This document provides guidance on conducting data analysis. It discusses the importance of carefully checking data multiple times. The purpose of data analysis is to answer research questions and determine relationships among variables. There are steps to follow both before and after data collection, including determining the analysis method, processing data, interpreting findings, and presenting results. Descriptive statistics summarize sample characteristics through frequencies, averages, and variability measures. Inferential statistics are used to draw conclusions about populations based on samples through statistical tests and evaluating hypotheses.

1. Data Analysis
Zeynab Moosavi

2. Be very accurate and careful
when working with the data
Check
Double-check
Triple check
Quadruple check
Data Analysis

3. The purpose
a) To answer the research
questions
b) to help determine
relationships among the
variables.

4. Steps in Data Analysis
Before Data
Collection
After Data
Collection

5. Before Data Collection
 Determine the method of data analysis
 Determine how to process the data
 Consult a statistician
 Prepare dummy tables

6.  Process the data
 Prepare tables and graphs
 Analyze and interpret findings
 Consult again the statistician
 Prepare for editing
 Prepare for presentation
After Data Collection

7. Data Analysis
Coding data
write a code number on each completed
questionnaire (unique identifier)
Assign numbers to the response items
If the question isn’t answered, leave the cell blank
Calculating descriptive statistics

8. Blank Excel Worksheet

9. Create a data base title
https://www.wcasa.org/file_open.php?id=951

10. Missing Data
 Participants do not answer certain items on a
questionnaire
 it is not clear what the correct answer is
 not be able to read the answer
 they might have circled two responses
 in pre- & post-test experiments, participants
complete one phase of the study, such as the pre-
test, but are absent for the second phase

11. The items are reverse coded.
Refers to the items that actually say the opposite
of what was intended.
For example,
"I enjoy going to school” Strongly
Disagreeing
the other item is "I dislike school Strongly
agreeing
Reverse Coded Items

12. Kinds of Data Analysis
Descriptive
Analysis
Inferential
Analysis

13. Descriptive Statistics
are numerical values obtained from the sample that
gives meaning to the data collected.
Descriptive Analysis
 refers to the description of the data from a
particular sample;
 hence the conclusion must refer only to the sample
 these summarize the data and describe sample
characteristics

14. frequencies
central tendency
(averages)
variability
Types of Descriptive Statistics

15. Frequency
the number of participants who
indicated that category

16. central tendency (averages)
1. Mode
2. Median
3. Mean

17. variability
Statistics that concern the degree to which the
scores in a distribution are different from or
similar to each other

18. Inferential Analysis
The use of statistical tests, either to test for
significant relationships among variables or to
find statistical support for the hypotheses.
Inferential Statistics
are numerical values that enable the researcher
to draw conclusion about a population based on
the characteristics of a population sample.


19. the researcher draws conclusions - or
inferences - about the entire
population based on the results from
the sample.

20. The purpose is
to determine whether the
findings from the sample can
generalize - or be applied - to the
entire population

21. Inferential statistics is used to determine
whether the difference between the two
groups in the sample is large enough to be able
to say that the findings are significant.
If the findings are indeed significant, then
the conclusions can be applied, or generalized,
to the entire population. On the other hand, if
the difference between the groups is very
small, then the findings are not significant and
therefore were simply in the result of chance.

22. This p-value is
the probability that the result is due to chance.
The p-value can range from 0.000 to 1.000. The
larger p is, the more likely the results are due to
chance.
If p is 0.50, the probability that the result is due to
chance is 5 out of 10.
A p of 0.850 means that the probability that the
result is due to chance is 85 out of 100
The standard in research is that the p
must be is less than 0.05 for the results to
be significant.

23. if the calculated p-value is less than
0.050, then the null hypothesis is
rejected. If the statement "There is no
significant difference" is rejected, this
means that there is a significant
difference. It can therefore be concluded
that a difference exists between the two
groups in the population, not just the
sample.

24. P-value
p<0.05
Null
hypothesis is
rejected
p>0.05
Null
hypothesis is
retained

25. Create Tables and Figures
 Every table must be discussed in the text.
 Refer to tables by the table numbers only, not where
they appear in reference to the text.
This is acceptable: "As can be seen in Table 3..."
This is not acceptable: "The table above shows..."
The rationale for this rule is that the placement of the
table may change when the final document is produced.
 Readers should be able to interpret a table just by
looking at the table, without reading the body of the
research report itself. Therefore, each table should
have a clear title that focuses on the key statistics
within that table and all acronyms and abbreviations
should be explained in the table notes.

26. Rules
 Table titles should be brief, but clearly explain the
table.
 All similar entries in the table should carry the
same number of decimal points
 Every entry within the same column should have
the same number of decimal places.

27. P-values & correlations should be rounded to three
decimal places (thousandths place value)
All other numbers (means, standard deviations, t-
values, F-values) should carry two decimal places
(hundredths place value).
If you are not the original author of the table, you
must cite the source of the table in a note at the
bottom.

28. Once the tables have been placed in the text,
number the tables starting from 1 in the order
that they appear in the work.
The decimal points must be lined up in each of
the columns. This makes the table easier to
read.
Tables typically have a horizontal rule at the
top, bottom, and after the table labels. Vertical
lines are rarely used according to APA
regulations

29. Figures
• Graphs effectively illustrate means, frequencies, and
percentages
• figures should also be fully understandable without
reading the text, but also be referenced in the text
• Figures should also be numbered consecutively
• Notes should be included so the reader can easily
interpret the figure
• Figures do not have titles
• A caption or legend at the bottom of the figure
functions both as a title and an explanation of the

32. The Results section should be
structured around:
• the demographics of the participants
• reviewing the purpose of the study
• answer each Research Question one by one
• Explain the statistic used to answer the Research questions
• Explain the measurement of each variable
• Present the statistics in a Table or Figure
• Interpret the statistics.

33. Errors in reporting of the Results:
A lack of clear reporting:
Numbers are reported in a table, but it is unclear
how the numbers were calculated based on the
items from the questionnaire
The statistics are not thoroughly explained:
Research hypotheses particularly need a detailed
explanation to interpret the key findings.
To repeat all statistics from the Table/Figure
in the body of the text.