One-Way ANOVA Video

The video below demonstrates how to conduct a One-Way ANOVA:

One Way ANOVA from Statistics Solutions on Vimeo.

Conduct and Interpret a One-Way ANOVA

One Sample t Test Video

The video below demonstrates how to conduct a one sample t test.

One Sample t Test from Statistics Solutions on Vimeo.

For more information on conducting and interpreting a one sample t test, please click here.

Conduct a Paired T-Test Video

The video below demonstrates how to conduct a paired t-test.

Paired t Test from Statistics Solutions on Vimeo.



Statistics Solutions

Capella University and the Scientific Merit Review

For the past 20 years Statistics Solutions’ mission is to help graduate students graduate.  Whether you go to Berkeley or Capella, students need help.  Students (“learner” always reminded me of Milgram’s 60’s Obedience to Authority study) at Capella however have a couple of things working against them.  First, they’re not on campus to get the help they need, and second, they’re paying tuition as the process continues.  One of the places students get stuck is writing aspects of the Capella’s Scientific merit review (SMR).

My staff and I have worked with over 2000 graduate students, and despite the resources at the universities, some still need help from an objective, non-evaluative professional.  We are such professionals!   When we work with students they typically get stuck in the same few places:  research questions, proposed data analysis, and the target population and participation selection. 

Research questions are easy to handle: make sure the constructs (your measures) are obtainable and measure what you want to measure, AND you arrange these constructs in statistical language.  For example, if you have constructs A and B, and want to relate them (read “correlate” them), then say that.  If you are assessing whether A predicts B (read “regression”), then say predict, impact, or account for variability in B.  

Capella’s Scientific Merit Review also asks for a data plan.  When it comes to data analysis plans, these plans are based on two things: the statistical language you used in the research questions and the level of measurement of your variables.  We have resources on our website or if you need more 1-to-1 help you can go to click here.  By the way, Capella will send you back for a round of revisions (tuition not included) if you don’t have this correct.  When I went to school 100 years ago, the IRB which we would have sent our SMR to, made sure that we didn’t hurt our participants but now they look at everything.  And let’s face it, the revision costs you both time and money. 

Sample size is typically trickier still (even with the help of G-power).  There are two tricks: selecting the right analysis (see data plan above) and selecting the effect size.  Effect size can be derived by looking at past research using these constructs and analyses, then calculating or seeing the effect size used.  There’s also a realistic aspect too:  for dissertations—and I’ve seen 1000’s of them—large and medium effect sizes, requiring relatively small sample (under 100 participants),  is the norm.  Requesting a small effect size (small effect take a lot of people to detect) requires typically 300-500 participants—and this is just not reasonable for a dissertation student to obtain.  Here are a couple of resources (sample size tool; power analysis) to get you started.  I should note that the exception is when you are conducting EFA, CFA, path analysis, and structural equation modeling; these techniques typically require 150 or more participants. 

I’m going to leave you with a Dissertation Template to look at.  It’s free and you may find some definition of terms helpful.  

Good luck with your Scientific Merit Review and call us if you run into trouble.  Contact us at: http://www.statisticssolutions.com/contact or call us at (877) 437-8622 (M-F, 9-5 EST)

PS:  A Stanford Ph.D. student just called; their private stats consultant just took another job.  See, everybody needs help sometimes, even schools with lots of resources!

 

Data Analysis

Data analysis is a procedure of collecting and analyzing raw data by interpreting the inference out of raw data. Data analysis is one of the important aspects of the analyst’s work. Data analysis plays a crucial role in deciding whether or not the retrieved data is reliable.

Data analysis is basically a two-step procedure that involves collecting and analyzing data. Data analysis can be explained with the help of the following example:

Suppose a researcher has conducted a survey in order to know if the manufacturing of auto parts in an auto industry is more in Pune or in Chennai. The first step of data analysis is to collect the data through primary or secondary research. The next step of data analysis is to make an inference about the collected data. The second step of data analysis in this case will involve SWOT Analysis. SWOT Analysis stands for Strength, Weakness, Opportunity and Threat of the data under study.

Primary research in data analysis is the one that involves collection of data through questionnaires or telephone interviews. Secondary research in data analysis is the one that involves collection of data using the internet.

There are basically two types of data analysis. These two types are as follows:

Qualitative data analysis: This kind of data analysis is the one that consists of an unstructured, exploratory research methodology based on small samples intended to provide an insight into the problem being solved.

Quantitative data analysis: On the other hand, this kind of data analysis seeks to quantify the data and typically involves some form of statistical data analysis.

Quantitative data analysis can be performed in those cases when one needs to get statistical inferences about the data. In such cases, data analysis is done by using some statistical techniques. These statistical techniques include Factor Analysis, Discriminate Analysis, etc.

A technical analyst performs data analysis by interpreting the charts using a time series technique, and he/she forecasts the price trends of a particular commodity or share. Thus, data analysis can be used to forecast about the data as well.

Data analysis is an integral part of every research work. The validity of data can be known only through data analysis.

In statistics, data analysis is done on quantitative data. Data analysis in relation to quantitative data analysis can be divided into descriptive statistics, exploratory data analysis and confirmatory data analysis.

Descriptive Statistics in data analysis involves techniques like mean, median, mode, variance, standard deviation, etc.

Exploratory data analysis involves the following steps:

· Formulation of a problem in data analysis.
· Identifying alternative courses of action in data analysis.
· Developing hypotheses in data analysis.
· Isolating key variables and relationships for further examination in data analysis.
· Gaining insights for developing an approach to the formulated problem in data analysis.

Sometimes, qualitative data analysis is undertaken to explain the findings obtained from quantitative data analysis. Thus, one can say that both qualitative data analysis and quantitative data analysis are interrelated with each other.

Data analysis is also synonymous to data modeling. Data modeling is a process in which a perfect model (which represents the data as a whole) is being fitted during the data analysis.

For information on statistical consulting, click here.