Blog Index – July

2014, Current to 7/28

Topic	Dates of Posts
Dissertation, general	6/25
Selecting a Topic	4/28, 5/9
Organization
Committee Members	5/7
URR
Center for Research Quality
Overview of Process
Premise
Prospectus	4/4, 4/7, 4/9, 4/11, 4/14, 4/18, 4/21, 4/23, 4/25, 4/28, 5/2, 5/5
Proposal
Research questions	4/18
C. 1
C. 2 (literature related)	6/9, 6/11, 6/16
C. 3	1/3, 1/6, 1/13
Defense
IRB	1/10, 1/15, 1/17, 1/20, 1/22, 1/24, 1/27, 1/29, 2/3, 2/5, 2/7, 2/10, 2/12, 2/17, 2/19, 2/21, 2/24
Data Collection
Quantitative	1/3, 2/26, 3/12, 7/9, 7/14, 7/16, 7/18, 7/21, 7/23, 7/25, 7/28
Qualitative	1/6, 3/14
Mixed Methods	1/3, 1/6
C. 4	3/5, 3/10, 3/12, 3/14, 3/17, 3/19, 3/21
C. 5	3/21, 3/24, 3/26, 3/28
Final Defense
Career
Goal Form	5/23, 5/26, 6/2
Motivation	1/1, 6/4, 6/6, 7/4, 7/11
Secondary Data	2/24
Support, Getting	5/21
Writing	5/16, 5/19, 6/16, 6/18, 6/20, 7/2, 7/7
Other	2/14, 3/3, 3/7, 4/16, 5/12, 5/14, 5/28, 6/2, 6/13, 6/23, 6/27

 2013

Topic	Dates of Posts
Dissertation, general	7/5, 8/16, 8/19, 9/27, 10/2
Selecting a Topic	4/23, 7/8, 7/10
Organization	4/22, 10/2
Committee Members	4/17, 5/3, 6/10, 7/19, 8/21
URR	5/8, 5/27
Center for Research Quality	12/9
Overview of Process	4/19, 9/18, 12/13
Premise	4/17, 9/6
Proposal	4/22, 9/9
Research questions	10/9
C. 1	5/6, 10/21, 10/23, 10/25, 10/28, 11/1
C. 2 (literature related)	4/26, 5/29, 6/3, 6/12, 6/17, 6/28, 9/16, 10/11, 11/4, 11/6, 11/9, 11/15
C. 3	5/1, 10/16, 10/28, 11/18, 11/20, 11/22, 11/25, 12/2, 12/4, 12/6, 12/11, 12/16, 12/18, 12/20, 12/23, 12/27
Defense	4/23, 5/8
IRB	5/10, 10/14
Data Collection	5/13, 5/15, 10/16
Quantitative	5/17, 7/24, 7/26, 7/29, 7/31, 8/2, 8/5, 10/4, 10/7, 11/20, 12/2, 12/4, 12/6, 12/18, 12/23, 12/27
Qualitative	5/20, 11/20, 11/22, 11/25, 12/11, 12/16
Mixed Methods	5/22, 11/18, 11/20, 11/22, 11/25, 12/11, 12/20, 12/23, 12/27
C. 4	5/17, 5/20, 5/22, 7/17
C. 5	5/24, 9/20, 10/11
Final Defense	4/23, 5/27/ 9/11
Career	7/12
Goal Form	8/12
Motivation	6/5, 6/26, 7/1, 8/16, 8/23, 9/2, 9/18, 10/18, 11/8, 11/27
Secondary Data	5/31
Support, Getting	4/26, 6/5, 6/24, 8/16
Writing	4/26, 4/29, 6/12, 6/21, 7/3, 8/9, 8/14, 9/4, 9/23,9/25
Other	4/18, 6/7, 6/14, 6/19, 6/24, 6/26, 7/1, 7/8, 7/15, 7/19, 7/22, 8/7, 8/16, 8/19, 8/26, 8/28, 8/30, 9/2, 9/13, 9/18, 10/18, 11/27, 12/13, 12/25

