pasw manual

8/4/2019 PASW Manual

1/24

1

Spelman College

Predictive Analytical Software (PASW) Users Manual

Sociology 334 Statistics

Monday, Wednesday, Friday 11:00 am

Dr. Bruce H. Wade

April 26, 2011

Christian J. Watkins


2/24

2

Table of Contents

Chapter 1: INTRODUCTION pg. 3

Chapter 2: FIRST STEPS pg. 4-8

Chapter 3: UNIVARIATE ANALYSIS & FREQUENCY DISTRIBUTIONS pg. 9-12

Chapter 4: GRAPHING AND CHARTING DATA pg. 13-16

Chapter 5: BIVARIATE ANALYSIS OF DATA pg. 17-18

Chapter 6: MULTIVARIATE ANALYSIS pg. 19-21

Chapter 7: CONCLUSION pg. 22

References Page pg. 23


3/24

3

Chapter 1

Introduction:

PASW stands for Predictive Analytics Software. This program is an all-inclusive

software used to analyze data. The program, SPSS, Statistical package for the Social

Sciences, is used to compute statistical analysis. PASW can receive data from just about

any type of file and use them to produce charts, designs of tendencies and distributions,

descriptive statistics, multifaceted statistical analysis, and tabulated charts.

PASW is a software that makes statistical analysis more suitable and accessible for

experienced users and beginners alike. The straightforward menus and dialog box

selections make it possible to present intricate analyses without typing in any command

syntax.

This software is great for behavioral research because it allows the researcher to

quickly and efficiently interpret social data, such as observations, surveys and interview

question responses. This program enables a greater emphasis on conceptual

understanding and interpretation. It also allows the user to study statistics in a way that

reflects statistical practice.

For my particular Senior thesis, on Fashion and its influence in the Spelman

community, this PASW software with help me to identify whether incoming freshmen

and transfer students are likely change their personal appearance to fit-in with the

Spelman look and the statistical significance that certain events may have on this

conformity. I will use PASW to analyze the results of my student survey that I will give

to randomly selected incoming new students. I will create a codebook of my student

questionnaire and create a dataset in PASW to test my results.


4/24

4

Chapter 2

The First Steps:

How to start a quantitative research project:

In a quantitative research project, the goal should be to determine the relationship

between an independent variable and a dependent variable in a population. Quantitative

research is about quantifying relationships between variables. These relationships are

expressed through statistics like differences between means, correlations, and relative

frequencies.

The first step in conducting a quantitative research project is to decide what type of

research you want to conduct. You must then turn your idea into a quantifiable research

question. You must state the problem of your quantitative research, or the reason why

you are conducting this research project. After doing so, you then state your hypothesis,

or educated guess as to what the outcome will be of this study. For example, I am doing a

research project on the relationship between driving drunk and regretful behavior. The

problem is that a person is killed every 30 minutes by a drunk driver. My hypothesis is

that there is a positive relationship between those who drink and drive and it resulting in

regretful behavior.

In step two, you must conduct a literature review on your research topic. The goal of

the literature review is to find similar research that has already been conducted on your

research topic that will help form your research report. It is best to have a number of

different sources such as journal articles, research papers, books, websites, and

systematic reviews. When writing your literature review, remember to put the various

literature in conversation with one another, not a paragraph description of each. Be sure

to note the key methods and findings involved.

The third part of conducting a research project is to design your study and develop

your methodology. In this step, you decide what type of study you want to conduct. Will

there be participant involvement? Is this a secondary analysis study? You must decide if

you want to use surveys, questionnaires, sampling methods, etc. Once you have made a

decision as to exactly how you want to test and obtain data, you create the design. For


5/24

5

example, I am trying to see whether natural hair or permed hair is most accepted or liked

by Spelman students. I then create a questionnaire using the beauty, self-esteem, and

body image scale developed by John West. Once you have created a questionnaire, you

then want to create a codebook to evaluate the data that you receive from the

questionnaire.

Finally, step four you can actually begin to conduct field research. In following my

previous example, I would randomly select a sample of the Spelman student body

population. I am going to give out 65 questionnaires, with a goal of at least getting 50 of

them back. Once I have all of the questionnaires completed and returned, I can begin the

fun part.

