca, EdD''

== Contributions ==

Our contributors [[contributions here]].

Please submit your contribution at [https://forms.gle/PBaVrxFffbk5CKgg8]

== Modules ==

1.1 [[The Greek Alphabet]] and its significance in statistics

1.2 An introduction to probability PowerPoint @[http://docs.google.com/Presentation?id=dfqvtcqp_97wcrbtsn]

2.1 [[Types of Data]]

2.2 [[Visualizing Data]]

2.3 Visually representing data PowerPoint @ [http://docs.google.com/Presentation?docid=dfqvtcqp_27dwth2zz2#]

2.3.1 Table 2 from LaBanca dissertation @ [http://docs.google.com/Doc?id=dfqvtcqp_25cb5pqcfw]

2.3.2 Cool graph of movie box office from NY Times [http://www.nytimes.com/interactive/2008/02/23/movies/20080223_REVENUE_GRAPHIC.html#]

2.3.4 [[Histograms]]

2.3.5 Scatterplots YouTube @ [http://youtu.be/HFuU1uxJ1tQ]

2.4 [[Shapes of distribution]]

2.5 Survey of Attitudes Toward Statistics (SATS) Data Set @ [http://spreadsheets.google.com/pub?key=pmUxljSzLg7PokF_oAoCRUg]

2.6 [[Data Screening]]

3.1 [[Central Tendency]]

3.1.1 Central Tendency and Normal Distribution PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_47dfhfr4nw ]

3.1.2 Central Tendency YouTube @ [http://youtu.be/Fn4z8RDpwDY]

3.2 [[Interquartile ranges]]

3.2.1 [[The Box Plot]]

==BOXPLOTS==

Boxplots can be used to explore distribution of one continuous variable for the whole sample or, alternatively, the researcher can search for scores to be disagregated by different groups. The output from boxplot gives the researcher a lot of information about the distribution of the continuous variable and the possible influence of the categorical variable. A boxplot allows the researcher to inspect a pattern of scores within each group and allows visual inspection of the differences between groups (Pallant, 2016).

''Contributed by Joseph W. Sullivan''
3.2.2 Interpreting a Box Plot - video [https://www.youtube.com/watch?v=b2C9I8HuCe4]

3.3 [[Standard deviation]]

3.3.1 [[Identifying percentile ranks and scores based on standard deviation]]

3.3.1.a [[Practice Identifying percentile ranks and scores based on standard deviation]]

3.4 [[z-scores]]

4.1 [[Percentile Rank]]
4.1.1 Areas under the standard normal curve for z values @ [http://spreadsheets.google.com/pub?key=pmUxljSzLg7NstEjJ40jJOQ]

4.1.2 z scores corresponding to divisions of the area under the normal curve @ [http://spreadsheets.google.com/pub?key=pmUxljSzLg7P6SVNiBdWbEg]

4.2 Conversion of data PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_98chfzg2tf]

4.2.1 Descriptive analysis of USRT data @ [http://docs.google.com/Doc?id=dfqvtcqp_61db6smpdm ]

4.3 [[Normal Curve Equivalent scores]]

4.3 [[Standard Error of Measurement]]

4.4 [[Confidence Intervals]]

4.4 z score machine @ [http://spreadsheets.google.com/pub?key=pmUxljSzLg7N8oLJZwmr3Zw]

5.1 [[Pearson r]]

5.2 [[Rules of thumb for interpreting the size of a correlation coefficient]]

5.3 Critical values for the correlation coefficient @ [http://spreadsheets.google.com/pub?key=pmUxljSzLg7MkuRpIgceTRQ]

5.4 [[Spearman rho]]

5.5 Correlation PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_134hsxg7td7]

5.6 [[Writing samples for correlations]]

6.1 [[Inferential Statistics Definition]]

6.2 [[Sampling]]

6.3 [[Sampling distributions]]

6.4 t test PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_153gr9f3hgd]

6.4.1 t -t test video [https://www.youtube.com/watch?v=N2dYGnZ70X0]

6.5 Sample data set @ [http://wolfweb.unr.edu/homepage/liu/stat/help/help.htm]

6.6 Critical values for t @ [http://spreadsheets.google.com/pub?key=pmUxljSzLg7OZzyZeHg9MIA]

6.7 Helpful Tutorial for Running a t-Test in Excel @ [https://www.rwu.edu/sites/default/files/downloads/fcas/mns/running_a_t-test_in_excel.pdf]

7.1 [[Effect size]]

7.1.1 Effect size calculator @ http://www.campbellcollaboration.org/resources/effect_size_input.php

7.1.2 [[Rules of thumb for interpreting effect sizes]]

7.2 Effect size PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_204f42f67dx ]

7.3 [[Hypothesis testing]]

7.4 Hypothesis testing PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_23295tm65dx]

7.5.1 Hypothesis testing template for a correlation @ [http://docs.google.com/Doc?id=dfqvtcqp_174ccchz4ds]

7.5.2 Hypothesis testing template for a t test @ [http://docs.google.com/Doc?id=dfqvtcqp_175hjcdsjff]

8.1 [[Type I and Type II Errors]]

8.2 Type I and Type II Errors PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_258wcq56rdv]

8.3 [[Levene's p versus the test statistic p]]

8.4 [[Analysis of Variance]]

