Practical Statistics for Educators - User contributions [en]

T-test - What is a t-test?

2022-04-27T22:37:52Z

TaniaNicole: 6.4.2 What is a t-test

t-tests are used to determine whether two samples are different. For example, two fifth grade classes (random samples) took the same reading pre and post-tests.
The T-Test will help us analyze whether the two means are different. This means we are comparing the mean of fifth-grade class #1 to fifth grade class #2 to see how they vary.
This will help to figure out if students are increasing performance overall or not. Also, if there is significant difference, the t-test will help us figure out if we need to investigate further to see why there is significant differences in performance (intervention, supplemental materials, delivery method of instruction, etc).

contributed by ''Tania Nicole Sutherland
''

An introduction to probability

2022-04-25T23:01:00Z

TaniaNicole: /* Introduction to Probability */

== Introduction to Probability ==

<big>'''Probability of an Event'''</big>

If all of the outcomes in an experiment are equally likely, then the probability of an event, ''E'', occurring is given by:

[[File:P(E)_definition.JPG]]

Note: the number of outcomes that result in event ''E'' occurring can never be negative and can never be greater than the total number of outcomes, so we know:

[[File:Range_of_P(E).JPG]]

<big>'''Theoretical Probability vs. Empirical Probability'''</big>

A probability computed by using a probability formula is called a '''''theoretical probability'''''.

A probability found by observing the actual outcomes of an experiment that is repeated many times is called '''''empirical probability'''''.

Consider rolling a 6-sided die.

We know that each outcome is equally likely, so the theoretical probabilities are as follows:

[[File:Theoretical_Probability_of_Dice.JPG]]

However, if we actually rolled a 6-sided die 600 times and recorded the outcomes, we may find that the empirical probabilities differ:

(Geogebra [https://www.geogebra.org/m/UsoH4eNl] is a great tool for simulating this experiment)

[[File:Empirical_Probability_of_Dice.JPG]]

Notice only one outcome (rolling a 5) matched the theoretical probability.

''contributed by David Ciskowski''

_____________________________________________________________________________________________________________________________________________________________________________________

What is probability?

Probability is the likelihood that an event will occur and is calculated by dividing the number of favorable outcomes by the total number of possible outcomes.

In Statistics, the probability distribution gives the possibility of each outcome of a random experiment or event. It provides the probabilities of different possible occurrences.

Consider this example.
When you flip a coin, there are only two possible outcomes. Heads or Tails. So the probability of getting heads is 1 out of 2 or 1/2 or 50%.
There is a 50% chance of getting heads and a 50% chance of getting tails.
Probability distribution maps out the likelihood of multiple outcomes in an equation or table,
If we flip the two coins twice in a row, there are four possible outcomes.
The is a distribution of
25% heads/heads
25% heads/tails
25% tails/tails
25% tails/heads

contribution by ''Tania Nicole Sutherland''

Effect size

2022-04-20T21:26:34Z

TaniaNicole: What is effect size?

Effect size for ANOVA

Partial Eta Squared

Trivial: <0.2
Small: 0.2-0.49
Moderate: 0.5-0.79
Large: >0.8

______________________________________________________________________________________________________________________________________________________________________________________

Knowing that the relationship is significant does not tell us whether this effect is strong or weak. So we need to calculate an effect size as well as the t-test.

Muijs, D. (2016).''Doing Quantitative Research in Education With SPSS'' (2nd ed.). Sage Publications.

An effect size is a way to quantify the difference between two groups.

While a p-value can tell us whether or not there is a statistically significant difference between two groups, an effect size can tell us how large this difference actually is. In practice, effect sizes are much more interesting and useful to know than p-values.

Bobbit, Z. (2020, January 1). Effect Size: What It Is and Why It Matters. ''Statistics. Simplified. Statology''.
https://www.statology.org/effect-size/

contributed by ''Tania Nicole Sutherland''

Effect size

2022-04-20T21:26:09Z

TaniaNicole: What is effect size?

Effect size

2022-04-20T21:24:27Z

TaniaNicole: What is effect size?