Some possible issues to watch for when collecting data are the availability and

reliability of research participants, recall bias, which is the participants inability to

remember information, and ethical issues. Some issues that may arise in data entry are

missing data, leading questions, and converting data from an excel spreadsheet to PASW

format.

When importing data from spreadsheet to PASW format, there is no one fullproof method but the subsequent steps are effective:

1. Close the Excel file2. Arrange the data in a rectangular grid3. Don't mix strings and numbers.4. Put descriptive names in your first row.

Excel should be closed before you try to import into SPSS.

(Figure 1-1)


6/24

6

Open SPSS and select FILE | OPEN from the menu.

(Figure 1-2)

(Figure 1-3)

Make sure that you havethe correct number of variables (columns) and cases (rows).(Figure 1-4)

This is what the SPSS data window should look like.

(Figure 1-5)


7/24

7

Data cleaning is used to ensure that a set of data is accurate and precise. During data

cleansing, records are verified for correctness and reliability. They are then either

corrected or deleted if necessary. This method can be used on single or between multiple

data sets. Data cleansing involves a person(s) or computer working through a set of

documents and verifying their precision. This is where mislabeled data is properly

labeled and filled, typos and misspellings are corrected, and missing data are completed.

Data cleaning is also used to get rid of out-of-date information or unrecoverable records

and free up space. Data cleansing is important to the proficiency of data dependent

programs. Its goal is to minimize errors, and make the data as useful and meaningful as

possible.

How to check for errors:

Frequency:(Analyze Descriptive Statistics Frequencies)

The simplest way to scan your data for weird stuff is to check the frequencies for

each variable. This lets you quickly see if anything unexpected entered the data

set.

Data Entry into SPSS begins by defining the names and properties of the variables.Then enter the specific values into each variable for independent sources of data. There is

one row for each independent data source and one column for each variable orcharacteristic that has been measured.

Entering the Data:

The first step for entering the actual data is to click on the Data View tab. To enter new data, click in an empty cell in the first empty row. The "Tab"

key will enter the value and jump to the next cell to the right. You may

also use the Up, Down, Left, and Right arrow keys to enter values andmove to another cell for data input.

To edit existing data points (i.e., the change a specific data value), click inthe cell, type in the new value, and press the Tab, Enter, Up, Down, Right,

or Left arrow keys.

Here is an illustrated view of what it should look like:


8/24

8

(Figure 1-6)

Columns to know:

Name: the name of each PASW variable in a file, each must be unique.Ex: Sum Dog_Rat

Type: the main two types of variables used are numerical and string. Numericalmay only have numbers assigned but string variables may contain letters or

numbers.

Width: the number of characters that PASW will allow to be input for thevariables.

Decimals: the number of decimal places PASW will display Label: used in text, to describe in better detail what a variable represents. Values: can be used for categorical data to know which numbers represent which

categories.Clicking here opens up the Value Labels dialogue box.

(Figure 1-7)

1. Click in the Value field to type a specific numeric value2. Click in the Label field to type the corresponding label3. Click on the Add button to add this pair of value and label to the list


9/24

9

Chapter 3

Uni-variate Analysis & Frequency Distribution

Descriptive statistics establish associations between variables. They provide simple

summaries about the sample and the measures. In descriptive statistics, you are

describing what the data shows. These statistics are used to present quantitative

descriptions in a manageable format. Descriptive statistics are used to help simplify huge

quantities of data. For example grade point average, GPA, describes the overall academic

performance of a student although he/she has taken a wide variety of courses. Descriptive

statistics do have their limitations but they provide an influential abstract that is capable

of facilitating comparisons athwart people and other units of analysis.

A Frequency Distribution is an organized tabulation of the number of individuals

located in each category on the scale of measurement (Gravetter, 2008). It presents a

picture of how the individual scores are distributed on the measurement scale.

A frequency distribution takes a disorganized set of scores and places them in order from

highest to lowest, grouping together all individuals who have the same score. For

example, if the highest score is X=8, the frequency distribution groups together all 8s,

then all the 7s, then all the 6s, and so on. So a frequency distribution allows the

researcher an at a glance view of the entire set of scores. It depicts whether the scores

are low or high, if they are concentrated in one particular area or spread out across the

scale, an usually offers an organized illustration of the data. It also allows you to see the

location of an individual score relative to all other totals in the group.