8.5 ANOVA PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_265ckd9j9dv]

8.6 [[ANOVA Case study]]

8.7 ANOVA video [https://www.youtube.com/watch?v=ITf4vHhyGpc]

8.8 Critical values for the F statistic @ [http://www.sussex.ac.uk/Users/grahamh/RM1web/F-ratio%20table%202005.pdf]

8.9 [[Rules of thumb for interpreting effect sizes of ANOVAs]]

9.1 Post Hoc test PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_284dbhghdc9]

9.2 [[Selecting a Post Hoc test]]

9.3 Hypothesis testing template for ANOVA @ [http://docs.google.com/Doc?id=dfqvtcqp_295ckngxdgj]

10.1 [[Chi square]]

10.1.1 Chi square video [https://www.youtube.com/watch?v=VskmMgXmkMQ]

10.2 [[Example for calculating chi square]]

10.3 Critical values for chi square @ [http://spreadsheets.google.com/pub?key=pmUxljSzLg7OhBVHQWoHTIQ]

10.4 [[Chi square analysis description/sample writing]]

10.5 Chi square PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_297dhg685g8]

11.1 [[Beyond the ANOVA]]

11.2 Beyond ANOVA PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_307cmrtpxg3]

11.3 2-way ANOVA PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_325hrt86ggt]

11.4 2-way ANOVA template @ [http://docs.google.com/Doc?id=dfqvtcqp_372599pr6w6]

11.5 1-way ANOVA Annotated SPSS Output @ [https://drive.google.com/file/d/1i0kIWgmXLCEIIIYCSqh2JS9Th3T_pyRC/view?usp=sharing]

11.6 2-way ANOVA Annotated SPSS Output @ [https://drive.google.com/file/d/1wX4xhQa7KGCd1Hey7VfX33Y1uT6QHdEu/view?usp=sharing]

12.1 [[MANOVA]]

12.2 [[Homogeneity vs Homoscedacity]] (Levene vs Box's M)

12.3 [[Post Hoc ANOVAs for MANOVA]] (univariate)

12.4 [[Post Hoc Discriminant Analysis]] (multivariate)

12.5 [[Covariates]]

12.6 [[MANCOVA]]

12.7 MANOVA Annotated SPSS Output @ [https://drive.google.com/file/d/1GiErYfmCdiNQlCps3anF4Bu3C_iYS7oD/view?usp=sharing]

12.8 MANCOVA Annotated SPSS Output @ [https://drive.google.com/file/d/1TodMQy4vQ4eHStSIAevATOuJuFg9KUTD/view?usp=sharing]

13.1 [[Multiple Regression Analysis]]

13.1.1 [[Collinearity]]

13.2 [[Multiple Linear Regression]]

13.3 Reading the MLR Output: An annotated output [https://drive.google.com/file/d/141RNyYNnuDDTNvHYi4fE8EF8qWmal1aa/view?usp=sharing]

13.4 MLR Annotated SPSS Output @ [https://drive.google.com/file/d/1SsPL1YD4VYguxLtwqM_R7GFBD5RsG9H6/view?usp=sharing]

14.1 Internal Consistency Reliability[https://docs.google.com/document/d/18K16I8u9sbwpUhW9nIENF4x9wDXAy-4wFuBp6a0AljA/edit]

==Internal Consistency Reliability==

"Internal consistency reliability relates to the extent to which all the variables that make up the scale are measuring the same thing" (Muijs, 2011, pg. 217). ''Contributed by Joseph W. Sullivan''

14.2 Cronbach's Alpha[https://docs.google.com/document/d/1_eyXOcFrBcDSctM27a9T2kUlx9D8TidV_YTHk-wvTu0/edit]

14.2.1 [[Cronbach's Alpha Values]]

Cronbach's Alpha is a measure of the correlations between all the variables that make up a scale. The concept behind this measure is to determine if items measure the same concept. If so, they will be highly correlated and have a high alpha, indicating a high level of internal consistency. However, the more items in a particular scale, the higher the alpha tends to be, even if the items don't measure the same thing. It is suggested that the researcher should also run a factor analysis to strengthen the reliability of the scale (Muijs, 2011).

''Contributed by Joseph W. Sullivan''

14.2.2 Cronbach's Alpha in SPSS [https://www.youtube.com/watch?v=Kz8OdR6lV44]

== Applied Research Designs ==

15.1 [[Instrumentation]]

15.2 [[Limitations]]

15.3 [[Practice determining the stat]]

== Getting started ==
* [http://www.mediawiki.org/wiki/Manual:Configuration_settings Configuration settings list]
* [http://www.mediawiki.org/wiki/Manual:FAQ MediaWiki FAQ]
* [http://lists.wikimedia.org/mailman/listinfo/mediawiki-announce MediaWiki release mailing list]

MANOVA

2019-12-17T03:57:50Z

Sullivan.joseph2050: /* Why MANOVAs are a good test for dissertations */

Multivariate variate analysis of variance (MANOVA) is the statistical procedure of comparing the means of several groups rather than a single group as you would find in an ANOVA. It is appropriate to use a MANOVA if the IV has 2+ levels and there are 2+ DV. Assumptions for use of MANOVA include: normal distribution. linearity, homogeneity of variances and homogeneity of variances and covariances. [http://userwww.sfsu.edu/efc/classes/biol710/manova/MANOVAnewest.pdf].