Effect size for ANOVA

Partial Eta Squared

Trivial: <0.2
Small: 0.2-0.49
Moderate: 0.5-0.79
Large: >0.8

______________________________________________________________________________________________________________________________________________________________________________________

"Knowing that the relationship is significant does not tell us whether this effect is strong or weak. So we need to calculate an effect size as well as the t-test..."

Muijs, D. (2016).''Doing Quantitative Research in Education With SPSS'' (2nd ed.). Sage Publications.

An effect size is a way to quantify the difference between two groups.

While a p-value can tell us whether or not there is a statistically significant difference between two groups, an effect size can tell us how large this difference actually is. In practice, effect sizes are much more interesting and useful to know than p-values.

Bobbit, Z. (2020, January 1). Effect Size: What It Is and Why It Matters. ''Statistics. Simplified. Statology''.
https://www.statology.org/effect-size/

contributed by ''Tania Nicole Sutherland''

Effect size

2022-04-20T21:22:28Z

TaniaNicole: What is effect size?

Effect size for ANOVA

Partial Eta Squared

Trivial: <0.2
Small: 0.2-0.49
Moderate: 0.5-0.79
Large: >0.8

______________________________________________________________________________________________________________________________________________________________________________________

"Knowing that the relationship is significant does not tell us whether this effect is strong or weak. So we need to calculate an effect size as well as the t-test..."

Muijs, D. (2016).''Doing Quantitative Research in Education With SPSS,'' (2nd ed.). Sage Publications.

An effect size is a way to quantify the difference between two groups.

While a p-value can tell us whether or not there is a statistically significant difference between two groups, an effect size can tell us how large this difference actually is. In practice, effect sizes are much more interesting and useful to know than p-values.

Bobbit, Z. (2020, January 1). Effect Size: What It Is and Why It Matters. ''Statistics. Simplified. Statology''.
https://www.statology.org/effect-size/

contributed by ''Tania Nicole Sutherland''

Effect size

2022-04-20T20:43:42Z

TaniaNicole: What is effect size?

T-test - What is a t-test?

2022-04-20T20:35:10Z

TaniaNicole: What is a t-test? (Broken down for basic understanding)

t-tests are used to determine whether two samples are different. For example, two sixth grade classes (random samples) took the same reading pre and post-tests.
The T-Test will help us analyze whether the two means are different. This means we are comparing the mean of fifth-grade class #1 to fifth grade class #2 to see how they vary.
This will help to figure out if students are increasing performance overall or not. Also, if there is significant difference, the t-test will help us figure out if we need to investigate further to see why there is significant differences in performance (intervention, supplemental materials, delivery method of instruction, etc).

contributed by ''Tania Nicole Sutherland
''

T-test - What is a t-test?

2022-04-20T20:34:50Z

TaniaNicole: What is a t-test? (Broken down for basic understanding)

T-test - What is a t-test?

2022-04-20T20:34:19Z

TaniaNicole: What is a t-test? (Broken down for basic understanding)

ANOVA Case study

2022-04-18T21:58:05Z

TaniaNicole: ANOVA Test used to compare students who took test early vs later in a multi-day test period

One of the great challenges affecting suburban school districts is staffing and space issues as it relates to Kindergarten. Many kindergarten programs in Connecticut are still half day, challenging those teachers to cover an ever-increasing curriculum in a short amount of time.

What about those students who struggle? How are they not "left behind" in an increasingly rigorous educational setting? One administrator attempted to address this issue by starting a Kindergarten "Buddy Program." The buddy program was an extended day program for those students at risk that took place after the first kindergarten session. Those students had intensive work for approximately 1 hour and then were bused home. This was a potential cost-effective solution. To see if is was an effective instructional strategy, a posttest was given to subjects in three settings (those of the Kindergarten buddy program, those in a traditional half day kindergarten program, and those in a full-day kindergarten program. A description of the assessment is provided below:

''contributed by Frank LaBanca, EdD''

== Gates-MacGinitie Reading Test: Level PR (Pre-Reading) ==

Paper-Pencil version only Find info about the G-M Reading Test here [http://www.riverpub.com/products/gmrt/index.html]

Designed to help teachers discover what students at the end of Kindergarten and students at the beginning Grade 1 know about important background concepts upon which beginning reading skills are built.