Frequency distribution can be illustrated in a number of ways but it must contain two

things:

1. A set of groups that frame the original measurement range.2. A documentation of the number of individuals in each group.


10/24

10

Examples:

(Figure 1-8) (Figure 1-9)

The images depicted above are two examples of frequency distribution. Figure 1-8 is a

frequency distribution table depicting the age of individuals and the percentage of people

in that category. The Figure 1-9 is afrequency distribution bar chart, it is the sameinformation as Figure 1-8, just illustrated differently.

Measures of Central Tendency

The central tendencyof a distribution, is the estimate of the middle of a distribution ofnumbers. The three major types of central tendency are:

1. Mode: the most frequently occurring number(s) in a set of values.a. Order the numbers in a set of values:

Ex: 2, 2, 3, 4, 8, 8, 8, 8, 10, 13, 14, 14, 14, 18, 19, 20, 21, 21b. Count each value and the most frequently occurring number is theMode.

Ex: 8 is the mode

2. Median: the score found exactly in the middle of the set of values.a. Order the numbers in a set of values:

Ex: 2, 2, 3, 4, 8, 8, 8, 8, 10, 13, 14, 14, 14, 18, 19, 20, 21, 21b. The number that is directly in the middle is the Median

Ex: 10 and 13, are both middle numbers so the average ofthese

two numbers equal the median.

i.

10 + 13 = 23ii. 23/2 = 11.5iii. 11.5 is the median

3. Mean: the average ofall numbers in a set of values. It is the most commoncentral tendency.

a. Add up all of the values


11/24

11

Ex: 2 + 2 + 3 + 4 + 8 + 8 + 8 + 8 + 10 + 13 + 14 + 14 + 14 + 18 +

19 + 20 + 21 +21= 207b. Divide the sum of the values by the number of values in the set of

data.

Ex: 207/18 = 11.5 is the mean

Dispersion is the spread of data around the central tendency. Also referred to as

statistical variability, it is a real number that begins as zero if all the data are identical,

and increases as the data differs. It cannot be less than zero. The most come measures ofdispersion are; standard deviation, range, and variance.

1. Range: the smallest interval that contains all the data. It is calculated bysubtracting the largest value in a dataset from the smallest value.

We will use the same numbers from the previous examples.a. Ex: 21-2 = 19 is the range2. Standard Deviation shows the amount of variation each number in a set of

data is from the mean (average). Standard deviation can be calculated by:

a. Subtract the mean from each number in the data setEx: 2 - 11.5= - 9.5

2 - 11.5= - 9.5

3 - 11.5= - 8.5

4 - 11.5= - 7.5

8 - 11.5= -3.5

8 - 11.5= -3.5

8 - 11.5= -3.5

8 - 11.5= -3.5

10 - 11.5= -1.5

13 - 11.5= 1.5

14 - 11.5= 2.5

14 - 11.5= 2.5

14 - 11.5= 2.5

18 - 11.5= 6.5

19 - 11.5= 7.5

20 - 11.5= 8.5

21 - 11.5= 9.5

21 - 11.5= 9.5

b. The resulting number,( +, -) are the standard deviations from themean. The next step is to square each standard deviation. This is

called the sum of squares.

Ex: - 9.52= 90.25

- 9.52= 90.25

- 8.52= 72.25

- 7.52= 56.25


12/24

12

-3.52= 12.25

-3.52= 12.25

-3.52= 12.25

-3.52= 12.25

-1.52= 2.25

1.5

2

= 2.252.52= 6.25

2.52= 6.25

2.52= 6.25

6.52= 42.25

7.52= 56.25

8.52= 72.25

9.52= 90.25

9.52= 90.25

732.50

c.

Now that we have found the sum of squares to be 732.50, wedivide this by the number of values in the dataset, which is 18

minus 1.

Ex: 732.50/17= 43.0882 (variance)

d. To find the standard deviation value, you take the square root ofthe variance value 43.0882.

Ex: SQRT(43.0882)= 6.56 is the standard deviation.

3. Variance: is the average of the squared differences from the mean. It hasbeen illustrated in the examples above.


13/24

13

Chapter 4

Graphing and Charting Data:

Graphs, charts, and tables are often used in statistics to visually convey data. These

illustrations are typically the first step in evaluating raw data to find outlying values,

trends, and data entry errors. An appropriate graph or chart can answer many questions in

a small amount of area and suggest further considerations.