''contributed by Raymond Manka''

For example, we may conduct a study where we look at math achievement based on standardized test scores and ELA acheivement based on standardized test scores between students in different socioeconomic groups (low, moderate, high). The two dependent variables would be math achievement vased on standardized test scores and ELA achievement based on standardized test scores. The independent variable is the socioeconomic status with three levels: low, moderate, and high. (Based on a case scenario provided by Dr. Frank Labanca)

Instead of a univariate ''F'' value, we would use a multivariate ''F'' value Wilk's λ.

''contributed by Mary Fernand''

Prior calculating a MANOVA (or other statistics) in SPSS, it may be necessary to "clean the data" in order to obtain a good sample of numbers.
Here is a video that discusses how to clean the data in SPSS. While it may be easier to do this prior to importing to SPSS, this video to be a bit long, but helpful in understanding the process.
https://www.youtube.com/watch?v=Ik4Dyn8e8vA

''contributed by Sheri Prendergast''

==How is a MANOVA different than a ANOVA?==

A MANOVA differs from ANOVA in that it allows the researcher to analyze data that involve more than one dependent variable. MANOVA can test hypotheses regarding the effect of one or more independent variables on two or more dependent variables.

''Contributed by Joseph W. Sullivan

==Why MANOVAs are a good test for dissertations==
A rationale for using a MANOVA as the statistical test for our dissertations includes the following: 1. A MANOVA is a test that allows us to measure multiple dependent variables together, which is likely given the scope and scale of the quantitative data collection for dissertations; 2. Passing the Box’s M test for significance .05 (Meyers) or .01 (Huberty & Olenjnik), mitigates risk that we’ve committed a Type I error (LaBanca, 2019, slides 5 & 12).

References:
LaBanca, F. (2019). Multivariate Analysis of Variance (MANOVA) [PowerPoint slides]. Retrieved from http://moodle.labanca.net/course/view.php?id=4.

''contributed by Emily Kilbourn''

Main Page

2019-12-17T03:46:18Z

Sullivan.joseph2050: /* Modules */

<big>'''Practical Statistics for Educators'''</big>
edited and maintained by Frank LaBanca, EdD

----

== Philosophy ==

Quantitative statistical analyses can be intimidating for many educators pursing an advanced academic degree. The thought of computational math can sometimes trigger unwarranted fears.

Here, we approach statistics from a straightforward conceptually-based perspective. Our goal is to collaborate and provide insight for statistics that make them meaningful tools in the educational arena.

Each "module" corresponds with the topics presented each week, and will expand as the course progresses. A topical outline can be found @ [http://docs.google.com/Doc?id=dfqvtcqp_46hhzzcsgt ]

Comments and edits are welcome and encouraged! Please give yourself credit as you contribute. At the end of a section you insert please add the following in italics:
''contributed by <your name>''
If you are modifying content, add the following under the contribution line:
''modified by <your name>'' We are glad to accept as many modifications as necessary to give the most meaning to each section. As we asynchronously socially construct knowledge together, we can recognize the accomplishments and contributions of each writer.

''contributed by Frank LaBanca, EdD''

== Contributions ==

Our contributors [[contributions here]].

Please submit your contribution at [https://forms.gle/PBaVrxFffbk5CKgg8]

== Modules ==

1.1 [[The Greek Alphabet]] and its significance in statistics

1.2 An introduction to probability PowerPoint @[http://docs.google.com/Presentation?id=dfqvtcqp_97wcrbtsn]

2.1 [[Types of Data]]

2.2 [[Visualizing Data]]

2.3 Visually representing data PowerPoint @ [http://docs.google.com/Presentation?docid=dfqvtcqp_27dwth2zz2#]

2.3.1 Table 2 from LaBanca dissertation @ [http://docs.google.com/Doc?id=dfqvtcqp_25cb5pqcfw]

2.3.2 Cool graph of movie box office from NY Times [http://www.nytimes.com/interactive/2008/02/23/movies/20080223_REVENUE_GRAPHIC.html#]

2.3.4 [[Histograms]]

2.3.5 Scatterplots YouTube @ [http://youtu.be/HFuU1uxJ1tQ]

2.4 [[Shapes of distribution]]

2.5 Survey of Attitudes Toward Statistics (SATS) Data Set @ [http://spreadsheets.google.com/pub?key=pmUxljSzLg7PokF_oAoCRUg]

2.6 [[Data Screening]]

3.1 [[Central Tendency]]

3.1.1 Central Tendency and Normal Distribution PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_47dfhfr4nw ]

3.1.2 Central Tendency YouTube @ [http://youtu.be/Fn4z8RDpwDY]

3.2 [[Interquartile ranges]]

3.2.1 [[The Box Plot]]

3.2.2 Interpreting a Box Plot - video [https://www.youtube.com/watch?v=b2C9I8HuCe4]

3.3 [[Standard deviation]]

3.3.1 [[Identifying percentile ranks and scores based on standard deviation]]

3.3.1.a [[Practice Identifying percentile ranks and scores based on standard deviation]]

3.4 [[z-scores]]

4.1 [[Percentile Rank]]
4.1.1 Areas under the standard normal curve for z values @ [http://spreadsheets.google.com/pub?key=pmUxljSzLg7NstEjJ40jJOQ]