Subtest 1, Literacy Concepts, evaluates students' understanding of the nature and uses of written English and their understanding of words and phrases commonly used in beginning reading instruction.

Subtest 2, Oral Language Concepts (Phonological Awareness), evaluates students' abilities to attend to the basic structure of spoken English words, especially to phonemic units (speech sounds), which are the basis of the alphabetic principle and much beginning reading instruction.

Subtest 3, Letters and Letter/Sound Correspondences, evaluates students' knowledge of letters and their abilities to relate them to sounds.

Subtest 4, Listening (Story) Comprehension, evaluates students' abilities to understand important elements of connected text. Designed for nonreaders, answer choices for Level PR predominantly consist of pictures.

''contributed by Frank LaBanca, EdD''

******************************************************************************************************************************************************************************

Another ANOVA Study

Instructors are often concerned when giving multiple-day tests because students taking the test later in the exam period
may have an advantage over students taking the test early in the exam period due to information leakage.

What about those students who also can seek tutoring for extra support to give them advantage of increased scoring?

However, exam scores seemed to decline as students took the same test later in a multi-day exam period (Mouritsen and Davis, 2012). This study reports mean test score analysis of a four-day exam period. Students with higher cumulative GPAs tend to take the exam earlier in the testing period. The majority of students take the exam the last day of the testing period. Test score variance for each test day also increases with each test day. One-way ANOVA analysis finds that mean test scores of students who take the test later in the test period significantly decline.

This study concluded that there was no significant impact on increasing test scores for students who took the exam in later in the multi-day testing window.

Mouritsen, M. L.; Davis, J. T.; Jones, S. C. (2016). Journal of Learning in Higher Education: ANOVA Analysis of Student Daily Test Scores in Multi-Day Test Periods, v12 n2 p73-82 (EJ1139744)

contributed by ''Tania Nicole Sutherland''

ANOVA Case study

2022-04-18T21:57:30Z

TaniaNicole: ANOVA Test used to compare students who took test early vs later in a multi-day test period

ANOVA Case study

2022-04-18T21:56:32Z

TaniaNicole: ANOVA Test used to compare students who took test early vs later in a multi-day test period

ANOVA Case study

2022-04-18T21:55:20Z

TaniaNicole: ANOVA Test used to compare students who took test early vs later in a multi-day test period

Talk:Main Page

2022-04-18T19:16:31Z

TaniaNicole: 6.4.2 What is a t-test

t-tests are used to determine whether two samples are different. For example, two sixth grade classes (random samples) took the same reading pre and post tests.
The T-Test will help us analyze whether the two means are different. This means we are comparing the mean of fifth grade class one to fifth grade class two to see how they vary.
This will help to figure out if students are increasing performance overall or not. Also, the t-test will help us figure out if we need to investigate further if there is significant differences in performance (intervention, supplemental materials, delivery method of instruction, etc).

** ''Contributed by Tania Nicole Sutherland'' **

Main Page

2022-04-18T19:14:27Z

TaniaNicole: /* 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. Quantitative research in education and other fields of inquiry is expressed in numbers and measurements. This type of research aims to find data to confirm or test a hypothesis. Quantitative study requires extensive statistical analysis, which can be difficult to perform for researchers from non- statistical backgrounds. Statistical analysis is based on scientific discipline and hence difficult for non-mathematicians to perform. But once one begins to embark on understanding all of the representations, descriptions, and analyses of particular data sets, statistics becomes an educator’s friend not foe.

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''
''contributed and modified by Jennifer Blue''

== 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]

1.3 [[Some Probability Formulas]]

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]

6.4.2 t-test - What is a t-test?

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]

14.1.1 [[Internal Consistency Reliability]]

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

14.2.1 [[Cronbach's Alpha Values]]

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]

The Box Plot

2022-04-18T18:45:27Z

TaniaNicole: /* How to Read a 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''

==Creating a box and whisker plot using SPSS==
(Refer to emailed file for screen-shots and further assistance)

1) Open SPSS and EXCEL

2) Copy the data (into SPSS) that you would like to use. For example, test scores disaggregated by gender. Make sure that you assign numbers to gender (In example below: 1 = male; 2 = female).

