PRACTICAL SKILL DEVELOPMENT (PSD) ON PRACTICE OF DATA MANAGEMENT AND ANALYSIS USING DIFFERENT SOFTWARES.

1. PRACTICAL SKILL DEVELOPMENT (PSD)
ON
PRACTICE OF DATA MANAGEMENT AND ANALYSIS USING DIFFERENT
SOFTWARES
Mohammad Aslam Shaiekh
Master of Public Health Program
School of Health and Allied Sciences
Faculty of Health Sciences
Pokhara University
Kaski, Nepal
May 2019

2. PRACTICAL SKILL DEVELOPMENT (PSD)
ON
PRACTICE OF DATA MANAGEMENT AND ANALYSIS USING DIFFERENT
SOFTWARES
Mohammad Aslam Shaiekh, 18700003
A PSD report
Submitted
(In Partial Fulfillment of the Requirements for the Degree of MPH 2nd
semester, Advanced Public
Health Research, PSD 524)
Master of Public Health Program
School of Health and Allied Sciences
Faculty of Health Sciences
Pokhara University
Kaski, Nepal
May 2019

3. i
APPROVAL
Mr. Mohammad Aslam Shaiekh has prepared the PSD report entitled “Practice of data management
and analysis using different software.” The PSD report has been prepared and presented for the
partial fulfillment of the requirement for the degree of Master of Public Health (MPH) and
forwarded for final evaluation.
_____________________
(Dr.Tulsi Ram Bhandari)
Date:
Master of Public Health (MPH) Program, School of Health and Allied Sciences, Faculty of
Health Sciences, Pokhara University, Pokhara Metropolitan-30, Kaski, Nepal.
This report has been reviewed and accepted
Accepted with condition
Not accepted
External Examiners
1.Name: _____________________ Signature: __________________ Date: _________
2. Name: _____________________ Signature: __________________ Date: __________
_______________________ ______________________________
(Mr. Chiranjivi Adhikari) (Dr. Damaru Prasad Paneru) School Seal
Program Coordinator Director

4. ii
DECLARATION
To the best of my knowledge and belief I declare that this PSD report entitled “Practice of data
management and analysis using different software.” is the result of my own practical skill
development work and contains no material previously published by any other person except where
due acknowledgement has been made. This PSD report contains no material, which has been
accepted for the award of any other degree or diploma in any university.
Signature:
Mohammad Aslam Shaiekh
Roll No: 18700003
PU Regd No: 2018-4-70-0003
Date: 28th
May 2019

5. iii
APPROVAL.......................................................................................................................................................i
DECLARATION................................................................................................................................................ii
2. Create a Google Form & Spreadsheet to collect information.................................................................. 7
3. Create data entry form: Excel, SPSS, Epidata ......................................................................................... 15
Data export to text ..................................................................................................................................... 22
5. Data cleaning.......................................................................................................................................... 23
6. Normality test of data (in SPSS)/Methods of normality test................................................................. 29
7. Merge and split of data set/s in SPSS and epidata................................................................................. 32
8. Select cases in SPSS/filter option/create subsets................................................................................... 33
Creating a subset :...................................................................................................................................... 33
.................................................................................................................................................................... 34
9. Preparation of data analysis plan........................................................................................................... 34
10. Data analysis using Excel/SPSS/SAS...................................................................................................... 34
11. Type of Analysis: univariate, bivariate, and multivariate..................................................................... 36
12. Create, tables/Academic tables and figures in excel, SPSS.................................................................. 36
Creating figures in SPSS.............................................................................................................................. 36
.................................................................................................................................................................... 37
13. Sample size calculation......................................................................................................................... 37
14. Organization of reviewed literature using referencing software: Zotero............................................ 39

