Tips and Guidelines

Chapter Four: Results

Chapter Four continued…

Setting Up the DataIntroduce the research question which will be

used as the basis for the data you are to present.

Present the data to support the question in as many ways as you can.

Examples of ways to present the data are the use of tables and graphs.

Tables are created using Microsoft Word to display only the data that is needed.

Explain the data completely objectively – no analysis of why these results are found.

Example of Data PresentationIntroductory ParagraphA simple study was conducted to examine the

implications of technology when it is used in a teacher’s classroom. First, a teacher of Language Arts gave a diagnostic reading test using STAR Reading software to measure the students’ reading and grade placement levels. The test was first administered prior to the two teacher’s classes reading three novels. The test was then administered again after the novels were read by the two classes. One of the classes was instructed using a variety of technological sources. The other class read the novels orally with no use of technology. The students’ reading levels in the class that was instructed with the use of technology had a greater gain than the class that was taught using no technology.

Example of Data PresentationGraphFigure 1: Results of the STAR Reading

Pretest and Posttest STAR Reading Results

6.19

6.55

7.24 7.19

5.50

6.00

6.50

7.00

7.50

1 2

1=Technology Used 2=No Technology Used Blue=Pretest Red=Posttest

Presentation of Data continuedDiscussing the GraphFigure one shows the results of the pretest

and the posttest using the STAR Reading assessment tool. The teacher’s class that used technology while reading the three novels shows a positive 1.05 reading level increase while the class that used no technology shows a .64 reading level increase. Group One is a full inclusion class with a mixture of regular education students and special education students while Group Two is a regular education class.

Presentation of Data continued…Using Graphs continued…

Figure 8: Average Comprehension Scores of all Guided Reading Groups

 

aboveonbelow

Reading Level

100.00

80.00

60.00

40.00

20.00

0.00

Mea

n

94.4091.00

82.29

97.20

92.17

61.86

Mean Scores of the Guided Reading Groups

posttestpretest

Presentation of Data cont…Explaining GraphsFigure Eight shows the mean pretest and

posttest Scott-Foresman results for all guided reading groups. Students reading below grade level made an average gain of 21% in comprehension. The students reading on grade level on the average made a 1% decrease in comprehension. The students reading above grade level made a 3% decrease in comprehension.

Table 2: Relationship between Student Behavior and Technology and Behavior and Student Interest in LessonVariable Pearson

CorrelationSignificance

Student Behavior and Technology

.459 0.01

Student Behavior and Interest in the Lesson

.459 0.01

Explanation for TableTable Two shows the relationship between

the statements if my teacher used more technology, I would behave better, and I often act up in class because class is not interesting. The Pearson Correlation was .459 with a significance of 0.01, which showed a positive correlation between the two variables.

Types of Tests to ConsiderBivariate Correlation – Pearson Correlation: To demonstrate the use of bivariate correlation, you

will explore the inter-relationships among some of the variables included in your survey and assessments. Before you begin this kind of testing, you would probably want to look through your results and see which factors you feel will probably should so interrelationship. This relationship could have a positive or negative correspondence. For example, the results of both questions chosen should yield the same score because of their similarity, or the results of both questions chosen should yield exact opposite results because they express opposite beliefs.

Bivariate Correlation – Pearson Correlation: continuedPearson correlation is used when you want to

explore the strength of the relationship between two continuous variables. This gives you an indication of both the direction (positive or negative) and the strength of the relationship. A positive correlation indicates that as one variable increases, so does the other. A negative correlation indicates that as one variable increases, the other decreases. Everyone would be able to try their hand at performing this test because all surveys have variables that can be tested for their interaction and relationship to each other.

T-tests:

T-tests are used when you have two groups (males and females) or two sets of data (before and after) and you wish to compare the mean score on some continuous variable. There are two main types of t-tests. Paired sample t-tests (also called repeated measures) are used when you are interested in changes in scores for subjects tested at Time 1 and then again at Time 2 (often after some intervention or event). The samples are related because they are the same people tested each time. Independent samples t-test are used when you have two different (independent) groups of people (males and females), and you are interested in comparing their scores. In this case you only collect information on one occasion, but from two different sets of people. Most, if not all of you, would only be dealing with the paired sample t-tests.

Example of SPSS Output DataDescriptive Statistics

Mean Std. Deviation NORAL13 3.61 1.20 54Written14 4.00 1.05 54

Summary of StepsOrganize Chapter Four information into

subheadings.Use your research questions to introduce, create,

organize, present, and explain your data.Look to have at least 2 different displays of data

(graphs, charts, and/or tables) for each set of data that elucidates a research question.

Among the tests to try are frequencies, means, Pearson Correlations, and T-tests.

Include mostly significant results, but it may be appropriate to include insignificant results as well.

Summary of Steps continuedTo decide which factors to examine, make a

list of positive or negative correlations that you expect to see among different variables. Use these as your impetus to input data.

For numerical data or scales, consider frequencies and means.

Don’t forget to look at anecdotal data for trends to see if it makes sense to use SPSS to analyze. Otherwise, stick to using it for explanations for Chapter Five.