3) Once data is entered into SPSS (as depicted above), click on: “Graphs  boxplot”

4) Click “define” (with “simple” & “summary for groups of cases” chosen)

5) Move “test” (or your variable of choice) into the variable section

6) Move “gender” (or whatever you choose) into the “category axis” section.

7) Click OK.

8) In order to format in APA, double click on the graph in SPSS. Change each axis to read what you would like them to read.

9) Close the “chart editor” and copy and paste your final graph from SPSS into your document of choice.

10) Have a drink to congratulate yourself on a job well done. Please note: this step is not in the latest version of APA).

''contributed by Chris Longo''

==What is a Box Plot and When is It Used==

The box plot or box-and-whisker plot is a graphic, created by John W. Tukey, used to show the distribution of a set of data. It is frequently used with data that can also be represented with a histogram, but the box plot shows more information than a standard histogram. For example, the box plot is useful to researchers because it shows extreme scores.

[[File:box-plot-explained.gif]]

==How to Read a Box Plot==

Let's say we ask 282 people how many pairs of shoes they've consumed in the past ten years. We'll sort those responses from least to greatest and then graph them with our box-and-whisker. See the example above.

Take the top 50% of the group (142) who bought more pairs of shoes; they are represented by everything above the median (the white line). Those in the top 25% of shoe buying (71) are shown by the top "whisker" and dots. Dots represent those who bought a lot more shoes than normal or a lot less than normal (outliers). If more than one outlier bought the same number of shoes, dots are placed side by side.

''contributed by Michael Minzloff''

*************************************************
Box and Whisker plots show the variability of a data set. In order to make a box and whisker plot, you need to know
Five Number Summary
1) Least value

2) Greatest Value

3) Quartile 1

4) Quartile 3

5) Median

For example, if you had a set of numbers and sorted the following from least to greatest on basketball scores for your team during the school year.
14, 15, 20, 26, 27, 30, 30, 30, 33, 35, 36, 38 (least number would be 14, Q1 =23, median =30 Q3=34 and greatest value is 38)
Show your kids to make a number line. In this instance, it would be counting up to 38 from 14 by 2's
Put a dot above 14 and above 38 which are your least and greatest.
Then put vertical lines above your Q1 which is 23, Median which is 30 and Q3 which is 34. Connect the lines to make a box with 23, 30 and 34. Then extend a vertical line from 23 to 14. This is the whisker because it is outside the box. Do the same with the other side of your box which would be to extend a line from 34 to 38. That is the other whisker.
There you have it! You have not only learned how to read The Box Plot and but also how to create it. A box and whisker plot. Fun!

** ''Contributed from Tania Nicole Sutherland''**

==How-to Video==

How to create a box and whisker plot using SPSS [http://www.youtube.com/watch?v=lRaMDHiIvc4&feature=youtu]

''contributed by Jen Eraca''

The Box Plot

2022-04-18T18:43:52Z

TaniaNicole: /* How to Read a 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''

==Creating a box and whisker plot using SPSS==
(Refer to emailed file for screen-shots and further assistance)

1) Open SPSS and EXCEL

2) Copy the data (into SPSS) that you would like to use. For example, test scores disaggregated by gender. Make sure that you assign numbers to gender (In example below: 1 = male; 2 = female).

3) Once data is entered into SPSS (as depicted above), click on: “Graphs  boxplot”

4) Click “define” (with “simple” & “summary for groups of cases” chosen)

5) Move “test” (or your variable of choice) into the variable section

6) Move “gender” (or whatever you choose) into the “category axis” section.

7) Click OK.

8) In order to format in APA, double click on the graph in SPSS. Change each axis to read what you would like them to read.

9) Close the “chart editor” and copy and paste your final graph from SPSS into your document of choice.

10) Have a drink to congratulate yourself on a job well done. Please note: this step is not in the latest version of APA).

''contributed by Chris Longo''

==What is a Box Plot and When is It Used==

The box plot or box-and-whisker plot is a graphic, created by John W. Tukey, used to show the distribution of a set of data. It is frequently used with data that can also be represented with a histogram, but the box plot shows more information than a standard histogram. For example, the box plot is useful to researchers because it shows extreme scores.