6. 1
1. Create/Adopt Interview Schedule/Guideline/Checklist
1.1 Define variable/item/construct:
A variable is a characteristics or attributes that can vary from person to person, from time to time or from
place to place. A variable is something whose magnitude can be changed and can takes on different values.
Variable is an empirical property that can take two or more values. These may be in the form of
numbers or non-numerical characteristics. For example weight, height, temperature, blood pressure etc
are the quantitative variables. Likewise, age, sex, education, occupation etc are the qualitative variables.
Variables can be classified in several ways. Some of the commonly used variables are as followings:
1. Discrete and Continuous variable:
2. Dependent and Independent Variable
3. Qualitative and Quantitative variables
4. Extraneous and Intervening variables
On the basis of characteristics variables are of two types i.e. Quantitative and qualitative.
Qualitative variables: Characteristics that is not measurable but expressed in description.For
example Sex, nationality, honesty etc. That have labels or names rather than numbers. Qualitative
variable can be categorize under Nominal and Ordinal.
i. Nominal: Only names the variable value. It classify the cases such as gender, caste, religion,
blood group. Each category can be assigned to a unique numeral. These variables are
distinct, exhaustive and mutually exclusive. There are two types of nominal variable i.e
dichotomous and multi/ polychotomous.
ii. Ordinal: Ordinal variable involves classification and magnitude. Those types of variables
with an ordered in series, mutually inclusive. For example Nutrition status: Mild, Moderate
and severe
Quantitative variables: Characteristics that is measurable and can be expressed in numerical form
e.g. height, weight, temperature etc. Arithmetic functions can be done. Quantitative variables further
classified into two types.
i. Continuous variable: Expressed in fractions or decimals eg. Body temperature, height,
weight etc.

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ii. Discrete (Numerical) variables: These variables can be expressed in whole numerical
value. Eg. number of death in a hospital per year, number of family member.
On the basis of relationship with each other
i. Dependent variables: Describe or measure the problems, depends upon independent
variables
ii. Independent variables Describe or measure the factors that are assume to cause or least
influence on dependent variables
iii. Confounding variables: An extraneous variable associated with the problem and possible
cause of the problem, may either strengthen or weaken the apparent relationship between the
problem and possible cause.
1. 2 Generation of items pool
Generation of items pool should reflect the focus of the scale. Generation of items pool is a
developing a series of statements relating to the variable being measured by using general criteria
for statement.
1.3 Reliability and validity measurement
Validity means that scientific observations actually measure what they intend to measure. Validity
refers to the soundness of the observations and to the accurateness of the data collected by research
methods/instruments.
Types of validity

8. 3
a. Face and content validity: An instrument is measuring what it supposed to is primarily based
upon the link between questions and objectives of study. Each question or item on the research
instrument must be in logical link with objective. Establishment of this link is face validity.
Item and question covers full range of issue or attitude being measured. Assessment of the items of
an instrument in this respect is called content validity.
b. Concurrent and Predictive validity is judged by how well an instrument compares with a
second assessment concurrently. Predictive validity is judged by the degree to which an instrument
can forecast outcome
c. Construct validity: this type of validity is a more sophisticated technique for establishing the
validity of an instrument. It is based upon statistical procedures. It is determined by ascertaining the
contribution of each construct to the total variance observed in a phenomenon.
Reliability: A result is said to be reliable if the same result is obtained when the study is repeated in
same conditions.
 Test/retest method
 Parallel forms of same test
 The split half technique
1.4 Psychometric analysis of the scale
In order for any scientific instrument to provide measurements that can be trusted, it must be both
reliable and valid. These psychometrics are crucial for the interpretability and the generalizability of
the constructs being measured.
Reliability is the degree to which an instrument consistently measures a construct -- both across
items (e.g., internal consistency, split-half reliability) and time points (e.g., test-retest reliability).
One of the most common assessments of reliability is Cronchbach alpha a statistical index of
internal consistency that also provides an estimate of the ratio of true score to error in Classical Test
Theory. A general rule of thumb is that solid scientific instruments should have a Cronbach‟s Alpha
of at least 0.7.
http://blog.motivemetrics.com/psychometrics-101-scale-reliability-and-validity
Test Procedure in SPSS Statistics
a. Click Analyze > Scale > Reliability Analysis

