Howdy folks, and welcome to the last episode before the Midterm! I know, you're all very excited for that... but pump the brakes because before we get there we need to finish our discussion of probability and the normal curve. In this episode, we review our learning about those topics and connect it to the use of z-scores, which are a way of telling us how different a specific value is from the mean. Z-scores are a measure that uses standard deviation--that value we went over all the way back in week 2! We will spend time defining z-scores and then working on calculating them... and then connecting it back to probability or the chances that our data value is normal or rare. Put on your thinking caps, push through the material, and as always let me know when and if you have questions!

Hiya folks, and welcome to one of our final podcast episodes before the Midterm Exam! In this episode, we are going to focus on 3 key points: (1) language that we use when describing samples versus populations (estimates and parameters); (2) a discussion and review of the basics of probability; and (3) connecting probability and the normal curve. There's a lot of conceptual leaps at times with these topics... so take your time with this one. And focus on concepts--thinking of the normal curve as covering 100% of our outcomes (aka linking probability with the normal curve) and then the ways that the Central Limit Theorem and Law of Large Numbers help us connect our samples back to our population. Let me know what questions you have as you make your way through this episode!

Hello again everyone, and welcome to our Week 6 episode on research and statistics! In this episode, we will walk through the research process broken down here into 7 steps: creating the question, completing a literature review, designing our study, specifying our measurements of variables (as well as our hypotheses), collecting our data, analyzing that data, and finally offering an interpretation/conclusion that addresses our research question. This episode is a bit on the long side, but it will hopefully serve you well as a guide for our Initial Draft assignment and in descriptions of the structure for the Midterm Exam!

Hi everybody, and welcome to our week 5 discussion of data quality! In this episode, we've really only got two major concepts to understand: reliability (aka consistency) and validity (aka accuracy). Of course, there are different ways to figure out our reliability (test-retest, interrater reliability, etc.) and validity (content, criterion, and construct) and we can certainly use SPSS to assist us with this... calculations like Cronbach's alpha are not something we should do by hand! What's most critical for this topic: get the general gist of these terms, understand how we can test or evaluate each quality measure, and then use the last few slides to 'test' your knowledge of validity measures!

Hi all, and welcome to a one-off episode on statistics terms! This episode is not part of our normal progression through the course but I did want to provide more discussion and explanation of terms you'd noted as difficult to understand. The episode starts with central tendency (including arithmetic mean and percentile ranks), then moves to variance (including exclusive range, inclusive range, mean deviation, and standard deviation), and wraps with some terms that don't fit neatly into one category (inferential statistics, independent variables, dependent variables, unbiased estimates, scales of measurement, and operational definitions). Email me about any terms that are still difficult to understand and I'll do my best to expand on this episode!

Hey there all, and welcome back for Episode 6 on correlation! For this week and episode, we're going to cover a few initial concepts that need to be more directly discussed: variables (including independent and dependent variables) and then the levels of measurement (nominal, ordinal, interval, and ratio). Next, we'll get into the definition and looks of correlation (as they would appear on a scatterplot), tie back how correlation requires us to have a true variable with different values, learn about the Pearson correlation coefficient, and the ways we can measure correlation when our variables aren't numbers (ex: Phi coefficient, Rank biserial coefficient, etc.). We'll wrap up with discussion of why we shouldn't confuse causation with correlation through examination of spurious correlations. Throughout this episode, I'll be asking you to think about the ways your research project relates back to the concepts... so be prepared to think about the question you want to investigate!

Howdy ho everyone! Welcome to our Week 3 episode on data visualization! In this episode, we will talk all things graphing-related: which graphs are best for showing raw data (histograms), showing change over time (line graphs are great at this), providing comparisons (bar or area charts), and more. In addition, I've included a link to a video on how graphs can be used to misrepresent data or make it harder to correctly interpret--I do hope you make time to watch it as it's only a few minutes but covers some great pointers! This episode also provides tips on how to do good visualization and then walks through some examples of graphing and data transformation in SPSS. Let me know if you have questions and if you're experiencing any difficulties using the SPSS software to create graphs this week!

Hello stats course members, and welcome to episode #4! In this episode, we spend a little over 20 minutes talking about different measures of variation or the measures we can use to understand how different the values in our dataset are from one another. The statistical measures we use for understanding variation include the range, standard deviation, and variance. We will go over the definition/explanation of each term and then we will do some practice (both by hand and in SPSS) for calculating standard deviation and variance. As always, let me know of any questions or concerns and I'll get back to you as soon as I can with more clarification and guidance!

Howdy all, and welcome back! In this episode, we begin our discussion and learning about measures of central tendency which are part of what we would consider to be descriptive statistics. We are specifically focusing on the mean, median, and mode. This episode will walk through the definition of each measure, an explanation of how we calculate them, and then some practice exercise using Excel and SPSS to find the mean, median, and mode of different datasets. In addition, we'll cover a bit about the normal distribution and when it's best to use the median or the mean. Be sure to install SPSS using the SPSS Download and Licensing Instructions on the Week 2 Roadmap Page if you're in the online only version of this class and let me know if you have any difficulties with that. The episode includes a lot of practice as well as answers for different portions of those practice exercises, so do take a few minutes to listen in and get that information!

Hi all, and welcome to the second episode of our podcast on Statistics for the Social Sciences! In this episode, we're going to take a little bit of time to further discuss/investigate the syllabus but the majority of this podcast is going to focus on defining some key terms: descriptive statistics, inferential statistics, sample, and population. We'll also spend some time walking through the basics of the research process as well as the types of questions we'll look into as part of your larger research paper. This episode wraps with the overall plan for the rest of the class and a quick note about SPSS, the statistical software we'll be making good use of to do the heavy lifting of statistical calculations. As you listen to the episode, do email me ([email protected]) if you have any concerns or questions come up and I will assist you as quickly as I can!