[[File:box-plot-explained.gif]]

==How to Read a Box Plot==

Let's say we ask 282 people how many pairs of shoes they've consumed in the past ten years. We'll sort those responses from least to greatest and then graph them with our box-and-whisker. See the example above.

Take the top 50% of the group (142) who bought more pairs of shoes; they are represented by everything above the median (the white line). Those in the top 25% of shoe buying (71) are shown by the top "whisker" and dots. Dots represent those who bought a lot more shoes than normal or a lot less than normal (outliers). If more than one outlier bought the same number of shoes, dots are placed side by side.

''contributed by Michael Minzloff''

-
Box and Whisker plots show the variability of a data set. In order to make a box and whisker plot, you need to know
Five Number Summary
1) Least value
2) Greatest Value
3) Quartile 1
4) Quartile 3
5) Median
For example, if you had a set of numbers and sorted the following from least to greatest on basketball scores for your team during the school year.
14, 15, 20, 26, 27, 30, 30, 30, 33, 35, 36, 38 (least number would be 14, Q1 =23, median =30 Q3=34 and greatest value is 38)
Show your kids to make a number line. In this instance, it would be counting up to 38 from 14 by 2's
Put a dot above 14 and above 38 which are your least and greatest.
Then put vertical lines above your Q1 which is 23, Median which is 30 and Q3 which is 34. Connect the lines to make a box with 23, 30 and 34. Then extend a vertical line from 23 to 14. This is the whisker because it is outside the box. Do the same with the other side of your box which would be to extend a line from 34 to 38. That is the other whisker.
There you have it! You have not only learned how to read The Box Plot and but also how to create it. A box and whisker plot. Fun!

** ''Contributed from Tania Nicole Sutherland''**

==How-to Video==

How to create a box and whisker plot using SPSS [http://www.youtube.com/watch?v=lRaMDHiIvc4&feature=youtu]

''contributed by Jen Eraca''

The Box Plot

2022-04-18T18:43:25Z

TaniaNicole: /* How to Read a 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''

==Creating a box and whisker plot using SPSS==
(Refer to emailed file for screen-shots and further assistance)

1) Open SPSS and EXCEL

2) Copy the data (into SPSS) that you would like to use. For example, test scores disaggregated by gender. Make sure that you assign numbers to gender (In example below: 1 = male; 2 = female).

3) Once data is entered into SPSS (as depicted above), click on: “Graphs  boxplot”

4) Click “define” (with “simple” & “summary for groups of cases” chosen)

5) Move “test” (or your variable of choice) into the variable section

6) Move “gender” (or whatever you choose) into the “category axis” section.

7) Click OK.

8) In order to format in APA, double click on the graph in SPSS. Change each axis to read what you would like them to read.

9) Close the “chart editor” and copy and paste your final graph from SPSS into your document of choice.

10) Have a drink to congratulate yourself on a job well done. Please note: this step is not in the latest version of APA).

''contributed by Chris Longo''

==What is a Box Plot and When is It Used==

The box plot or box-and-whisker plot is a graphic, created by John W. Tukey, used to show the distribution of a set of data. It is frequently used with data that can also be represented with a histogram, but the box plot shows more information than a standard histogram. For example, the box plot is useful to researchers because it shows extreme scores.

[[File:box-plot-explained.gif]]

==How to Read a Box Plot==

Let's say we ask 282 people how many pairs of shoes they've consumed in the past ten years. We'll sort those responses from least to greatest and then graph them with our box-and-whisker. See the example above.

Take the top 50% of the group (142) who bought more pairs of shoes; they are represented by everything above the median (the white line). Those in the top 25% of shoe buying (71) are shown by the top "whisker" and dots. Dots represent those who bought a lot more shoes than normal or a lot less than normal (outliers). If more than one outlier bought the same number of shoes, dots are placed side by side.

