دليل المعلم 2020 2021 دراسات اجتماعية منهج إنجليزي صف حادي عشر فصل ثالث
CASE STUDY: JADID TRADING LLC (CONTINUED)
After confirming that the unit of analysis as employee performance, and the units of observation related to other details about sales staff, you are nearly ready to start the analysis process. However, first you need to identify the level of measurement relevant to the data set. Keep in mind there might be different levels of measurements that are best suited to individual questions. Therefore, a questionnaire can include multiple levels of measurements. Here is some more information about levels of measurements. Apply this information to complete related activities
Level of Measurement: Nominal data
Nominal data is basic classification data. The simplest level of measurement usually includes names. By itself it does not have any quantitative value and cannot be measured. Instead, these can be grouped together to measure other variables. For example, fathers and mothers, males and females, Rome and Venice, can all make up nominal data. They can be grouped. For example, males and females can be used to measure other variables. One example could be male and female students' scores in an English exam. In this example, the nominal data (male and female) is being used to label other variables, like scores in an English exam. This type of data can be grouped together into categories to analyse each category. For example, the percentage of males in the English class
Level of Measurement: Ordinal data
Ordinal data is data or information in a logical order. For example, very sad, sad, happy, very happy are usually used in scaled questions (1 to something), where the difference between values follows a logical order, and used to rank information. But, the difference between them is not necessarily the same. For example, the number of people who said between somewhat satisfied, and satisfied. This type of data is usually presented in tables, charts, bar graphs, etc. Lastly, this type of data is not constant, meaning the values change with the variables. For example,
DATA TABULATION, FREQUENCY DISTRIBUTIONS & PERCENT DISTRIBUTIONS
Deciding on the level of measurement you need to create tables for your raw data is an important step in data analysis. It further develops your understanding of the data and helps in identifying patterns. This process also confirms the total values in each category
You need to identify which questions go together, and have the same level of measurement. Then start with the basics and calculate frequency (how many times something happens), and percent distributions (how much of the total does each category represent)
Note to students. At this stage it is important to have transferred all the data onto excel as you will be completing a number of exercises based on this data below. Alternatively, you can use graph paper, calculator and additional resources to complete manually, but that will take you much longer
Frequency distributions are presented in a table with the number of individual scores that can be grouped in the same category. Since there are too many different joining dates in the data set, it is useful to group them in a logical order. In this case, 2 weeks apart is one way of doing it
This process of tabulation makes it easier for the researcher to understand the date, identify mistakes, and start identifying patterns. Researchers would be developing these tables, finding frequencies, assigning percentages and preparing graphs all at the same time. This is an important part of the data analysis process as it allows the researcher
to adjust how the data is displayed. For example, you would have noticed how the questions 2,7 and 8 have been displayed differently compared to how they were posed in the questionnaire. Why do you think this is
PERCENT DISTRIBUTION
A percent distribution shows the quantity of respondents who are represented within each category. For example, question 8 shows 40% of respondents live in Dubai while 60% live in Sharjah. As nominal measures, they will not tell you anything more until you compare and contrast with other information
Lesson 4: Methods of Quantitative Data Analysis
manageable format that might show patterns. Once similar exercises are done for all questions these, patterns might be easier to see
DESCRIPTIVE ANALYSIS (CONTINUED)
As you can you see, some descriptive statistics are better suited to specific questions. It should now also be clearer that these descriptive analyses only describe the numbers in the variable. Up to now, you have been looking at these variables individually. This is also known as univariate analysis. Working out the mean has enabled us to better understand individual responses when compared to the whole, even though you might still be focusing on a single variable. For example, in question 3 or 4, there is a better understanding of what is going on. The tables you made earlier can help enhance this understanding
While the mean scores are important, it is vital to remember that they only provide a description of what the variable is showing. They do not explain the rationale or reasoning behind those numbers. If the researcher or the person analysing the data does not understand the question, then it can all go wrong. For example, in question 4 of the questionnaires the employees were asked "how satisfied would you say you are with your line manager
The average for this question was 2.8, very close to the median of 3. So, over all it does not look so bad. But go back to the actual questionnaires What does 2.8 really mean? Upon closer inspection, you can see that on average employees are either "not satisfied" or just "somewhat satisfied" with the manager. Consider the rating I which basically says, "not sure." then the average of 2.8 is even worse
This is where students really start to apply data analysis and start process of identifying patterns. Teachers need to guide students through this process
These are very hands on lessons. The students should already know how to apply these basic methods. Therefore, the teacher should encourage
Social Studies Gradell - Teacher Guide Term 3
have access to categorical variables, which are represented in qualities or characteristics. For example, joining date
satisfaction with manager, and such
In the Jadid Trading L.L.C. case study you are trying to work out possible reasons for the poor performance of sales staff in the company. Although you have identified a number of mistakes and drawbacks in the questionnaires and subsequent data set, you still have access to a combination of variables that you can use to find relationships and patterns that might explain the poor performance
Finding Patterns in Data
Up to now you have been looking at individual variables, but this approach is limited to describing the data. In this lesson of quantitative data analysis, you will learn about combination of variables. This will allow us to find relationships and patterns within the data more clearly. This process also allows the researchers to justify their findings and make recommendations
From this basic analysis you should have been able to notice that the data and analysis just tells you whether there is a relationship and the type of relationship. It cannot tell you why the relationship is there. The "why" is best answered by using qualitative methods that seek to understand why people behave in the way that they do. This fact marks one of the biggest differences between qualitative and quantitative research. However, you can analyse the data to make predictions and start to form conclusions
Descriptive analysis continued. This is where students really start to apply data analysis and start process of identifying patterns. Teachers need to guide students through this process. These are very hands on lessons. The students should already know how to apply these basic