9. 4
b. Transfer the variable Q1 to Q10 into the items box. You can do this by drag-and-dropping the
variables into their respective boxes or by using the arrow. You will be presented with the
following screen:
c. Leave the Model set as "Alpha", which represents Cronbach's alpha in SPSS Statistics. If you
want to provide a name for the scale, enter it in the Scale box. Since this only prints the name
you enter at the top of the SPSS Statistics output, it is certainly not essential that you do (in
our example, we leave it blank).
d. Click on the statistics button, which will open the Reliability Analysis: Statistics dialogue
box, as shown below:

10. 5
e. Select item, Scale and Scale if item deleted options in Descriptives for area and Corelation item
in Inter item and click on Continue as shown below:
f. Click on Ok to generate Output .
g. SPSS Statistics produces many different tables. The first important table is the Reliability
Statistics table that provides the actual value for Cronchbach alpha, as shown below:
From our example, we can see that

11. 6
Cronbach's alpha is 0.805, which indicates a high level of internal consistency for our scale with this
specific sample
This column presents the value that Cronbach's alpha would be if that particular item was deleted
from the scale. We can see that removal of any question, except question 8, would result in a lower
Cronbach's alpha. Therefore, we would not want to remove these questions. Removal of question 8
would lead to a small improvement in Cronbach's alpha, and we can also see that the "Corrected
Item-Total Correlation" value was low (0.128) for this item. This might lead us to consider whether
we should remove this item.
Cronbach's alpha simply provides you with an overall reliability coefficient for a set of variables
(e.g., questions).

12. 7
2. Create a Google Form & Spreadsheet to collect information
2.1 Create form
Go to google page > Goggle apps > Drive > New > More > Google form

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14. 9

15. 10
For data entry click on preview

16. 11
Response summary

17. 12
2.2 Create spread sheet
Spreadsheet created

18. 13
Download as excel file in selective folder

19. 14
Color customization

20. 15
3. Create data entry form: Excel, SPSS, Epidata
3.1 Create data entry form in Excel
3.1.1 Leveling and form creation

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3.1.2 Data entry using form
3.1.3 Filter option
Select the row to activate filter .
Go to Data > Click on Filter as shown
3.1.4 Validation option (Range, dropdown menu)
For data validation,
Goto > Data > Select Data validation.

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A dialog box with data validation appears.
Go to setting > Select list among various option in Allow > Enter the variables in Source as shown
For a whole number : Select whole number in Allow > Select between in data and enter the
maximun and minimum value > Click ok
Conditional formatting
a. Open excel > Select the column
b. Select conditional formatting > Select (Format only cells that contains)
c. Enter the cell value less than and the 25000 as shown below > Click on format

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d. Select yellow colour to fill yellow colour who has salary less than 25000
e. Click on Ok

24. 19
3.1.5 Input message
Input messege is used to describe the variable
3.2 SPSS
3.2.1 Creating three files
Creating three files in SPSS. These are given below.
3.2.2 Data file, syntax file and output files
Data file: Open SPSS > File > New > Data.
Data file has two view one is variable view and another is data view. In variable view questionnaire
are develop and data are entered in data view.
Syntax file:
New syntax file are developed by using following command.
Open SPSS > File > New> Syntax file
Syntax file auto opens whenever a command is given.
Output file:
Any result of the command given is shown on output file.

25. 20
3.3 Epidata (overview of epidata)
3.3.1 Overview of work process tool bar
The Epidata interface
.
When a new form is opened you will see several toolbar options below the work process tools.
3.3.2 Creating three files
1. Creating new Qes file:
 Go to Define data > New Qes files
2. Creating data files from Qes files:
 Go to make data files > field pick list
 ID Number: <idnum>
 For string values of values: Shift_
 Defining attributes of variables: #
 Defining Numeric value: #
3. Check file:
 Go to checks > select file
 Range label for no. of attributes for specific variable (eg 1 Yes, 2 No )
 Input values or attributes > Accept and close
 Click edit > Type comment > Accept and close > save
 Jump file: Define jump – Eg: if answer is 2 (No) > q2

