Data analysis and Interpretation

Students welcome to data analysis and interpretation so now that most of you have gone out and started to collect your data let's see what the next phase will probably look like the purpose of this lecture is to start making some sense of the data that you've collected this is to see how your intervention went and how it performed then the neck the next part you know once you get it all together is you analyze it and so the analysis helps you answer some of the questions that you were talking about the whole time as you were developing your program which is has the program made a difference and then also how big was this difference whether was it whether it was an attitudes behavior or even the knowledge that you changed steps in this process so when we talk about data there's a couple of steps that I'd like to bring your attention to keep in mind is organizing it so how are you gonna store it or are you gonna keep it because that makes it easy for you to start preparing the data so that you can start describing it then when you describe the data you know kind of like just see see what you got looking at what you got you start to interpret and this is what's gonna be really cool because when you were putting when you're making when you're writing your objectives and when you were creating your pretest post-test that's what you were doing to help you assess and evaluate your process assess and evaluate your the change that you were gonna make so when we talk about quantitative data that talks about specifically like the number sign in other words any degree of change that has taken place is more number based and then allows to allows an assessment to be made about the consistency of the data talk about qualitative data so with qualitative data.

This here is important because it helps you look at common patterns this is also important because it's used in in forums where you're collecting information like when you did your interviews so there's not a rule number thing that you can do there all the time sometimes people just give you kind of opinions and what they think of what they feel can't really put a quantitative value to that so that's called qualitative data and I want to introduce the the notion of theme analysis so a theme analysis is essentially what qualitative data is a theme analysis is when you look through qualitative data so all the quotes kind of like when you do your end of semester evaluations we ask that you give us some feedback and it's like open space where you type things up we look for theme analysis and I'll have some examples of this there we go so an example is for example like 10 3 you would ask include any additional comments regarding the lectures and if you see the three bullets this is showing very raw results it's you know just what people said one very knowledgeable but hard to follow this is with regards to lecture expert in the field but not delivering material effectively as well as no doubt very knowledgeable but dry delivery so the next step would be to analyze and interpret this so you're looking at it you see this theme so going back to theme analysis the theme is you know it does seem that this person is knowledgeable that does not seem to be a problem in terms of content a area of improvement would be in the delivery so that would be something to keep in mind now that you've kind of looked at it this way the next step would be to and interpret interpret your data then asking the question so why is this so and then searching for solutions so you look at your results in the past you've looked at your results from the needs-assessment findings and now you're going to be looking at your results from your pre and post-tests project questionnaires so things to keep in mind how many individuals responded this is what we call the end which I'm sure you're familiar with and just as you're looking at everything putting everything in a table you know probably doesn't make a lot of sense yet but you're just looking at everything that's called the raw data here are some examples of how some people may collect demographics so you'll see they're just you know put in all the different race or ethnic categories and then you tally it up and that's your n on the left hand side or a different way to collect data besides grab a bar graph would be something like a pie chart so this is where they collected like body index for their project and this is what it looked like then your results could also look like this this is an example of priam post-test results very raw data and they went ahead and put the question on the left hand side and then they did two columns for a pretest post-test corrects as you can see if you're just quickly glancing at it you can get an idea that there was a difference there was a degree of change in the work that they did after their intervention so for analysis and discussion you want to ask yourselves where your project goals and objectives met was there an impact what did you find so interpretation of what the data is telling you this is kind of gonna come gonna be up to your discussion this is part of the discussion when as a group you start looking at what you know did our program have an effect and in some cases it may have a positive effect and may have negative effect or may have no effect in hindsight this is very important I was talking to a lot of groups throughout and saying that it's very important that if you know once you gather your data and you look at your pre and post-test is what may have affected the outcome so was there anything that was missing anything that you would do differently and did you discover that conclusions can't be drawn because you're missing data that you should have collected so this is really cool because this is where now that you've done it all in hindsight you think ah we should have asked the question this way or we should have asked this question instead of this or that and a lot of times this becomes just the case where you have a clear sense of direction once you've went ahead and piloted something for the first time okay so this is a very busy looking slide however I feel they did a nice job explaining that changes in the end so there was somewhat of a change between their pretest and their post test participant numbers and also they show some analysis which is the interpretation of what the data is telling you so you can go ahead and take some time or rewind this video to watch this particular section to study it a little bit to get some ideas for yourself however we'll go ahead and move on now when we look at outcomes when you are discussing the outcomes you know based on what you gathered just keep in mind that there's such things as short-term outcomes which includes mostly changes in skills attitudes and knowledge medium-term outcomes which then starts to talk about behavior and decision making some maybe some higher level stuff that takes a longer time to solidify and then the long-term outcomes which would be when someone say like in maintenance persistence of behaviors and broader lifestyle changes those are kind of like longer what you would do with your patient visit after visit after visit so which you would work on on a long-term so they may take time and you know if you know sometimes patients or your participants your community will get to the end goal that you know you had in mind when you started this project but it will take some time and you may not be there to see that however you may want to focus more on the short-term stuff which is how most of your objectives were written so we're almost done now just in relation to your rubric.

I want to go direct you to your rubric which is on blackboard under the assignments in rubrics folder and go into the section that talks about the presentation rubric and there's a section there that talks about analysis and discussion because you will be graded on this when you do your presentation and focus on these particular questions where your project objectives met and to what extent describe to what extent your objectives were met and then also include how many examples were met based on the data if you read this particular example in quotes from years past.

I'd encourage for example if you were writing something like this I would encourage you courage a more clear answer so in answering and say yes or no and which if any were met or were not met and then to what extent continuing on with the rubric present obstacles or barriers including solutions so important so important so important and this is where a lot of the learning actually takes place on how to improve the process for next time also the other part in the rubric that we want you to refer to is called the the star star wish format so refer to your rubric and so here we want to see if the group shared a star star wish where a star is how you made a difference or an impact or how your project proposal made a difference or an impact and then the next would be your wish what we would have done differently saying next time so this is very important and considering how you're refining your project and refining refining your process for next time considerations as you're writing your just analysis and thinking about your discussion piece correlation should be identified and discussed including expected and unexpected results so sometimes we learn things that we didn't really plan for or we get some more insight so that the insight that you gather is almost I would argue sometimes as important as what you were looking for and then also going back to goals and objectives when you're discussing it I mean when you're presenting this to some extent you know you want to continue to discuss the even I'm sorry you want to continue to talk about your goals and objectives and your discussion analysis and look show us the evidence I guess is the better way to put it show us evidence of how you met some of these goals and objectives or how you you know you probably didn't and then also if you did not meet them propose a rationale maybe say in once you've reflected as a group and maybe individually as well you know what do you think would have made a difference if you were to do this again what why do you think maybe some of the objectives were not met alright that's about it try to keep it really short for you if you have any questions you know we're available you can email me start me in the hall.

Kona mission lab hours just reach out we'll find a way and nice job keep going you're almost done with all of the stages and talk to you on Monday February 8th for some of you and then other results also on February 9th take care.