The purpose of creating charts, tables, and graphs is to present data accurately and

clearly so that it is easily understandable. These illustrations are useful for highlighting

the shape of a distribution and conveying case clusters in a range of values. The massive

amount of computer graphic programs available has greatly made creating the

illustrations easier. The most commonly used programs to depict raw data information

are Microsoft Excel and PASW.

Types of Charts and Graphs:

1. Bar Graphs are usually rectangular bars with proportional lengths to the value itindicates. These bars are plotted vertically and horizontally. It is useful if you are

trying to record certain information whether it is continuous or not. Bar graphs are

effective ways to display the relative frequencies for two or more categories of a

variable when you want to emphasize comparisons. Bar charts are often mistaken

with histograms, which we will discuss later in this chapter. The main difference

between the two is that, in bar charts, each column represents a group defined by a

categorical variable; and with histograms, each column represents a group defined by

a quantitative variable. In bar graphs, the x-axis does not have a low end or high end;

because the labels on the x-axis are categorical, not quantitative.


14/24

14

Ex: (Figure 1-10)

2. Pie Charts are used to present the proportion of cases in categories of variables. Eachslice of the pie represents the proportion of cases in that category: the bigger the slice

of the pie is, the larger the relative size of that category is. A pie chart can be used to

examine variables at any level of measurements.

Ex:(Figure 1-11)


15/24

15

3. Line Charts use a dot to represent the midpoint of each interval. Then lines are usedto connect the dots. Line charts are a great way of illustrating trends across time. Line

graphs can use multiple lines and axes to convey a large amount of information in a

compact space.

Ex: (Figure 1-12)

4. Histograms show the distribution of data. It is an estimate of the probabilitydistribution of a continuous variable. It consists of tabular frequencies, shown in

rectangular form, over discrete intervals, with an area equal to the frequency density

of the interval. It shows the proportion of cases that fall into each category with the

total equaling one.

Histograms are used to

plot the density of data.

Ex: (Figure 1-12)


16/24

16

5. Scatter Plot is a diagram used in mathematics to display values for two variables in aset of data. Displayed as a collection of points, each dot represents a position on the

horizontal axis and the other variable determining the position on the vertical axis.

The independent variable is plotted on the x-axis and the dependent variable is plotted

on the y-axis. A scatter plot can be used to determine the degree of correlation

between two variables. It can suggest correlations between variables with a certain

confidence interval. These correlations can be positive, negative, or uncorrelated. A

positivecorrelation slopes from low left to high right. Its a negative correlation if it

slopes from high left to low right.

Ex: (Figure 1-13)


17/24

17

Chapter 5:

Bivariate Analysis of Data

The relationship between two variables is known as a Bivariate relationship.

Regression and correlation analysis is used to determine both the nature and the strength

of a relationship between two variables.

Correlations tell us how well the estimating equation actually describes the

relationship. It tells us the direction and strength of relationships between two variables.

The symbol for sample correlation coefficient is r, and the symbol for the population

correlation coefficient is . The likelihood ofr being close to (+, -) is very rare. If r is

approximately (+,-) .30, then it is considered to be important.

-1.0 0 +1.0

An ordinal scale describes some sort of ordering between variables but not related to

relative size or degree of difference between the items. It can include median and

percentile statistics. In this scale, the numbers assigned to objects or events represent rank

order. This scale also uses names to represent the order. Intelligence quotient is also

considered ordinal.

Ex 1: 1st, 2nd, 3rd, 4th, etc.

Ex 2: Bad, Medium, Good, Very Satisfied, Neutral, Unsatisfied

Ex 3: IQ 60, IQ 160, IQ 70

Ex 4: Restaurant ratings, Likert scales

Perfect Positive

Relationship

No Linear

RelationshipPerfect Negative

Relationship


18/24

18

An interval scale classifies variables into categories where one unit on the scale

represents the same magnitude on the characteristic being measured across the entire

range of the scale. For example, we measure anxiety on an interval scale, well 10 and 11

are essentially the same as 40 and 41. Interval scales do not have a true Zero points.

Therefore it is not accurate to make conclusions about how many times higher one score

is than another.