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4. Enter data:
 Go to enter data > select file > open > data entry and save at last in appropriate folder.
4. Export data:
 Go to export data > select file > define what you want to export in.
4. Data export/transform: Excel to SPSS, SPSS to Excel, Epidata to
Excel/SPSS/SAS/Text
4.1 Data transform from Excel to SPSS
a. Open SPSS > File > Open > Data> Select folder of data > Click on File types > Click on Excel
file in file name > Open > Tick on read variable names from the first row of data > ok.
4.2 Data export from SPSS to Excel
Open SPSS main file > then go to file > Save as > select SPSS file in file name> save as type >
Excel 2007 through 2010 XLSX save.
4.3 Data export from Epidata to Excel/SPSS/SAS/Text
1. Open Epidata > Export data > all the options can be selected as shown below
Data export to excel
2. Open epidata > Export data > Excel

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3. Select the rec file and Click on Open. A dialog box appears, it gives the option of exporting to a
specific place in the computer. You can also select records which ever you want to export and also
the variables and click on Ok.
5. You can find the excel file in the given destination.
Data Export to SPSS
1. Open epidata > Export data > SPSS
All the process will be same as the excel file but in the file name will be saved as .sps which means
it is a syntax file. While exporting to spss it will give you two file one will be syntax file and
another will be .txt file.
We need to open syntax file and run the command as per the instruction given .
Data export to SAS
1. Open epidata > Export data > SAS
All the process will be same as the excel file but in the file name will be saved as .sas .While
exporting to SAS it will give you two file one will be syntax file and another will be .txt file.
We need to open syntax file and run the command as per the instruction given.
Data export to text
Open epidata > Export data > Text

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Filename will be saved as .txt . Other steps are similar
5. Data cleaning
Data cleaning is preparing data for the analysis.
5.1 Check frequency (Find missing value and addressing it/replace missing value by mean)
Missing value
The reasons for missing data could be :
 Participants forgetting to answer the question
 Incorrectly answering the questionnaire
 Data entry error
Step 1
Check frequency of that variables
Analyze > Descriptive Statistics > Frequencies
Find the variable you need, click on arrow, and Ok
Output view is shown.
The frequency table above shows that there are two items missing DM item 1 and DM Item 2.
Check those two items to understand the reason of missing.

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In case of data entry error go back to your data sheet and re- enter. If participant left item blank you
need to use replace value functions.
1. Go to Transform > Replace missing value function
2. Select series mean in method

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Missing values has been replaced.
To make the data set clean the new values can be copied to the original items .
5.2 Find range/outliers and treating the outliers
To check for outliers in SPSS:
1. Analyze > Descriptive Statistics > Explore...
2. Select variable (items) > move to Dependent box.
3. Click Statistics... > tick Outliers > Continue... > OK.
4. In Output window: Go to Boxplot > Look at circles and *. These are potential outliers. If
there's none, then there is no potential outlier in your dataset. If there are circles or *, then
there are potential outliers in your dataset.
5. To check if the outliers affect your data:
In the output window: Look at Descriptive table > Compare 5% trim mean and mean values.
If there's a large difference between these values, then there's huge possibility that your
further analyses, e.g. correlation and regression, will be affected. 5% trimmed mean is the
mean that slashes out 5% of the extreme ends (both lower and higher ends) of your dataset.