4.1.2 z scores corresponding to divisions of the area under the normal curve @ [http://spreadsheets.google.com/pub?key=pmUxljSzLg7P6SVNiBdWbEg]

4.2 Conversion of data PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_98chfzg2tf]

4.2.1 Descriptive analysis of USRT data @ [http://docs.google.com/Doc?id=dfqvtcqp_61db6smpdm ]

4.3 [[Normal Curve Equivalent scores]]

4.3 [[Standard Error of Measurement]]

4.4 [[Confidence Intervals]]

4.4 z score machine @ [http://spreadsheets.google.com/pub?key=pmUxljSzLg7N8oLJZwmr3Zw]

5.1 [[Pearson r]]

5.2 [[Rules of thumb for interpreting the size of a correlation coefficient]]

5.3 Critical values for the correlation coefficient @ [http://spreadsheets.google.com/pub?key=pmUxljSzLg7MkuRpIgceTRQ]

5.4 [[Spearman rho]]

5.5 Correlation PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_134hsxg7td7]

5.6 [[Writing samples for correlations]]

6.1 [[Inferential Statistics Definition]]

6.2 [[Sampling]]

6.3 [[Sampling distributions]]

6.4 t test PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_153gr9f3hgd]

6.4.1 t -t test video [https://www.youtube.com/watch?v=N2dYGnZ70X0]

6.5 Sample data set @ [http://wolfweb.unr.edu/homepage/liu/stat/help/help.htm]

6.6 Critical values for t @ [http://spreadsheets.google.com/pub?key=pmUxljSzLg7OZzyZeHg9MIA]

6.7 Helpful Tutorial for Running a t-Test in Excel @ [https://www.rwu.edu/sites/default/files/downloads/fcas/mns/running_a_t-test_in_excel.pdf]

7.1 [[Effect size]]

7.1.1 Effect size calculator @ http://www.campbellcollaboration.org/resources/effect_size_input.php

7.1.2 [[Rules of thumb for interpreting effect sizes]]

7.2 Effect size PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_204f42f67dx ]

7.3 [[Hypothesis testing]]

7.4 Hypothesis testing PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_23295tm65dx]

7.5.1 Hypothesis testing template for a correlation @ [http://docs.google.com/Doc?id=dfqvtcqp_174ccchz4ds]

7.5.2 Hypothesis testing template for a t test @ [http://docs.google.com/Doc?id=dfqvtcqp_175hjcdsjff]

8.1 [[Type I and Type II Errors]]

8.2 Type I and Type II Errors PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_258wcq56rdv]

8.3 [[Levene's p versus the test statistic p]]

8.4 [[Analysis of Variance]]

8.5 ANOVA PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_265ckd9j9dv]

8.6 [[ANOVA Case study]]

8.7 ANOVA video [https://www.youtube.com/watch?v=ITf4vHhyGpc]

8.8 Critical values for the F statistic @ [http://www.sussex.ac.uk/Users/grahamh/RM1web/F-ratio%20table%202005.pdf]

8.9 [[Rules of thumb for interpreting effect sizes of ANOVAs]]

9.1 Post Hoc test PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_284dbhghdc9]

9.2 [[Selecting a Post Hoc test]]

9.3 Hypothesis testing template for ANOVA @ [http://docs.google.com/Doc?id=dfqvtcqp_295ckngxdgj]

10.1 [[Chi square]]

10.1.1 Chi square video [https://www.youtube.com/watch?v=VskmMgXmkMQ]

10.2 [[Example for calculating chi square]]

10.3 Critical values for chi square @ [http://spreadsheets.google.com/pub?key=pmUxljSzLg7OhBVHQWoHTIQ]

10.4 [[Chi square analysis description/sample writing]]

10.5 Chi square PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_297dhg685g8]

11.1 [[Beyond the ANOVA]]

11.2 Beyond ANOVA PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_307cmrtpxg3]

11.3 2-way ANOVA PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_325hrt86ggt]

11.4 2-way ANOVA template @ [http://docs.google.com/Doc?id=dfqvtcqp_372599pr6w6]

11.5 1-way ANOVA Annotated SPSS Output @ [https://drive.google.com/file/d/1i0kIWgmXLCEIIIYCSqh2JS9Th3T_pyRC/view?usp=sharing]

11.6 2-way ANOVA Annotated SPSS Output @ [https://drive.google.com/file/d/1wX4xhQa7KGCd1Hey7VfX33Y1uT6QHdEu/view?usp=sharing]

12.1 [[MANOVA]]

12.2 [[Homogeneity vs Homoscedacity]] (Levene vs Box's M)

12.3 [[Post Hoc ANOVAs for MANOVA]] (univariate)

12.4 [[Post Hoc Discriminant Analysis]] (multivariate)

12.5 [[Covariates]]

12.6 [[MANCOVA]]

12.7 MANOVA Annotated SPSS Output @ [https://drive.google.com/file/d/1GiErYfmCdiNQlCps3anF4Bu3C_iYS7oD/view?usp=sharing]

12.8 MANCOVA Annotated SPSS Output @ [https://drive.google.com/file/d/1TodMQy4vQ4eHStSIAevATOuJuFg9KUTD/view?usp=sharing]

