What is exploratory factor analysis (EFA)?

Exploratory Factor Analysis is an item-reducing strategy intended to create factor scores. For example, if you have a 100 question survey, you probably don’t have 100 unique constructs/factors. Factor analysis would “boil-down” these 100 questions to perhaps 10 subscales or constructs.

Two important issues in Exploratory Factor Analysis is how many factors there are, and which questions load on (or relate) to that factor. There are several ways to determine the number of factors: scree plot, 70% or more of the variance accounted for, etc. After the number of factors is determined, the questions that go with each factor can be determined by taking your sample size, noting the critical value for a correlation given that sample, and doubling it. If you had 50 observations (participants), the critical value of the correlation at an alpha of .05 is .361; doubling this value equals .722. That is, questions that have a loading of .722 or more would go with that factor.

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What does a bivariate correlation indicate?

A correlation, or bivariate correlation, measures the relationship between two variables. The correlation measures the strength of the relationship.

The strength of a correlation ranges from the absolute value from 0 to 1; the closer the correlation is to 1, the stronger the relationship, the closer the correlation is to 0, the weaker the relationship. For example, the relationship between temperature and ice cream cones sold may be .80. This indicates a strong relationship. The direction can be positive or negative. For example, the positive correlation in the ice cream example is positive; as the temperature increase, ice cream cones sold increase. A negative correlation may be found between spending and saving in the bank; as spending increases, saving decrease.

There are other correlations, such as partial correlations, point-biserial correlations.

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What can a dissertation statistical consultant do for me?

A dissertation consultant should have been through the process of a dissertation because we know the challenges of committees and the importance of timely feedback.

Your consultant must be able to clarifying the research questions, assist with selecting the appropriate statistics, select the correct sample size given the statistical analyses selected, conducting the statistics while examining their assumptions and remedies of violations, write-up the results with APA tables, and clearly explain what the finding indicate. Further, you should get continued support!

If you have questions, feel free to contact Statistics Solutions for a free consult. Our phone number is (877) 437-8622 or email us at: www.StatisticsSolutions.com.

The best in finishing your dissertation!

How do I select a statistical consultant?

It only makes sense to hire a company where the consultant has dealt with dissertation committees (i.e., the consulting company have employees who have a doctorate degree) and has expertise in statistics (Ph.D. is preferred). Confirm their credentials—if they say they have a degree from Harvard Department of Statistics—call Harvard’s statistics department top confirm (I have a Ph.D. in Clinical Psychology from an APA-accredited program at Miami University in Oxford, Ohio).

Make sure the company is responsive, patient with answering your questions, and trust your experience with the potential company. Remember, you are the customer!

If you have questions, please feel free to call us at 877.437.8622 or visit our website at http://www.statisticssolutions.com/

How do I assess the assumptions of an independent sample t-test or ANOVA?

There are three main assumptions in these statistical techniques. The first, and most important, is independent of observations. That is, one participant does not influence the score of another participant. The second assumption is homogeneity of variance. ANOVA assumption can be tested by checking the SPSS box for “homogeneity tests.” For t-test, SPSS provides the Levene test as a default. The third assumption is normality assumption. This can be tested with a one-sample KS test. For more information you can email us at www.StatisticsSolutions.com or call 877-437-8622.

How do I calculate the sample size for my research?

Sample size is an elusive topic. While completing my master’s and doctorate degrees, the issue never arose! First, the sample size is determined in coordination with a preselected power (typically .80), alpha (typically .05), and an effect size (typically .50). Secondly, the sample size is different for different analyses (e.g., for multiple regression, t-tests, etc.) At www.StatisticsSolutions.com we use Cohen’s articles on statistical power and G-power (online) to calculate the required N. For more information you can email us at www.StatisticsSolutions.com or call us at 877-437-8622.