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Treating the outliers
 Leave it if it is a legitamate outliers – use a non parametric test for skewed dataset
 Correct data entry error in case of error
 Winsorize the data (it means to trim the data )
 Remove the data from the dataset in case of illegimate outliers, it is necessary to explain
your reasons for removal of the data such as multivariate outlier
5.3 Reverse coding (Negative statement of Likert scale)
Questionnaires that use a Likert scale (eg. strongly disagree, disagree, neutral, agree, strongly agree)
for answering questions often contain some items which are to be reverse scored.
For example, in a self-esteem questionnaire we may have some positively worded questions (eg. I
take a positive attitude toward myself), but also some negatively worded questions (eg. At times, I
think I am no good at all).
In the above example, we might attribute an answer of strongly disagree with a score of 1, disagree
= 2, neutral =3, agree = 4 and strongly disagree =5 for each question. This would be fine for the
positively worded questions, as this would give people with high self-esteem a high score, however,
we can‟t use the same scoring for the negatively worded questions.
Instead what we do is reverse score the negatively worded questions. Reverse scoring means that the
numerical scoring scale runs in the opposite direction. So, in the above example strongly disagree
would attract a score of 5, disagree would be 4, neutral still equals 3, agree becomes 2 and strongly
agree = 1.
The same principle applies regardless of the length or wording of Likert Scale being used. For
example, we might have the following 7 point scale:
Disgusting Horrible Unpleasant Neutral Pleasant Lovely Adorable
1 2 3 4 5 6 7
...which for reverse scored questions becomes:
Disgusting Horrible Unpleasant Neutral Pleasant Lovely Adorable
7 6 5 4 3 2 1

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After you have reverse scored the necessary items in your scale, you can then calculate the total
score for your questionnaire.
Recoding into different variable
Current value labels
Go to Transform > Recode into different variable
In this example I want to reverse score a 5 point Likert Scale, so 1 becomes 5, 2=4, 3=3, 4=2 and
5=1. In the „Old Value‟ box on the left, enter 1 and in the „New Value‟ box on the right enter 5.
Then click Add to move the values into the „Old-->New‟ box. Repeat this process for all the values,
including those that will be staying the same (eg. 3 still stays as 3 in this example)

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A new variable has been added . Put the label and values
Remember to use these new re-coded columns in any total score calculations or analyses, not the
original columns.
Recode into same variable
You can also go to Transform – Recode into Same Variables to recode data, however this will
overwrite the original data, so if you are not confident with recoding data it is safer to use the
Recode into Different Variables option.

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6. Normality test of data (in SPSS)/Methods of normality test
6.1 Skewness and kurtosis
Skewness is a measure of symmetry, or more precisely, the lack of symmetry. A distribution, or data
set, is symmetric if it looks the same to the left and right of the center point.
Kurtosis is a measure of whether the data are heavy-tailed or light-tailed relative to a normal
distribution. That is, data sets with high kurtosis tend to have heavy tails, or outliers. Data sets with
low kurtosis tend to have light tails, or lack of outliers. A uniform distribution would be the extreme
case.
The histogram is an effective graphical technique for showing both the skewness and kurtosis of
data set.
6.2 Normality test
Step 1
Select "Analyze -> Descriptive Statistics -> Explore".
Step 2
From the list on the left, select the variable "Data" to the "Dependent List".
Click "Plots" on the right. A new window pops out. Check "None" for boxplot, uncheck everything
for descriptive and make sure the box "Normality plots with tests" is checked.

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Click on Ok
Step 3
The results now pop out in the "Output" window.
Step 4
We can now interpret the result.
The skewness and Kurtosis should be as close to zero as possible. In reality however the data are
skewed and kurtotic. A small departure from zero is therefore no problem, as long as the measures
are not too large as compared to their standard error.
We must divide the measures by its standard error. This will give the z value which is in between
+1.96 to – 1.96 .From above output,
Skewness= 0.374/0.365= 0.374
Kurtosis= 0.584/0.717= 0.81

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The value are in between +1.96 to -1.96. Hence the data are little skewed and kurtotic but they don‟t
differ significantly from normality.
6.2.1 Shapiro Wilk
The Shapiro wilk test p- value must be above 0.05 to reject alternative hypothesis and conclude that
the data comes from a normal distribution.
6.2.1 Kolmogorov-Sminov
Reject the null hypothesis if p> 0.05
6.3 Probability test plot
6.3.1 Q-Q Plot
The dots should be along the line for a variable to be normally distributed.
6.3.2 Box plot

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7. Merge and split of data set/s in SPSS and epidata
Merging file in SPSS
Open SPSS > go to file > open > data> example employee dataset 1 > merge file > add cases> An
external SPSS statistics file > browse > add cases > employee data set 2 > open > continue> Paste >
syntax > run > merge file > save as > concerned folder
Split of data in SPSS
Go to Data > Split file > Select compare groups > Select the variable (eg: educational level) > Click
on Ok
On the data viewer you can see split file by educational level on lower right corner. Further analysis
can be done for educational level.
In split file the option of output by groups can also be used instead of compare group it gives the
same result but only output view is changed.