13.1 [[Multiple Regression Analysis]]

13.1.1 [[Collinearity]]

13.2 [[Multiple Linear Regression]]

13.3 Reading the MLR Output: An annotated output [https://drive.google.com/file/d/141RNyYNnuDDTNvHYi4fE8EF8qWmal1aa/view?usp=sharing]

13.4 MLR Annotated SPSS Output @ [https://drive.google.com/file/d/1SsPL1YD4VYguxLtwqM_R7GFBD5RsG9H6/view?usp=sharing]

14.1 Internal Consistency Reliability[https://docs.google.com/document/d/18K16I8u9sbwpUhW9nIENF4x9wDXAy-4wFuBp6a0AljA/edit]
==Internal Consistency Reliability==

"Internal consistency reliability relates to the extent to which all the variables that make up the scale are measuring the same thing" (Muijs, 2011, pg. 217). ''Contributed by Joseph W. Sullivan''

14.2 Cronbach's Alpha[https://docs.google.com/document/d/1_eyXOcFrBcDSctM27a9T2kUlx9D8TidV_YTHk-wvTu0/edit]

14.2.1 [[Cronbach's Alpha Values]]

Cronbach's Alpha is a measure of the correlations between all the variables that make up a scale. The concept behind this measure is to determine if items measure the same concept. If so, they will be highly correlated and have a high alpha, indicating a high level of internal consistency. However, the more items in a particular scale, the higher the alpha tends to be, even if the items don't measure the same thing. It is suggested that the researcher should also run a factor analysis to strengthen the reliability of the scale (Muijs, 2011).

''Contributed by Joseph W. Sullivan''

14.2.2 Cronbach's Alpha in SPSS [https://www.youtube.com/watch?v=Kz8OdR6lV44]

== Applied Research Designs ==

15.1 [[Instrumentation]]

15.2 [[Limitations]]

15.3 [[Practice determining the stat]]

== Getting started ==
* [http://www.mediawiki.org/wiki/Manual:Configuration_settings Configuration settings list]
* [http://www.mediawiki.org/wiki/Manual:FAQ MediaWiki FAQ]
* [http://lists.wikimedia.org/mailman/listinfo/mediawiki-announce MediaWiki release mailing list]

Collinearity

2019-12-17T03:31:07Z

Sullivan.joseph2050: /* Collinearity and Multicollinearity */

== Collinearity and Multicollinearity ==

'''Collinearity''' is "a condition that exists when two predictors correlate very strongly" (Meyers, Gamst, & Guarino, 2017, p. 189).

'''Multicollinearity''' is a condition that exists when "more than two predictors correlate very strongly" (p. 189).

If the predictor variables are too strongly correlated with one another the researcher will have a challenging time estimating the relationship between the dependent and predictor variables. SPSS is capable of providing a diagnostic to find out if multicollinearity exists within your data. This feature is able to determine the tolerance, or amount of variance in the individual variable not explained by the other predictor variables.

(Muijs, 2011, pg. 155-157)

''Contributed by Joseph W. Sullivan''

Steps for How to Detect Multicollinearity in SPSS:

1. Click "Analyze"

2. Then Select "Regression"

3. Click "Linear"

4. Put all the IV's in the IV section and then move ONE IV into the DV box.

5. Uncheck all boxes in "Statistics" except for "Collinearity Diagnostics"

6. Click "Ok"

*The output should indicate if there is a VIF. If the VIF is above 3 there is likely multicollinearity issues, and if it is above 10 you are highly likely to have multicollinearity issues.

Detecting Multicollinearity in SPSS [https://www.youtube.com/watch?v=oPXjQCtyoG0]

''contributed by Britany Kuslis, WCSU Cohort 8''

References:

Gaskin, James, director. ''Detecting Multicollinearity in SPSS''. YouTube, YouTube.com, 26 Mar. 2011, www.youtube.com/watch?v=oPXjQCtyoG0.

Meyers, S., Gamst, G., & Guarino, A.J. (2017). ''Applied multivariate research: Design and interpretation.'' Thousand Oaks, CA: Sage Publications.

Histograms

2019-12-17T03:09:13Z

Sullivan.joseph2050: /* Histograms */

==Histograms==
"Histograms are used to display the distribution of a single continuous variable (e.g. age, perceived stress scores)." Examining the shape of the curve will provide information about the distribution of scores of a continuous variable. If we assume that scores of each variable measured are distributed normally, most scores will occur in the center, and taper towards the extremes. The skewness of the data is determined if the data displayed is either distributed more to right or left side of the visual.

(Pallant, 2016, pg. 68)

''contributed by Joseph W. Sullivan

==To create a histogram on SPSS, do the following:==

1) After entering data into SPSS, click on "Graphs", scroll down to “Legacy
Dialogs", move cursor to the right and scroll down to "Histograms".

2) Click on the variable in the left box you want entered into the right variable box

3) Click on “display normal curve” to view the bar graph data in bell curve form

4) Click "OK".