''contributed by Michael Minzloff''

-
Box and Whisker plots show the variability of a data set. In order to make a box and whisker plot, you need to know
Five Number Summary
1) Least value
2) Greatest Value
3) Quartile 1
4) Quartile 3
5) Median
For example, if you had a set of numbers and sorted the following from least to greatest on basketball scores for your team during the school year.
14, 15, 20, 26, 27, 30, 30, 30, 33, 35, 36, 38 (least number would be 14, Q1 =23, median =30 Q3=34 and greatest value is 38)
Show your kids to make a number line. In this instance, it would be counting up to 38 from 14 by 2's
Put a dot above 14 and above 38 which are your least and greatest.
Then put vertical lines above your Q1 which is 23, Median which is 30 and Q3 which is 34. Connect the lines to make a box with 23, 30 and 34. Then extend a vertical line from 23 to 14. This is the whisker because it is outside the box. Do the same with the other side of your box which would be to extend a line from 34 to 38. That is the other whisker.
There you have it! You have not only learned how to read The Box Plot and but also how to create it. A box and whisker plot. Fun!

** Contributed from Tania Nicole Sutherland**

==How-to Video==

How to create a box and whisker plot using SPSS [http://www.youtube.com/watch?v=lRaMDHiIvc4&feature=youtu]

''contributed by Jen Eraca''

The Box Plot

2022-04-18T18:31:14Z

TaniaNicole: /* How to Read a 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''

==Creating a box and whisker plot using SPSS==
(Refer to emailed file for screen-shots and further assistance)

1) Open SPSS and EXCEL

2) Copy the data (into SPSS) that you would like to use. For example, test scores disaggregated by gender. Make sure that you assign numbers to gender (In example below: 1 = male; 2 = female).

3) Once data is entered into SPSS (as depicted above), click on: “Graphs  boxplot”

4) Click “define” (with “simple” & “summary for groups of cases” chosen)

5) Move “test” (or your variable of choice) into the variable section

6) Move “gender” (or whatever you choose) into the “category axis” section.

7) Click OK.

8) In order to format in APA, double click on the graph in SPSS. Change each axis to read what you would like them to read.

9) Close the “chart editor” and copy and paste your final graph from SPSS into your document of choice.

10) Have a drink to congratulate yourself on a job well done. Please note: this step is not in the latest version of APA).

''contributed by Chris Longo''

==What is a Box Plot and When is It Used==

The box plot or box-and-whisker plot is a graphic, created by John W. Tukey, used to show the distribution of a set of data. It is frequently used with data that can also be represented with a histogram, but the box plot shows more information than a standard histogram. For example, the box plot is useful to researchers because it shows extreme scores.

[[File:box-plot-explained.gif]]

==How to Read a Box Plot==

Let's say we ask 282 people how many pairs of shoes they've consumed in the past ten years. We'll sort those responses from least to greatest and then graph them with our box-and-whisker. See the example above.

Take the top 50% of the group (142) who bought more pairs of shoes; they are represented by everything above the median (the white line). Those in the top 25% of shoe buying (71) are shown by the top "whisker" and dots. Dots represent those who bought a lot more shoes than normal or a lot less than normal (outliers). If more than one outlier bought the same number of shoes, dots are placed side by side.

''contributed by Michael Minzloff''

-
Box and Whisker plots show the variability of a data set. In order to make a box and whisker plot, you need to know
Five Number Summary
1) Least value
2) Greatest Value
3) Quartile 1
4) Quartile 3
5) Median
For example, if you had a set of numbers and sorted the following from least to greatest on age and years
14, 15, 20, 26, 27, 30, 30, 30, 33, 35, 36, 38 (least number would be 14, Q1 =23, median =30 Q3=34 and greatest value is 38)
Show your kids to make a number line. In this instance, it would be counting up to 38 from 14 by 2's
Put a dot above 14 and above 38 which are your least and greatest.
Then put vertical lines above your Q1 which is 23, Median which is 30 and Q3 which is 34. Connect the lines to make a box with 23, 30 and 34. Then extend a vertical line from 23 to 14. This is the whisker because it is outside the box. Do the same with the other side of your box which would be to extend a line from 34 to 38. That is the other whisker.
There you have it! A box and whisker plot. Fun!

==How-to Video==

How to create a box and whisker plot using SPSS [http://www.youtube.com/watch?v=lRaMDHiIvc4&feature=youtu]

''contributed by Jen Eraca''