Archive for the ‘Lessons and Seminars’ Category

How to Use a Semi-Colon

Often underused, misused, or simply feared, the semi-colon is a versatile punctuation mark that may be employed in two distinct ways.

First, and most commonly, a semi-colon connects two independent clauses. (As a quick refresher, an independent clause is a phrase that can stand on its own as a sentence. For example, “The French bulldog puppies scrambled for the new toy” is an independent clause, but “Whenever the French bulldog puppies scrambled for the new toy” is not.) Though these clauses may lack a coordinating conjunction between them—“and,” “but,” “or,” “nor”—they should be related in meaning. For example: “The woman hated attending hockey games; she would begin shivering in the stands within minutes.”

Second, a semi-colon may be used within a list to separate items that already use commas. For example: “While visiting colleges my parents and I visited Cambridge, Massachusetts; Princeton, New Jersey; and Philadelphia, Pennsylvania.” Often, when used in this way, the semi-colon is informally called a “super-comma.”

For more information on correct grammar, be sure to visit Purdue University’s Online Writing Lab (OWL).

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Sites consulted for this article:

Inman, Matthew. How to use a semicolon. The Oatmeal, 2013 (copyright). Web. 29 April 2013.  <http://theoatmeal.com/comics/semicolon>.

Purdue Online Writing Lab. The Writing Lab, The OWL at Purdue, and Purdue University, 1995-2013 (copyright). Web. 29 April 2013. <http://owl.english.purdue.edu/engagement/index.php?category_id=2&sub_category_id=1&article_id=44>.

Rubin, Jeff. The Semicolon. National Punctuation Day, n.d. Web. 29 April 2013. <http://www.nationalpunctuationday.com/semicolon.html>.

 

By Katie | Wednesday, May 15th, 2013 | No Comments »

Statistics: On Multiple Regression

For the final installment in this series on introductory statistics for the social sciences, Kyle T. begins discussing multiple regression, a statistical test and topic that can be subject to much more advanced investigation by those students who are interested in learning more about this subject. In short, multiple regression extends regression in a way similar to how factorial AnoVa extends AnoVa; multiple regression allows prediction based on a number of different, independent variables.

We hope that you’ve enjoyed this overview of statistics, geared for social-scientific students and research, this Spring.  We’re excited now, leaving Memorial Weekend and entering June, to be transitioning into a new series, geared for premedical students, preparing for the MCAT this Summer. Stay tuned, eager minds; the Veritas video series will return anon!

Warmly yours,

The Veritas Team

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By admin | Tuesday, May 31st, 2011 | No Comments »

Statistics: On Regression

As promised, this week Kyle T. addresses regression, a more complex correlation model that does allow for interpretations of causality.  However, despite its relative complexity, regression is still just, as Kyle says, “glorified slope-intercept”! Following this logic, you’ll be up and running these analyses in no time.

Hoping you’re enjoying the Spring!

The Veritas Team

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By admin | Tuesday, May 24th, 2011 | No Comments »

Statistics: On Correlation

Robust like the non-parametric statistics that Kyle T. discussed last week, correlational analyses, Kyle’s topic this week, provide the raw materials for the much vaunted regressive analyses, his topic next week.  In essence, as Kyle explains, correlations generally assess how two sets of patterns of data line up next to one another; and, as he emphasizes, however they line up does not say anything about however they may arrive in that relationship: Correlation does not describe causality.

However, the two are related; to learn how correlation and causality do connect, be sure to check back in on our next installment in this series: Regression.

Warmly yours,

The Veritas Team

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By admin | Tuesday, May 17th, 2011 | No Comments »

Statistics: On Non-Parametric Statistics

This week Kyle T. addresses the other major type of statistics: non-parametric statistics.  As he explains, non-parametrics allow for more robust testing than parametrics, since only non-parametrics can test with non-normal distributions and with ordinal (or less) dependent variables.  However, as he adds, non-parametrics’ robustness is counterbalanced by their loss of statistical power; the “collapse” of levels of granularity or fineness within the information gathered about the groups being tested eliminates much of the incisiveness of the tests’ results.

Next up: Correlation!

Warmly yours,

The Veritas Team

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By admin | Tuesday, May 10th, 2011 | No Comments »

Statistics: On Factorial AnoVa

This week Kyle T. expands on the basic AnoVa that he’s presented over the past two weeks and shows how it may be used to test groups that differ one more than one categorical variable. This expansion is called the Factorial AnoVa and can be used in between-subjects, within-subjects, and mixed research designs.

Next week: Non-Parametric Statistics!

Statistically yours,
The Veritas Team

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By admin | Tuesday, May 3rd, 2011 | No Comments »

Statistics: On Analysis of Variance (AnoVa – Part Two)

Wrapping up his explanation of the AnoVa, Kyle T. this week presents his third way of understanding the intention of the statistical test by graphically modelling distributions to be compared: By looking at distributions as representations, the comparison becomes clearer than the abstract concepts in consideration.

See you in May!

The Veritas Team

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By admin | Tuesday, April 26th, 2011 | No Comments »

Statistics: On Analysis of Variance (AnoVa – Part One)

This week, as promised, Kyle T. introduces the extension of the t-Test, the Analysis of Variance (AnoVa), meant for comparisons among more than two participant groups. His explanations of the AnoVa tie onto the heart of the test, discerning the variance within groups from the variance between groups; he turns to information theory for its descriptive analogy, educing a signal from noise.

Next week: A brief continuation of explaining the AnoVa, including graphs!

Warmly yours,

The Veritas Team (more…)

By admin | Tuesday, April 19th, 2011 | No Comments »

Statistics: On t-Testing (Part Two)

Continuing last week’s discussion of the t-test, Kyle T. describes the Paired or Dependent Samples t-Test and the ways in which it relates to its already presented sibling, the Independent Samples t-Test. Though this relationship is not sequential as have been the relationships between the other previously presented tests, the two tests nevertheless logically support one another and continue the progression of complexity in statistical analyses that he’s been developing so far.

Next week: AnoVa!

Hoping that you’re enjoying warm weather this Spring,

The Veritas Team

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By admin | Tuesday, April 12th, 2011 | No Comments »

Statistics: On t-Testing (Part One)

Hey, statistics students and aspiring social scientists; it’s t-Test week!

This week Kyle T. presents the basics of the t-test, the most foundational analysis in inferential statistics. Thus, this test and its consequential relatives will be the analyses that you conduct most often in your own research. Exciting!

Happy April,

The Veritas Team

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By admin | Tuesday, April 5th, 2011 | No Comments »
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