Data Management and Cleaning: Essential Techniques for Social Work Research

Question 1

Which of the following is NOT considered a fundamental data cleaning technique?

Accepted Answer

Data backup

Answer

Data imputation

Answer

Data merging

Answer

Data transformation

Question 2

Which of the following data types is most appropriate for storing dates and times?

Accepted Answer

Datetime

Answer

String

Answer

Integer

Answer

Float

Question 3

What is the primary purpose of using primary keys in data management?

Accepted Answer

To uniquely identify records within a dataset

Answer

To establish relationships between different tables

Answer

To ensure data consistency

Answer

To optimize data storage space

Question 4

Which of the following is NOT a benefit of data cleaning in social work research?

Accepted Answer

Enhanced generalization of research findings

Answer

Improved data quality and accuracy

Answer

Reduced bias and errors

Answer

Increased efficiency in data analysis

Question 5

What is the process of identifying and rectifying errors, inconsistencies, and missing values in a dataset called?

Accepted Answer

Data validation

Answer

Data imputation

Answer

Data transformation

Answer

Data standardization

Question 6

Which of the following methods is used to replace missing values with estimated values based on the relationships between variables?

Accepted Answer

Regression imputation

Answer

Listwise deletion

Answer

Pairwise deletion

Answer

Mean imputation

Question 7

Which of the following is NOT a technique used for outlier detection?

Accepted Answer

Classification algorithms

Answer

Z-score method

Answer

Interquartile range method

Answer

Box plot analysis

Question 8

What is the process of converting raw data into a format suitable for analysis called?

Accepted Answer

Data preparation

Answer

Data cleaning

Answer

Data transformation

Answer

Data coding

Question 9

In social work research, which ethical consideration is paramount when managing and cleaning data?

Accepted Answer

Protecting participant privacy and confidentiality

Answer

Ensuring data accuracy and completeness

Answer

Using appropriate data analysis methods

Answer

Storing data securely to prevent unauthorized access

Question 10

When preparing mental health data for a social work research project, which tool is specifically designed to find and eliminate duplicate entries?

Accepted Answer

Deduplication software

Answer

Regular expressions (Regex)

Answer

Machine learning algorithms

Answer

Data profiling tool

Question 11

What is the primary purpose of metadata in data management and cleaning?

Accepted Answer

Provide structured descriptions of the data's content and meaning

Answer

Store the actual data values in an organized manner

Answer

Conduct statistical analysis and draw inferences from the data

Question 12

Which data cleaning technique is specifically employed to address missing data?

Accepted Answer

Data imputation

Answer

Data transformation

Answer

Data merging

Answer

Data standardization

Question 13

What is the primary goal of data standardization?

Accepted Answer

To ensure data compatibility and enable comparisons

Answer

To detect outliers and extreme values

Answer

To prepare data for statistical analysis

Answer

To protect data privacy

Question 14

When is data transformation most appropriate?

Accepted Answer

When data is not in a format suitable for analysis

Answer

When data contains errors or inconsistencies

Answer

When missing values need to be imputed

Answer

When data needs to be normalized

Question 15

Which of the following is NOT an advantage of utilizing data management software?

Accepted Answer

Elimination of the need for manual data manipulation

Answer

Improved data accuracy and consistency

Answer

Enhanced data visualization and exploration

Answer

Automatization of data cleaning and validation

Question 16

Which practice is considered best when cleaning data?

Accepted Answer

Documenting all data cleaning steps and decisions clearly

Answer

Solely relying on automated data cleaning tools without manual verification

Answer

Removing all data points with missing or incomplete information

Answer

Modifying data to match expected patterns

Question 17

Which data format is most suited for handling large and complex datasets?

Accepted Answer

JSON

Answer

CSV

Answer

XML

Answer

Excel

Question 18

Which of the following is a recommended practice to enhance data quality during the data cleaning process?

Accepted Answer

Remove duplicate data entries

Answer

Create new variables from existing ones without thoroughly documenting the transformations

Answer

Correct spelling and grammar errors without consulting the original source

Answer

Add arbitrary values to incomplete data records

Question 19

What is the primary purpose of data imputation?

Accepted Answer

To replace missing values with plausible estimates using statistical techniques

Answer

To create additional variables based on existing ones

Answer

To transform variables to have different scales

Answer

To identify and remove outliers

Question 20

Which data type is most appropriate for representing a person's age?

Accepted Answer

Numeric

Answer

Date

Answer

Boolean

Answer

Categorical

Question 21

What is the fundamental distinction between a variable and a constant?

Accepted Answer

A variable can assume different values, whereas a constant remains fixed

Answer

A variable is quantitative, while a constant is qualitative

Answer

A variable is measured on a nominal scale, while a constant is measured on an ordinal scale

Question 22

Which of the following is a suitable tool for visualizing data distributions?

Accepted Answer

Histogram

Answer

Factor analysis

Answer

ANOVA

Answer

Regression analysis

Question 23

What is the main objective of data normalization?

Accepted Answer

To transform variables to a common scale, enabling meaningful comparisons

Answer

To handle missing values

Answer

To eliminate outliers

Answer

To create additional variables

Question 24

Which of the following represents an example of a categorical variable?

Accepted Answer

Gender

Answer

Income

Answer

Age

Question 25

What is the key difference between a cross-sectional study and a longitudinal study?

Accepted Answer

A cross-sectional study captures data from a sample at a single point in time, while a longitudinal study collects data from the same sample repeatedly over an extended period

Answer

A cross-sectional study is quantitative, whereas a longitudinal study is qualitative

Question 26

What is the primary purpose of metadata in data management?

Accepted Answer

To provide comprehensive information about the data, including its source, structure, and intended meaning

Answer

To protect the data from unauthorized access

Answer

To store the actual data values