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8. Select cases in SPSS/filter option/create subsets
Open SPSS data file then go to Data > Select cases > Select If condition satisfied > Click on If >
Select the variable > Example Gender> Write Gender =3 > Continue > Paste > syntax run > output
window
Filter on Option is shown on lower right. Repeat the same procedure and click on reset to turn the filter off.
Creating a subset :
Repeat the procedure of select cases > enter the option in if condition and then select in Output > Copy
selected cases to a new dataset > enter the name of the data set > Click ok
A new data set is shown in data viewer > Save the data file

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9. Preparation of data analysis plan
A detail plan is to be made on how to analyze the collected information to meet the desired
objectives of the study based on study variables. For this, study variables should be identified,
measurement scale of variables and univariate/bivariate/multivariate analysis plan should be
declared. For example:-
 Variables : Dependent (Outcome ) and Independent (Explanatory) Variables
 Measurement scales : Nominal, Ordinal, Discrete and Continuous
 Univariate analysis: Percentages, IQR, Mean, Median, Mode etc.
 Bivariate analysis: Chi-square tests, Binary logistic regression etc.
 Multivariate analysis : Multiple logistic regression
10. Data analysis using Excel/SPSS/SAS
10.1 Data analysis using excel
Analysis of data by using following command

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Open the Microsoft excel data file > click on data > go to Data analysis > then select data analysis
tool like descriptive statistics ( mean, median, mode, range, standard deviation),confidence interval,
t- test, Simple linear regression and scatter diagram etc as per need.
Analysis of descriptive statistics
Open the Microsoft excel data>then click on data> data analysis > analysis tool > select descriptive
statistics > Ok > Select column to calculate a mean median mode standard deviation and range >
input range > select all value of calculating column >Tick on label in first row > Output range
(click on empty box below the given column) > click on summary statistics then Ok.
After calculating the value of standard deviation and mean we can calculate coefficient of variation
through manually by applying the following formula. i.e = (Standard deviation/Mean)*100.
Analysis of confidence interval
Open the Microsoft excel data > select variable to calculate CI. For example we select age > then go
to data > data analysis > descriptive statistics > Input range > select all value of defined column >
tick on label on first row> Output range (click on empty box below the given column) > click on
summary statistics > Confidence label for mean 95% then Ok.
t-test analysis: two sample assuming equal variances
Open the Microsoft excel data file > select a variable having two options > go to data > Sort > sort
by > select variable which you prefer > then go to order click on smallest to largest > Ok> then go
to ms excel > Type select variables of option 1 in one column and option 2 in another column >
then copy and paste a given value in select variable then go to data > data analysis> t test two
sample assuming equal variances > ok > then go to variable first range > select all value of first
range then go to second column of select variables. > here as also select all value of second column
> type 0 on hypothesis main differences > tick on labels> Alpha value 0.05 > click on output range
at empty box of excel > ok
To compare t stat value with t critical two value if t stat value is less than t critical value we cannot
reject null hypothesis. Therefore we cannot conclude that there is any differences between the
values of X.