5) The histogram will appear PASW Output Statistic Viewer

''contributed by Jen Eraca''

Histograms

2019-12-17T03:08:46Z

Sullivan.joseph2050: /* To create a histogram on SPSS, do the following: */

==Histograms==
"Histograms are used to display the distribution of a single continuous variable (e.g. age, perceived stress scores)." Examining the shape of the curve will provide information about the distribution of scores of a continuous variable. If we assume that scores of each variable measured are distributed normally, most scores will occur in the center, and taper towards the extremes. The skewness of the data is determined if the data displayed is either distributed more to right or left side of the visual.
(Pallant, 2016, pg. 68)
''contributed by Joseph W. Sullivan

==To create a histogram on SPSS, do the following:==

1) After entering data into SPSS, click on "Graphs", scroll down to “Legacy
Dialogs", move cursor to the right and scroll down to "Histograms".

2) Click on the variable in the left box you want entered into the right variable box

3) Click on “display normal curve” to view the bar graph data in bell curve form

4) Click "OK".

5) The histogram will appear PASW Output Statistic Viewer

''contributed by Jen Eraca''

Contributions here

2019-12-17T02:51:42Z

Sullivan.joseph2050: /* Student Contributors */

== Editor ==
Frank LaBanca, EdD

== Faculty Contributors ==
Karen Burke, EdD

Patricia Cosentino, EdD

Deborah Hardy, EdD

Jennifer Mitchell, EdD

== Student Contributors ==
David Bozzuto

Karen Fildes

Michael Minzloff

Damien Holst

Jennifer Eraca

John Ryan

Kara Kunst

Emily Rhew

Cassandra Cosentino

Kristina Hislop

Mary Fernand

Thomas Fox

Helen Knudsen

Ashley Brooksbank

Scott Trungadi

Sheri Prendergast

Britany Kuslis

Mykal Kuslis

Nicole Griffin

Joseph W. Sullivan

Instrumentation

2019-12-10T02:52:41Z

Sullivan.joseph2050: /* Maslach Burnout Inventory - Educators Survey (MBI-ES) */

When attempting to measure a phenomenon in educational research, a reliable and valid instrument is necessary. Below are descriptions of several instruments. The writing samples come from dissertation proposals.

== Levels of Use (LoU). ==

This instrument is one of three diagnostic instruments of the Concerns-Based Adoption Model (CBAM) that evolved out of the educational change work of Fuller, Hall, Dirksen, & George during the 1970s (SEDL, 2006). The purpose of the LoU structured interview is to identify teachers’ current behaviors in regard to a specific innovation. The instrument uses a branching technique that uses operationally defined phenomenon to differentiate eight Levels of Use and decision points between each level (see Appendix E). The district will identify a research-based instructional strategy as the innovation to be measured before the study begins. The LoU breaks use and nonuse of the innovation, or instructional strategy, into a continuum of eight categories: (a) Nonuse, (b) Orientation, (c) Preparation, (d) Mechanical Use, (e) Routine, (f) Refinement, (g) Integration, and (h) Renewal. These levels characterize each teacher’s development in acquiring new skills and use of the innovation. Each level describes a very different set of behavioral actions and related understandings of the innovation and its use. Operational definitions have been developed for each Level of Use.

Validity of the LoU was established using ethnographic methodology. First, teachers were assigned LoU ratings based on interviews using the instrument. These ratings were compared to ratings assigned to the same teachers by (a) an observer who spent a full day observing the teacher, and (b) an independent rater who read the observer’s notes and assigned a rating based on the content of the notes. Correlations between LoU ratings obtained using the instrument and the methodology described above were .98 and .65, respectively. Inter-rater reliability for the LoU ratings were established by converting the ratings to a numeric value; this analysis yielded a coefficient of .98 (Cronbach’s alpha).

''contributed by Jennifer Mitchell, EdD''

== Assessment of Reading Comprehension (ARC). ==

This reading comprehension assessment was developed by the researcher. Reliability and validity data for the Assessment of Reading Comprehension (ARC; form A and form B) were collected during a pilot study. This reading comprehension instrument was designed to reflect the comprehension strands measured on the Connecticut Mastery Test (CMT). These strands include: (a) forming a general understanding, (b) developing an interpretation, (c) making reader/text connections, and (d) examining the content/structure of text (CSDE, 2006; see Appendix B). The researcher collected evidence for content validity by having a panel of reading experts reviewed the ARC. The instrument was revised to more accurately reflect question stems on the CMT. The panel determined that the instrument had strong content validity. The reliability estimates indicate strong total test internal consistency levels. Coefficient values for both Form A and Form B were .85 (Cronbach’s Alpha). The alternate form reliability correlation for the ARC was .76, indicating a high positive correlation between Form A (pretest) and Form B (posttest). Refer to Appendix C for a summary of procedures conducted during the ARC pilot study and Appendix D for a copy of Form A and Form B of the ARC.

''contributed by Jennifer Mitchell, EdD''

== Gates Macginite Reading Test (GMRT) and Degrees of Reading Power (DRP). ==

Students will also be administered either the Gates Macginite Reading Test (GMRT) or the Degrees of Reading Power (DRP). Data from one of these instruments will be utilized as a covariate to produce adjusted means for students’ initial reading achievement. The district’s reading and language arts coordinator will determine which assessment will be administered based on which instrument yields the most valuable information for the district. Refer to Appendix C for reliability and validity information for both instruments.

