How do I determine if a questionaire I want to use has 'good psychometric properties'? (Jessica, United Kingdom)

Thanks for the great question, Jessica! Typically, when the term psychometrics is used in the context of survey research, it is in reference to a survey instrument's reliability. The most common statistic used to determine a survey instrument's reliability is Cronbach's alpha. Cronbach's alpha is a statistic that evaluates how much individual survey items covariate with one another to predict a single construct. In English, that means it is a test how much a group of survey questions measures the construct they are intended to represent.
As an example, if an assessment of depressive symptoms contains 10 items, a Cronbach's alpha for those 10 items is a measure of how well the group of those 10 items (as a whole) represents a respondent's level of depressive symptoms . If you've not yet collected data, the best way to determine an instruments psychometrics is to review previous studies that have used the survey and calculate what the average Cronbach's alpha was among them. Cronbach's alpha typically ranges from 0 to 1 (although negative numbers are possible, they are usually meaningless), with values closer to one indicating stronger reliability. There is no official value that indicates strong reliability, but a review of the literature does show some general conventions on the topic.
Commonly, a Cronbach's alpha in the range of .70 to .79 is considered adequate, a value in the range of .80 to .89 is considered good, and a Cronbach's alpha in the range of .90 to .99 is considered excellent (an alpha of 1.00 is most likely an error or an indication that something is wrong with your data). Another test of reliability includes test-retest reliability (which uses a correlation to test for agreement between two measures of the same construct). If you've already collected your data, the statistic can generally be easily obtained using any statistical program (such as SPSS or SAS). I hope that was helpful and please keep the questions coming!

Survey Data Analysis Question: How do I know if I should be using Exploratory Factor Analysis (EFA) or Confirmatory Factor Analysis (CFA)? (Jamie, Phoenix, AZ)

Fantastic question Jamie! The decision about whether to use EFA or CFA isn't always a clear cut one. At it's most basic statistical root, EFAs are  useful when you do not have an a priori hypothesis about how a set of items should be grouped together to measure unique constructs, but you think there are some distinct constructs that can be measured amongst a set of items. By contrast, a CFA is more appropriate when an a priori hypothesis exists about the structure of the data (the hypothesis may be rooted in a conceptual framework, prior EFA analysis, or both).

With that said, you are likely to see EFA used when some hypotheses exist about a set of items, so the above rules are not always rigid. The key in your decision is: what is the question you are trying to answer? If your research question is one of an "exploratory nature", then an EFA may be your best choice. However, if you are seeking to test an existing theory, hypothesis, or test competing models/structures, a CFA is what you are looking for. When sample size is abundant, one can randomly split their sample and extract a factor structure from the first half of their data (using EFA) and then test that structure, using a CFA on the second half of their data! If this remains unclear, feel free to send along more specifics (perhaps in the forum) and I'll try to offer a bit more guidance.