As one part of the business intelligence magic quadrant, a simple analysis can be powerful to evaluate activities such as extension. Extension activities or technology area assistance usually expect an improvement in insight and technology. Then the question usually arises: how do we evaluate the results of these mentoring activities? And usually, we conduct satisfaction surveys on some of the performance carried out during mentoring. The problem is that sometimes we are asked to evaluate some obstacles and take corrective steps to overcome them. A satisfaction survey that contains a percentage of perceptions is certainly not enough to describe this. Satisfaction survey techniques only capture the satisfaction scale in one dimension; there needs to be an additional dimension, such as the level of importance, and be able to summarize both dimensions so that it can be used to evaluate mentoring or extension activities.
The technique in question is Important and Performance Analysis (IPA), a quadrant technique that connects two subjects or two objects to assess the effectiveness of an activity. For more information about the material and further information, you can use Google to find reference materials and more quality literature. I wrote this article to directly practice with the data you have or to help you make a little research framework for technology area assistance or extension activities that you will do.
IPA analysis is essentially combining two objects or two subjects. You can define two objects as farmers and extension workers, or two subjects as the level of satisfaction and the level of importance of extension activities according to farmers.
If you use two objects, farmers and extension workers, then only one research subject should be used, for example only the satisfaction level variable. This means that you want to compare the level of satisfaction of extension activities between the perception of farmers and the perception of extension workers. The difference in perceptions obtained is a discussion of the success or constraints of the extension activities carried out.
If you are using two subjects, importance and satisfaction, then you should only use one subject, i.e., the farmer. This means that you want to get information about farmers’ perceptions of whether the extension variables are important and satisfied. The difference between the two is a discussion of the constraints and success rates of extension activities.
You can also do it both ways, meaning you want to discuss the differences in perceptions between extension workers and farmers, and you also want to discuss the gap between the level of importance and the level of satisfaction felt by farmers as objects of assistance or extension.
This tool is very effective, and I use it quite often. Almost every year, I use this tool because most of my activities involve mentoring. Here are the outputs that I have produced in the assessment of the attributes of superior rice seeds in Maluku Province.
However, in this article I will use fictitious data as an exercise considering that the results of the above study are still in the review process, so I cannot publish them here.
Based on the picture above, there are 4 quadrants, each of which has its own meaning.
Quadrant 1 = Top priority
In this quadrant contains the value of farmer desires / high level of farmer interests while low satisfaction. Variables included in this quadrant become a top priority as an evaluation or correction material so that the activities in question can run optimally. If the research or data collection is done at the end of the activity, it can be an evaluation for the next activity how to increase satisfaction on the variables included in quadrant 1. If data collection is done in the middle of the activity, then there is still time to make efforts to increase farmer satisfaction, for example with in-depth interviews what obstacles faced, and so on.
Quadrant 2: maintain achievement
This quadrant is the ideal quadrant. You can expect that all variables are included in this quadrant because it consists of variables that have a high level of importance and a high level of satisfaction. This means that farmers consider the variables in this quadrant to be very important, and their performance is very satisfactory.
Quadrant 3: Low priority
This quadrant consists of variables that have a low level of importance, and satisfaction is also low. This means that farmers think that the variables in this quadrant are not important, so they don’t need to strive for more. Increased satisfaction with the variables in this quadrant can lead to excessive categories.
Quadrant 4: Excessive
This quadrant contains variables that are considered unimportant or less important but highly satisfactory. You should focus on quadrants 1 and 2 because increasing the level of satisfaction means meeting farmers’ expectations. However, on the other hand, we can evaluate whether the variables we evaluate are included in the actual category. It is possible that farmers consider certain variables unimportant, while we as mentors think they are very important. This can identify the success of activities in changing farmers’ perceptions.
Processing data for this analysis is primary data that generally comes from questionnaires. In the questionnaire, respondents are asked about how important and satisfied they are with the performance of certain attributes or variables on a Likert scale.
The scale is 1–5, with 1 = not important, 2 = less important, 3 = quite important, 4 = important, and 5 = very important.
Likewise with the level of satisfaction: 1 = not satisfied, 2 = less satisfied, 3 = moderately satisfied, 4 = satisfied, and 5 = very satisfied.
Data recapitulation and scoring
Primary data from observations are recapitulated and processed to obtain a score for each variable at both the importance and satisfaction levels.
Score Value (x) = (n x scale (x)) / N
n is the number of respondents who stated the response (x), scale (x) is the size of the scale value for criterion x (1 – 5), while N is the total respondents.
Then the total in each variable is summed from score 1 to score 5.
Confused? Here’s an example
The data I have:
Determine the value of each score (1 – 5)
Then add up the total score.
Want an easier way? You can just average the values after entering the scale data into the initial data. The result is the same. Take a look below.
It appears that the results are the same between the average and the score value formula to get the importance and satisfaction levels for a particular variable.
Do the same for the satisfaction level. Then the data is ready to be processed into quadrants
IPA Analysis Steps
The steps for making IPA quadrants are as follows:
Prepare data consisting of level of importance and level of satisfaction. As I explained earlier, you can also make 2 objects with 1 subject, for example the level of satisfaction between extension workers and farmers. But the example here is 2 subjects with 1 object.
This time I used SPSS. This is fictitious data for example only with some variables that I have used in 2016. Then click Graphs – Legacy Dialogs – scatter dot
Enter the importance level on the Y axis and the satisfaction level on the X axis. Then click OK. Then a plot diagram appears
Double-click the image to exit the chart editor. Click elements in data label mode to label each point. Then click each point in the image, and each description will appear. After all appears again, click elements—data label mode. Remove the data leak mode checklist and click Apply and Close in the small windows that appear.
Then click option – X axis reference line
We can just fill the x line with the median or mean value. This time I filled in the value 3 because the value 3 is the middle value of the scale 1–5. This means that the point on the right means more than enough (satisfaction scale) and the point on the left means less than enough.
Then click Apply and Close.” Next, click Options: Y-axis reference line. Still in the chart editor. This line is the center line of the Y axis, or the importance level axis. This means that the point above this line means that it has more than enough importance, and conversely, the point below this line means that it has less than enough importance.
Fill in 3 because 3 is the middle value of the Likert scale used. You may use the median or mean option, depending on your research model.
Then click apply and close. The IPA quadrant is ready for interpretation.
The results of the IPA quadrant in this exercise show that:
Quadrant 1: variable 3 (plant age) and variable 4 (growing power)
Quadrant 2: variable 1 (productivity) and variable 2 (pest resistance)
Quadrant 3: variable 8 (variety type) and variable 7 (packaging quality)
Quadrant 4: variable 5 (fertilizer efficiency) and variable 6 (storability)
I have explained each quadrant at the beginning of this article. If you find this article useful, please help spread the word. Thank you for visiting. The video in this article is about important performance analysis, commonly referred to as quadrant analysis, complete with variable names in the chart.