''contributed by Jennifer Mitchell, EdD''

== Structured Coaching Log (SCL). ==

The purpose of the coaching logs is to document the events that occur during the coaching treatments (independent variable) throughout the 10-week quasi-experiment. The SCL will document all professional development training components and coaching strategies implemented with each teacher. Log codes will include a teacher code, a professional development component code, the amount of time spent on each training component, and the instructional strategy focus of each coaching session. Codes have been predetermined by the researcher to create consistent and standard log entries (see Appendix F). Coaches will be trained to use these codes. Evidence for content validity (Gall, Gall, & Borg, 2003) of the SCL was gathered during a pilot study (see Appendix E).

''contributed by Jennifer Mitchell, EdD''

== The School Counselor Activity Rating Scale. ==

This scale was developed by Janna L. Scarborough, Ph.D., NCC, NCSC, ACS, Assistant Professor, and School Counseling Program Coordinator - Counseling & Human Services Syracuse University. Permission from the developer has been granted to utilize the instrument.

The School Counselor Activity Rating Scale survey defines the logical methods of evaluation which include (a) examining the rationale for each objective within each subgroup of the rating scale as defined by the instrument in terms of coordination, consultation, curriculum, and other activities; (b) the consequences of achieving the objective as defined by preferred and actual activities; and (c) consideration of high order values of goals which is aligned in New York State to the comprehensive model of school counseling. The School Counseling Activity Rating Scale was developed by establishing a list of work activities that reflected the job of school counselors. Task statements were created that reflected the activities under the four major interventions described in the National Model for School Counseling Programs (ASCA, 2003). Items described activities in: counseling (individual and group), consultation, coordination, curriculum (classroom lessons), and other duties.

The School Counseling Activity Rating Scale uses a response format in which school counselors are asked how often an activity is performed. The author recognizes that the verbal frequency scale has limitations, but it was selected for perceived ease, comprehensiveness, and flexibility. Two types of frequencies were measured: actual and preferred activity on a 5-point rating scale numbered 1-5 as defined: (1 ) never do this; (2) rarely do this; (3) occasionally do this; (4) frequently do this; and (5) routinely do this.

The School Counseling Activity Rating Scale’s content validity was obtained by administering a pretest to assess for production mistakes (Scarborough, 2005). A review of the instrument was also conducted by professionals in the school counseling field. A field test of the survey was conducted and results were achieved by utilizing the varimax rotation for factor analysis and construct validity was obtained by reviewing the scores of the one-way ANOVA (Scarborough, 2005). Internal consistency was obtained through the Conbach’s coefficient alpha for each subset of the survey (Scarborough, 2005, p. 278). The coefficient alpha results of each subset are as follows: counseling showed a .85 for actual and .83 for preferred; coordination showed a .84 for actual and .85 for preferred; consultation showed a .75 for actual and .77 for prefer; and curriculum showed a .93 for actual and .90 for preferred (Scarborough, 2005).

''contributed by Deborah Hardy, EdD''

== Readiness Survey. ==

The Readiness Survey (Carey, 2005) was developed to help school counselors and administrators assess their district's readiness to implement the American School Counselor Association National Model (ASCA,2000), and to determine areas that will need to be addressed to successfully implement the National Model (Poynton, 2005). The survey addresses areas of needs for implementation and diagnoses problems in readiness towards integration into local school districts.

The Readiness Survey (Carey, 2005) is composed of seven indicator areas including community support, leadership, guidance curriculum, staffing time and use, school counselor’s beliefs and attitudes, school counselor’s skills, and district resources. The survey uses a rating scale as defined by (1) like my district; (2) somewhat like my district; (3) not like my district. Validity and reliability of the instrument are in process of being determined as per the University of Massachusetts National Outreach Center for School Counseling.

''contributed by Deborah Hardy, EdD''

== The Gates-MacGinitie Reading Test. ==

The Gates-MacGinitie Reading Test (GMRT-4) (2002) is an instrument that will be used in the study; it will be administered to students in May 2007. The GMRT-4 will be used to assess students’ level of reading achievement. GMRT-4 is found to have strong reliability and validity. The reliability estimates indicate strong total test and subtest internal consistency levels with coefficient values at or above .90. Content validity was documented through a process of test development to identify the scope of the subtests and identify effective items within subtests. Construct validity is supported by strong intercorrelations between subtests and total test scores. Students’ raw scores will be converted into national stanines, normal curve equivalents, percentile ranks, grade equivalents and extended scale scores (MacGinitie, et al., 2002).

''contributed by Patricia Cosentino, EdD''

== The Roxy Kindergarten Inventory of Skills. ==

Another instrument that will be used to assess the kindergarten students is The Roxy (pseudonym) Kindergarten Inventory of Skills, which is a district assessment. The Roxy Kindergarten Inventory of Skills will assess students in the following content areas: upper and lower case letter recognition, rhyme recognition and rhyme production, initial sound production, oral blending and oral segmentation. Content validity was originally found through the design of the test when literacy experts from the Roxy district designed the test. Connecticut State Frameworks were reviewed, alternate tests were examined, and important concepts were included in the inventory. Additional content validity will be found by having a jury of 10 experts including kindergarten and first grade teachers and early childhood administrators review the document and validate the content of the assessment as it compares to the Connecticut State Frameworks. The instrument was used in a pilot study in the spring of 2006 in which it was found to have construct validity. The 26 students who were deemed to be below grade level and who were struggling in kindergarten performed poorly on the assessment whereas the students who performed on grade level in class scored on grade level on the assessment.

