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Research Project Stages, Data Frequency, Time Period, and Data Manipulation in SAS, Quizzes of Introduction to Database Management Systems

University of Adelaide (UA)Introduction to Database Management Systems

The stages of a research project, including the identification of the problem and hypothesis development, data collection and analysis, interpretation of results, and policy recommendation. Additionally, it covers data frequency and time period definitions, as well as sas codes for removing duplicate variables, sorting data, and removing duplicate observations. Outliers and variable coding are also discussed.

Typology: Quizzes

2017/2018

Uploaded on 09/03/2018

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TERM 1
Stages of a research project
DEFINITION 1
1. Identification of the problem = research question 2.
Hypothesis development (not always applicable) 3. Data
collection 4. Data analysis 5. Interpretation of results 6.
Policy recommendation
TERM 2
Data frequency
DEFINITION 2
Annually
Monthly
Daily
Intra-day (minute)
TERM 3
Time period
DEFINITION 3
1 month
1 quarter
1 year
10 years etc
TERM 4
SAS code to remove duplicate variables
DEFINITION 4
Option NODUPKEY deletes those observations with duplicate
BY variable (in our case: Company) values.PROC SORT
data=work.sample nodupkey;BY Company;RUN;
TERM 5
SAS code to sort data
DEFINITION 5
PROC SORT data=work.sample;BY Company;RUN;
pf2

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TERM 1

Stages of a research project

DEFINITION 1

  1. Identification of the problem = research question 2. Hypothesis development (not always applicable) 3. Data collection 4. Data analysis 5. Interpretation of results 6. Policy recommendation TERM 2

Data frequency

DEFINITION 2 Annually Monthly Daily Intra-day (minute) TERM 3

Time period

DEFINITION 3 1 month 1 quarter 1 year 10 years etc TERM 4

SAS code to remove duplicate variables

DEFINITION 4 Option NODUPKEY deletes those observations with duplicate BY variable (in our case: Company) values.PROC SORT data=work.sample nodupkey;BY Company;RUN; TERM 5

SAS code to sort data

DEFINITION 5 PROC SORT data=work.sample;BY Company;RUN;

TERM 6

SAS code to remove duplicate observations

DEFINITION 6 PROC SORT data=work.sample nodup; BY Company; RUN; Option NODUP deletes duplicated observations. TERM 7

Outliers

DEFINITION 7

  • 3 or 3. TERM 8

Variable

coding

DEFINITION 8 For example, if in quantitive analysis, one needs to analyze the impact of gender, one should code the variable gender: 1 if gender is female and 0 if gender is male.