Data Management and Preparation ppt

Data management and preparation is all about what?

what you do with your data after you collect it, but before you analyze it (STATS)

Data Management:

Administrative process that includes:

◦ Acquiring, validating, storing, protecting, and processing data to ensure the accessibility, reliability, and timeliness of the data for its users

All research projects need a data management plan that addresses (4):

◦ How the data will be stored

◦ Where the data will be stored

◦ Under what conditions the data can be accessed by others

◦ How potentially identifying information in the dataset is handled

Before starting data collection, you should ALWAYS think of what happens with…?

the specimens/data upon conclusion of the study

Many scientific journals are moving towards ____ _____ of

the data used in their publications

open access

Open access to datasets is important because it allows researchers to (4):

◦ Verify research conclusions

◦ Be more transparent of the research process

◦ Share data

◦ Build larger data sets

A case against Open Access:

◦ Use of Open AI sources

◦ Ethics of open access for some data

◦ Cost

Data Preparation:

Process of extracting data to remove

unnecessary information or cleaning up a

dataset to make it useable

Data preparation includes a variety of activities, such as (4)

gathering, combining, structuring, and organizing data

No matter how much preparation you did before data collection, your data will still need ________ before you can analyze it

organizing

6 steps in Data Preparation

1. Data collection

2. Data discovery and profiling

3. Data cleansing

4. Data structuring

5. Data transformation and enrichment

6. Data validation and publishing

Entering data by hand is common, but can result in ________ _____.

transcription errors

Before you can begin data analysis, you will need to make sure your data are formatted ______ and _________.

correctly; consistently

Data cleansing is also used for:

finding/removing outliers from data to make your statistics more accurate

These are examples of what?

• Check for errors

  • Look for data entry mistakes/typos

• Possibly removing outliers

• coding a value for missing data

• changing variable format

• remove unneeded variables

things done during data preparation

Data transformation is used when…

you need to convert data into new values or new variables altogether

Another part of data preparation is dealing with missing data through what means (3)?

• Listwise deletion?

• Mean replacement?

• Multiple imputation?

The more thorough you are during data preparation, the easier ____ ______ will be.

data analysis