Ex: Fahrenheit scale of temperature

To compute correlations in PASW:

1. Click AnalyzeCorrelationsBivariate2. A dialogue box should appear. Pick your two variables from the list that you want to

analyze. Be sure to know what level of measurement your variables are so that you know

which correlation coefficientto use. If your hypothesis is asking about the direction of

the variables being tested, greater than or less than, be sure to check the one-tailed box.

For all others, check the two-tailed box under Test of Significance.

3. This is how your correlation should look once it is analyzed.

(Figure 1-14)


19/24

19

Chapter 6

Multivariate Analysis

Multivariate analysis involves the analysis of multiple variables at a time. This

technique is used to perform studies across multiple dimensions while taking into account

all variables being analyzed. It is concerned with data sets with more than multiple

variables being tested at one time.

Multivariate analysis uses multiple regressions to analyze multiple variables. There

is only one dependent variable but there can be multiple independent variables.

Regression analysis helps you to understand how the typical value of the dependent

variable changes when any one of the independent variables is varied. It estimates the

conditional expectation of the dependent variable given the average value of the

dependent variable when the independent variables are held fixed. In all circumstances,

the estimation target is a function of the independent variables.

It is widely used for forecasting and predicting. It is also used to determine which

independent variables are related to the dependent variable and explore the forms of these

relationships. In some circumstances, regression analysis can be used to infer causal

relationships between independent and dependent variables.

Two types of Regressions are

In linear regression, data are modeled using linear functions to estimate the unknown

parameters from the data. A linear function can determine if the mean ofy given X is an

affine function ofX. This form of regression focuses on the conditional probability

distribution ofy given X. It can be used for forecasting and prediction. It can be used to

determine which independent variable has the strongest relationship to the dependent

variable and which doesnt.


20/24

20

Ex: (Figure 1-15)

Statistics uses logistic regression for prediction of the probability of occurrence of an

event by inputting data to a logistic curve. It is a generalized linear model used for

binomial regression. For example, a persons age, sex, and BMI can be used to predict

their likelihood of having a heart attack within a specified time period. It is widely used

in the medical field as well as the marketing field, to determine buying propensity.

This is the logistic function:


21/24

21

This is an example of a logistic regression graph:

(Figure 1-16)

This graph shows that the variable z represents the exposure to some set of independent

variables, while f(z) represents the probability of a particular outcome, given that set of

explanatory variables.


22/24

22

Chapter 7

Conclusion

Social Statistics are important to understanding human behavior in our social

environment. As shown throughout this manual, there are many ways of testing and

understanding the relationships between different factors associated with human

behavior. Social scientist use social statistics for things like, evaluating the quality of

services available to certain groups of people or organizations. They are used to help

analyze different behaviors of groups of people in their natural environment as well as

different environments or special circumstances. Social statistics are also helpful in

establishing the wants and needs of people through statistical sampling.

Predictive Analytics software is just one of the software programs, along with

Microsoft Excel and SAS, that have helped to make researchers lives so much easier

through their statistical analysis programs. I hope that this manual has been informative,

and that it helps you to better understand some of the concepts involved in social science

quantitative analysis, as well as how to work some of the programs that SPSS provides.


23/24

23

References

Healey, Joseph F. 2005. Statistics: A tool for Social Research. Belmont,CA:Thomson Wadsworth

Weinstein, Jay Alan. 2010.Applying Social Statistics: An introduction toquantitative reasoning. Lanham, MD: Rowman & Littlefield.

IBM SPSS Statistics Student Version 18.0 for Windows and Mac OS X. 2011.London: Pearson. Computer software.

SPSS Statistics 17.0 Brief Guide. 2008. Chicago, IL. (http://www.spss.com) Simon, Steve. 2008. Importing Spreadsheet Data into SPSS. Childs

Mercy Hospital and Clinics. Retrieved April 28, 2011.

(http://www.childrensmercy.org/stats/data/excel.asp ).

o Figure 1-1 thru 1-6:http://www.childrensmercy.org/stats/data/excel.asp

SPSS Basic Skills Tutorial: Data Entry. SPSS Basic Skills Tutorials.Retrieved April 29, 2011.

(http://my.ilstu.edu/~mshesso/SPSS/data_entry.html ).

o Figure 1-7: (http://my.ilstu.edu/~mshesso/SPSS/data_entry.html ) Stockburger, David W. Frequency DistributionIntroductory Statistics:

Concepts, Models, and Applications. Retrieved April 25, 2011.

(http://www.psychstat.missouristate.edu/introbook/sbk07.htm ).

o Figure 1-8, 1-9, and 1-13:http://www.psychstat.missouristate.edu/introbook/sbk07.htm
http://www.spss.com/http://www.spss.com/http://www.spss.com/http://www.childrensmercy.org/stats/data/excel.asphttp://www.childrensmercy.org/stats/data/excel.asphttp://www.childrensmercy.org/stats/data/excel.asphttp://www.childrensmercy.org/stats/data/excel.asphttp://www.childrensmercy.org/stats/data/excel.asphttp://my.ilstu.edu/~mshesso/SPSS/data_entry.htmlhttp://my.ilstu.edu/~mshesso/SPSS/data_entry.htmlhttp://my.ilstu.edu/~mshesso/SPSS/data_entry.htmlhttp://my.ilstu.edu/~mshesso/SPSS/data_entry.htmlhttp://my.ilstu.edu/~mshesso/SPSS/data_entry.htmlhttp://my.ilstu.edu/~mshesso/SPSS/data_entry.htmlhttp://www.psychstat.missouristate.edu/introbook/sbk07.htmhttp://www.psychstat.missouristate.edu/introbook/sbk07.htmhttp://www.psychstat.missouristate.edu/introbook/sbk07.htmhttp://www.psychstat.missouristate.edu/introbook/sbk07.htmhttp://my.ilstu.edu/~mshesso/SPSS/data_entry.htmlhttp://my.ilstu.edu/~mshesso/SPSS/data_entry.htmlhttp://www.childrensmercy.org/stats/data/excel.asphttp://www.childrensmercy.org/stats/data/excel.asphttp://www.spss.com/


24/24

Create a Graph. Kids Zone. Retrieved April 29, 2011(http://nces.ed.gov/nceskids/createagraph/default.aspx?ID=6cddd6c73b91401

38df31e2fbe1eb65c).

o Figure 1-10:http://nces.ed.gov/nceskids/createagraph/index.asp?ID=9664f172

a1db4726aa74c9899e5fb106o Figure 1-12:

http://nces.ed.gov/nceskids/createagraph/index.asp?ID=9bc5334c

02d342568aa6a97d2c330a11
http://nces.ed.gov/nceskids/createagraph/default.aspx?ID=6cddd6c73b9140138df31e2fbe1eb65chttp://nces.ed.gov/nceskids/createagraph/default.aspx?ID=6cddd6c73b9140138df31e2fbe1eb65chttp://nces.ed.gov/nceskids/createagraph/default.aspx?ID=6cddd6c73b9140138df31e2fbe1eb65chttp://nces.ed.gov/nceskids/createagraph/default.aspx?ID=6cddd6c73b9140138df31e2fbe1eb65chttp://nces.ed.gov/nceskids/createagraph/index.asp?ID=9664f172a1db4726aa74c9899e5fb106http://nces.ed.gov/nceskids/createagraph/index.asp?ID=9664f172a1db4726aa74c9899e5fb106http://nces.ed.gov/nceskids/createagraph/index.asp?ID=9664f172a1db4726aa74c9899e5fb106http://nces.ed.gov/nceskids/createagraph/index.asp?ID=9bc5334c02d342568aa6a97d2c330a11http://nces.ed.gov/nceskids/createagraph/index.asp?ID=9bc5334c02d342568aa6a97d2c330a11http://nces.ed.gov/nceskids/createagraph/index.asp?ID=9bc5334c02d342568aa6a97d2c330a11http://nces.ed.gov/nceskids/createagraph/index.asp?ID=9bc5334c02d342568aa6a97d2c330a11http://nces.ed.gov/nceskids/createagraph/index.asp?ID=9bc5334c02d342568aa6a97d2c330a11http://nces.ed.gov/nceskids/createagraph/index.asp?ID=9664f172a1db4726aa74c9899e5fb106http://nces.ed.gov/nceskids/createagraph/index.asp?ID=9664f172a1db4726aa74c9899e5fb106http://nces.ed.gov/nceskids/createagraph/default.aspx?ID=6cddd6c73b9140138df31e2fbe1eb65chttp://nces.ed.gov/nceskids/createagraph/default.aspx?ID=6cddd6c73b9140138df31e2fbe1eb65c

pasw manual

Documents