 Next time I will begin a series on organizing your research process. Do you have an issue or a question that you would like me to discuss in a future post? Would you like to be a guest writer? Send me your ideas! leann.stadtlander@waldenu.edu

 

Extreme Scores: Effects and Causes

Extreme scores can cause serious problems for statistical analyses. They generally increase error variance and reduce the power of statistical tests by altering the skew (making the data "lean" in one direction, rather than clustered in the middle) or kurtosis (when the distribution is squashed down or has a very high narrow peak) of a variable . This can be a problem with multivariate analyses. The more error variance in your analyses, the less likely you are to find a statistically significant result when you should find one (increasing the probability of a Type II error).

 

Extreme scores also bias estimates such as the mean and SD. Since extreme scores bias your results, you may be more likely to draw incorrect conclusions, and your results will not be replicable and generalizable.

 

Extreme scores can result from a number of factors. It is possible that the extreme score is correct- an example is although the average American male is around 5' 10" there are males that are 7' tall and some that are 4 foot tall. These are legitimate scores even though they are extreme.

 

Another cause of an extreme score is through data entry error, someone that was actually 5' 6" tall may be incorrectly entered as 6' 5". So the first step is to always double check that extreme scores were entered correctly. A third cause may be that participants purposefully report incorrect scores. It can also happen that a participant accidently reports an incorrect score. Thus, an extreme score that has been entered incorrectly may need to be removed.

 

The info in today's post comes from Osborne (2013).

Osborn, J. W. (2013). Best practices in data cleaning. DC: Sage.

 

Next time I will post an updated blog index. Do you have an issue or a question that you would like me to discuss in a future post? Would you like to be a guest writer? Send me your ideas! leann.stadtlander@waldenu.edu

Making Data Make Sense- Extreme Scores

What are extreme scores? They are scores far outside the norm for a variable or population, leading to the conclusion that they are not part of your true population and probably do not belong in your analyses. A common operationalizing definition for extreme scores is +/-3 standard deviations (SDs) from the mean.

Recall that standard normal distribution of a population has 68.26% of the population between +1 and -1 SD of the mean (see attached diagram: 34.13% between 0 to +1 SD +  34.13% between 0 and -1 SD = 68.26%).

So 95.44% of the population should fall between 2 SD from the mean (34.13% + 34.13% + 13.59% +13.59% = 95.44%), and 99.74% of the population should fall 3 SD of the mean. In other words, the probability of randomly sampling an individual more than 3 SD from the mean in a normally distributed is 0.26%, which gives good justification for considering scores outside 3 SD as suspect. Our concern is that these scores are not part of the population of interest in your study, but instead are different in some key way.


Next time we will consider how extreme scores affect statistical analyses. Do you have an issue or a question that you would like me to discuss in a future post? Would you like to be a guest writer? Send me your ideas! leann.stadtlander@waldenu.edu

Missing Data as a Variable/ Best Practices

You may wish to examine missing data an outcome itself, as there may be information in the missingness. The act of failing to respond vs. responding might be of interest. This can be examined through a "dummy variable," representing whether a person has missing data or not on a particular variable. You can then do some analyses to see if there any relationship that develop.

Osborne (2013) provides some best practices in dealing with missing data that are great to remember.

• First, do no harm.be careful in your  methodology to minimize  missing data. 
• Be transparent. Report any incidence of missing data (rates by variable, and reason for missing data if known). This can be important information for readers. 
• Explicitly discuss whether data are missing at random (i.e., if there are differences between individuals with complete and incomplete data).  
• Discuss how you, as the researcher, dealt with issue of incomplete data. 

Osborn, J. W. (2013). Best practices in data cleaning. DC: Sage.

Next time we will consider outliers or extreme scores. Do you have an issue or a question that you would like me to discuss in a future post? Would you like to be a guest writer? Send me your ideas! leann.stadtlander@waldenu.edu