''contributed by Patricia Cosentino, EdD''

== The California Measure of Mental Motivation (CM3) ==

The California Measure of Mental Motivation (CM3) is a quantitative instrument focused on measuring cognitive competencies (Giancarlo, 2010). The CM3 is administered to measure cognitive engagement and motivation toward problem solving and learning (Giancarlo, Blohm, & Urdan, 2004). The CM3 is comprised of four major scales including learning orientation, creative problem solving, mental focus, and cognitive integrity (Giancarlo et al., 2004). The CM3 is composed of approximately 25 items for the four major scales. These four factors demonstrate a stability across study samples, and scales derived from the major factors correlated with known measures of student motivation and achievement (Giancarlo et al., 2004). Level II+ of the CM3 adds a fifth important scale: scholarly rigor. Level III of the CM3 adds a sixth major scale: technical orientation. The response format used to collect information appears in the form of a X-point Likert scale, with scales ranging from "strongly agree" to "strongly disagree." Sample items from the instrument are not available for view due to test security. Scores are reported based upon a 50-point metric. Scores ranging from 0 – 9 points represent individuals who are “strongly negatively opposed” to a particular characteristic; scores ranging from 10 – 19 reflect “somewhat negative” perceptions; scores in the 20 – 30 range are considered to be “ambivalent;” scores in the 31 – 40 range are “somewhat disposed” toward the topic; and scores of 41 and above are “strongly disposed” to the attribute (Giancarlo, 2010, p. 26). The CM3 is both a valid and reliable quantitative instrument (Giancarlo et al., 2004). Cronbach's alpha coefficient was used to evaluate internal consistency of scores obtained by the CM3 for the four subscales of the 25-item version. Across the studies conducted, the values ranged from 0.53 to 0.83 (Giancarlo et al., 2004). The reliability estimates for learning orientation ranged from .79 - .83 across the various studies. Creative problem solving produced an alpha coefficient ranging from .70 - .77. Mental focus ranged from .79 - .83 and cognitive integrity ranged from .53 - .63 (Giancarlo et al., 2004).

References:

Giancarlo, C. A. (2010). The California Measure of Mental Motivation: User manual. Millbrae, CA: California Academic Press.

Giancarlo, C. A., Blohm, S. W., & Urdan, T. (2004). Assessing secondary students’ disposition toward critical thinking: Development of the California Measure of Mental Motivation. Educational and Psychological Measurement, 64(2), 347-364.

''contributed by Scott Trungadi, Cohort 8''

== Maslach Burnout Inventory - Educators Survey (MBI-ES) ==

The Maslach Burnout Inventory-Educators Survey (MBI-ES)(1996) is designed to assess and measure levels of professional burnout in education professions. Burnout is a psychological syndrome of emotional exhaustion that depletes workers emotional resources and prohibits the human service worker from contributing on a psychological level to their clients. The MBI-ES can help people develop awareness to whether burnout is something they need to address in order to understand their own personal feelings potentially related to things like job satisfaction, personal stress levels, and overall motivation (Maslach, Jackson, & Leiter, 1996). The MBI-ES does not measure specific stressors or particular reasons for burnout. Instead, this scale is used to determine if participants are experiencing barely noticeable or major feelings, attitudes, or ideas of burnout.
The MBI-ES takes about 10 to 15 minutes to complete and consists of 22 items which are divided into three subscales. The examiner should be a neutral person and it is suggested that they not be someone who has direct authority of the respondents. Through factor analysis this particular instrument revealed that the following three subscales emerged: Emotional exhaustion, depersonalization, and personal accomplishment. Emotional exhaustion is characterized by feelings of emotional or physical depletion and represents 9 questions on the questionnaire. 5 questions were designed to measure characteristics of depersonalization which would indicate the lack of empathy and emotional distance between the respondent and their coworkers. The third subscale measured is personal accomplishment which describes feelings of confidence and competence in one’s job and is assessed by 10 questions. The items are written in the form of statements about personal feeling or attitudes: “I feel burned out from my work,” and “I don’t really care what happens to some recipients” are questions that can be found directly on the survey. The items are answered in terms of frequency in which the respondent experiences these feelings, on a 7-point scale ranging from 0, “never” to 6, “every day”. Each respondents test form is scored by using a scoring key for each subscale. The scores for each subscale are considered separately and are not combined into a single, total score, creating three scores computed for each respondent. Each score is then coded as low, average or high by using numerical cutoff points listed on the scoring key.
The consequences of burnout are potentially very serious for workers, their clients, and the larger institutions in which they interact. The MBI-ES is considered to be the leading measurement tool of burnout and will provide a foundation of knowledge to explore individual perspectives that create stress and lead to findings of how professionals cope with that stress. Combined with an additional measure of burnout, the Areas of Work Life Survey (AWS), both measures can contribute to exploring perceptions of work setting qualities that could also contribute to worker burnout.

References:

Maslach, C., Jackson, S. E., & Leiter, M. P. (1996). Maslach burnout inventory. (3rd ed.). Palo Alto, CA: Consulting Psychologists Press.

' 'contributed by Joseph W. Sullivan, Cohort 8' '

Instrumentation

2019-12-10T02:51:46Z

Sullivan.joseph2050: