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Slide1

Stata

and

logit

recapSlide2

Topics

Introduction

to

Stata

Files / directories

Stata

syntax

Useful

commands

/

functions

Logistic

regression

analysis

with

Stata

Estimation

Goodness

Of Fit

Coefficients

Checking

assumptions

Slide3

Overview of

Stata

commands

Note: we did this interactively for the larger part …Slide4

Stata file types

.

ado

programs

that

add

commands

to

Stata

.do

Batch files

that

execute

a set of

Stata

commands

.

dta

Data file in

Stata’s

format

.

log

Output

saved

as

plain

text

by

the

log

using

command

(

you

could

add

.

txt

as well)Slide5

The working directory

The

working

directory is the default directory

for

any

file operations

such

as

using

&

saving

data, or

logging

output

cd

“d:\

my

work

\” Slide6

Saving output to log files

Syntax

for

the log

command

log

using

[

filename

]

,

replace

text

To

close a log file

log

closeSlide7

Using and saving datasets

Load a

Stata

dataset

use

d:\

myproject

\

data.dta

,

clear

Save

save d:\

myproject

\data,

replace

Using

change directory

cd d:\

myproject

use

data,

clear

save data,

replace

Slide8

Entering data

Data in

other

formats

You

can

use

SPSS

to

convert

data

that

can

be

read

with

Stata

.

Unfortunately

,

not

the

other

way

around

(

anymore

)

You

can

use

the

infile

and

insheet

commands

to

import data in ASCII

format

Direct import

and

export of Excel files in

Stata

is

possible

too

Entering

data

by

hand (

don’t

do

this

…)

Type

edit

or

just

click on the data-editor buttonSlide9

Do-files

You

can

create

a

text

file

that

contains

a series of

commands

. It is the equivalent of SPSS syntax (but way

easier

to

memorize

)

Use

the do

-file editor

to

work

with

do-files Slide10

Adding

comments

in do-files

// or *

denote

comments

stata

should

ignore

Stata

ignores

whatever

follows

after

///

and

treats

the next line as a

continuation

Example

IISlide11

A

recommended

template

for

do-files

capture

log

close

//if a log file is open, close it, otherwise disregard

set

more

off

//

dont'pause

when output scrolls off the page

cd

d:\

myproject

//change directory to your working directory

log

using

myfile

, replace

text

//log results to file

myfile.log

… here you put the rest of your

Stata

commands …

log close

//close the log fileSlide12

Serious data analysis

Ensure replicability use do+log files

Document your do-files

What is obvious today, is baffling in six months

Keep a research log

Diary that includes a description of every program you run

Develop a system for naming filesSlide13

Serious data analysis

New variables

should

be

given

new

names

Use

variable

labels

and

notes

(I

don’t

like

value

labels

though

)

Double check

every

new

variable

ARCHIVESlide14

Stata

syntax examplesSlide15

Stata

syntax

example

r

egress

y x1 x2

if

x3<20, cluster(x4)

regress

=

command

What

action

do

you

want to

performed

y x1 x2 =

Names

of variables, files

or

other

objects

On

what

things

is the

command

performed

if

x3 <20 =

Qualifier

on

observations

On

which

observations

should

the

command

be

performed

, cluster(x4) = Options

appear

behind

the

comma

What

special

things

should

be

done

in

executing

the

commandSlide16

More

e

xamples

tabulate

smoking race if

agemother

>30

, row

More

elaborate

if

-statements:

sum

agemother

if

smoking==1

&

weightmother

<100

Slide17

Elements used for logical statements

Operator

Definition

Example

==

is

equal

in

value

to

if

male == 1

!=

not

equal

in

value

to

if

male !=1

>

greater

than

if

age

> 20

>=

greater

than

or

equal

to

if

age

>=21

<

less

than

if

age

< 66

<=

less

than

or

equal

to

if

age

<=65

&

and

if

age

==21 & male

==1

|

or

if

age

<=21 |

age

>=65Slide18

Missing values

Automatically

excluded

when

Stata

fits

models

(

same

as in SPSS);

they

are

stored

as the

largest

positive

values

Beware!!

The

expression

age

>65

can

thus

also

include

missing

values

(these are

also

larger

than

65)

To

be

sure

type:

age

>65

&

age

!=.”Slide19

Selecting observations

drop

[

variable

list

]

keep

[

variable

list

]

drop

if

age

<65

Note

:

they

are

then

gone

forever

.

This

is

not

SPSS’s

[filter]

command

.Slide20

Creating new variables

Generating new variables

generate

age2 =

age

*

age

(

for

more

complicated

functions

,

there

also

exists

a

command

egen

”, as we

will

see

later)Slide21

Useful functions

Function

Definition

Example

+

addition

gen y = a+b

-

subtraction

gen y =

a-b

/

Division

gen

density

=

population

/

area

*

Multiplication

gen y = a*b

^

Take

to a power

gen y = a^3

ln

Natural

log

gen

lnwage

=

ln

(

wage

)

exp

exponential

gen

y =

exp

(b)

sqrt

Square root

gen

agesqrt

=

sqrt

(

age

)Slide22

Replace command

replace

has the

same

syntax as

generate

but is

used

to

change

values

of a

variable

that

already

exists

gen age_dum5

= .

replace

age_dum5

= 0

if

age

< 5

replace

age_dum5 =

1

if

age

>=5Slide23

Recode

Change

values

of

existing

variables

Change 1

to

2

and

3

to

4 in

origvar

,

and

call the new

variable

myvar1:

recode

origvar

(1=2)(3=4), gen(myvar1)

Change

1’s

to

missings

in

origvar

,

and

call the new

variable

myvar2:

recode

origvar

(1=.)

, gen

(myvar2)

By: olivia-moreira
Views: 20
Type: Public

Stata and logit recap - Description


Topics Introduction to Stata Files directories Stata syntax Useful commands functions Logistic regression analysis with Stata Estimation Goodness Of Fit Coefficients ID: 652464 Download Presentation

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