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10.2 Data analysis using SPSS
Open SPSS file>Analyze>Descriptive>
 Frequencies
 Cross tabulation
 Explore
11. Type of Analysis: univariate, bivariate, and multivariate
Univariate analysis: Go to analyze>Mean, mode, median, frequency
Bivariate analysis: Go to analyze> Crosstab Eg. Chi square test
Multivariate analysis: Go to Analyze > Descriptive statistics/Correlation/Regression
12. Create, tables/Academic tables and figures in excel, SPSS
Creating figures in SPSS
Go to Analyze > Descriptive > Frequency > Select the variable > Click on Chart > Select the
required figures > Click on Ok
The out put is shown.
The colour of the figure can be changed by double clicking on charts > Select the required function
in Chart editir to modify charts.
Another way ,
Go to Graphs > Legacy dialog > select the required figure.
Creating figures in Excel
Enter the data > Select the data > Click on Insert > Select the option of figures > The diagram
appears > Diagram can be modified by clicking on it.

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Creating table in SPSS
13. Sample size calculation
13.1 Manual calculation of sample size
Using mean
i) n = Z2
/2
2
( Where Z /2 = 1.96, 2
= variance )for infinite population
d
ii) n = Z2
/2
2
In Case of finite population(when N is known)
d2
+ Z2
/2
2
N
Using Proportion
i) n = Z2
/2pq(Where Z /2 = 1.96) In case of infinite population
d2

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ii) For finite population of size ‘N’
n = no , no = Z2
/2pq
1+ no/N d2
(P = Probability of success, q = Probability of failure)
13.2 Using open epi
Open open epi through google search and choose sample size for proportion, mean difference etc.
Then enter new data, give population size, expected percentage, confidence level and design effect
Openepi>sample size>proportion>enter new data> calculate

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14. Organization of reviewed literature using referencing software: Zotero
Zotero: Zotero offers users a variety of ways to capture, import and archive item information and
fles. Zotero automatically captures bibliographic information from web. Zotero‟s book icon will
appear in Firefox‟s location bar (at the top of the browser window, where the current web address,
or URL, appears), like so: Simply click on the book icon and Zotero will save all of the citation
information about that book into your library.
Open Zotero > click on new collection > rename > save
If we are looking at a group of items (e.g., a list of search results from Google Scholar), a folder
will appear. Clicking on the folder will produce a list of items with check boxes next to them;
choose the ones you want to save and Zotero will do the rest.
Go to Google Scholar>Search “title”>click on save to Zotero (Zotero item selector)>choose &OK

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For citation when we would like to cite something from our collection click the first button, “Zotero
Insert Citation” ( ). If this is the first citation we have added to the document the Document
Preferences window will open. Chose the bibliographic format we would like to use from the list
and click OK.

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Eg. Maternal Anemia, as determined by low hemoglobin or hematocrit, is common among
women in their reproductive years in particular if the women are poor, pregnant, and members
of an ethnic minority. (Scholl, 2005)

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REFERENCES: Scholl, T.O., 2005. Iron status during pregnancy: setting the stage for
mother and infant. Am. J. Clin. Nutr. 81, 1218S–1222S.

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15. Basic format of academic research proposal.
A. Preliminaries
i. Front Cover Page
ii. Approval Page
iii. Table of Contents
iv. List of Abbreviations
B. Body
CHAPTER I: INTRODUCTION
1.1. Background
1.2. Justification of the study
1.3. Research Questions
1.4. Objectives of the study
1.5 Conceptual Framework
1.6 Operational definitions
1.7 Expected Outcome
CHAPTER II: LITERATURE REVIEW
2.1 Introduction
2.2 Literature search methods and strategies
2.3 Literature review
CHAPTER III: METHODOLOGY
3.1. Study Method
3.2. Study Type
3.3. Study Population
3.4. Study Area
3.5. Study Period
3.6. Sample Size
3.7. Sampling Technique
3.8. Selection Criteria: Inclusion and Exclusion Criteria

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3.9. Data Collection Technique
3.10. Data Collection Tools
3.11. Pretesting of the Tools
3.12. Data Management and Analysis
3.13. Quality Control and Quality Assurance
3.14. Ethical Consideration
C. Supplementary Section
References
Appendixes
a. Informed Consent
b. Data Collection Tools
c. Gantt Chart
d. Map of study area
e. Budget