MCQs

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Overall MCQs for Reference

Which term best describes the process of changing values within a dataset to different categories?
a. Imputation
b. Recoding
c. Sampling
d. Scaling
Answer: b. Recodingsagepub+1
Recoding is commonly used to:
a. Create more complex variables
b. Simplify analysis by grouping categories
c. Increase row count
d. Remove outliers
Answer: b. Simplify analysis by grouping categoriessdmx+1
Which of the following is NOT a reason to recode data?
a. Categorizing continuous variables
b. Fixing errors in coded data
c. Improving visualization techniques
d. Creating if/else scenarios
Answer: c. Improving visualization techniquesstudy
What technique is often implemented for converting categorical variables into numerical ones?
a. Rescaling
b. Feature selection
c. One-hot encoding
d. Outlier analysis
Answer: c. One-hot encodinggyansetu+1
When recoding in SPSS, what command is typically used?
a. Merge
b. Collapse
c. Recode
d. Join
Answer: c. Recodescribd+1
In R programming, recoding variables usually involves:
a. Creating new columns
b. Applying logical operators
c. Sorting data
d. Normalizing values
Answer: b. Applying logical operatorssfu+1
Which function in Python is often used for mapping old values to new categories during recoding?
a. replace()
b. filter()
c. groupby()
d. split()
Answer: a. replace()pythonfordatascienceorg.wordpress
Why is recoding critical before statistical modeling?
a. It reduces bias
b. Ensures variable compatibility with models
c. Increases missing values
d. Discards useful information
Answer: b. Ensures variable compatibility with modelslibguides.library.kent+1
What would likely happen if a variable with nine categories were collapsed into three via recoding?
a. Increased complexity of analysis
b. Simplified comparative analysis
c. Increased data sparsity
d. Lost original categories permanently
Answer: b. Simplified comparative analysissagepub
Which approach is used when collapsing adjacent categories during recoding?
a. Aggregation
b. Grouping
c. Sampling
d. Scaling
Answer: b. Groupingsdmx+1
Recoding data ensures that:
a. Outliers are detected
b. Variables fit analytical models
c. Data types remain unchanged
d. All missing values are imputed
Answer: b. Variables fit analytical modelsstudy+1
Which method allows converting multiple relevant string values into a single category for analysis?
a. String concatenation
b. Pattern mapping
c. Text joining
d. Recoding via lookup table
Answer: d. Recoding via lookup tablecran.r-project
To reclassify survey responses “yes”, “no”, “maybe” into binary “1” (yes) and “0” (no/maybe), what process is used?
a. Binning
b. Recoding
c. Normalization
d. Feature extraction
Answer: b. Recodingsagepub+1
What outcome is expected after recoding a continuous variable into ordinal groups?
a. Higher granularity
b. Reduced noise
c. Simplified interpretation
d. Increased variance
Answer: c. Simplified interpretationstudy+1
Collapsing categories in SPSS is also referred to as:
a. Labeling
b. Grouping
c. Collapsing
d. Mapping
Answer: c. Collapsinglibguides.library.kent+1
Recoding categorical variables is required for:
a. Linear regression
b. Decision trees
c. Neural networks
d. All of the above
Answer: d. All of the aboveplacementpreparation+1
When mapping values manually during recoding, a common mistake is:
a. Skipping labels
b. Spelling mistakes
c. Double counting variables
d. Ignoring missing data
Answer: b. Spelling mistakescran.r-project
A lookup table in recoding serves the purpose of:
a. Standardizing variable types
b. Defining old to new value pairs
c. Grouping observations
d. Filtering variables
Answer: b. Defining old to new value pairscran.r-project
Which R function can be used to recode multiple string labels?
a. recode2NA()
b. applyLookup_expandVar()
c. as.factor()
d. toString()
Answer: b. applyLookup_expandVar()cran.r-project
Recoding can also help correct:
a. Data drift
b. Coding errors
c. Model overfitting
d. Missing value imputation
Answer: b. Coding errorssagepub+1
In pandas, which method would efficiently recode values across a column?
a. map()
b. read_csv()
c. sample()
d. groupby()
Answer: a. map()pythonfordatascienceorg.wordpress
Recoding for binary classification generally results in:
a. Two outcome groups
b. Increased outliers
c. More continuous variables
d. Data loss
Answer: a. Two outcome groupssdmx+1
In Qualtrics survey data, recode values are used to:
a. Organize survey logic
b. Merge datasets
c. Filter missing responses
d. None of the above
Answer: a. Organize survey logicqualtrics
What is the best technique to convert numeric scales (e.g., 1-10) into ‘Low’, ‘Medium’, ‘High’ groupings?
a. Feature scaling
b. Recoding
c. Normalization
d. Sampling
Answer: b. Recodingstudy+1
Which process is NOT involved in recoding data for analysis?
a. Value mapping
b. Creating identifiers
c. Frequency tabulation
d. Category collapsing
Answer: c. Frequency tabulationsagepub
When multiple columns represent a single dummy variable, recoding helps by:
a. Collapsing variables into a single feature
b. Creating many new columns
c. Increasing redundancies
d. Reducing sample size only
Answer: a. Collapsing variables into a single featurecran.r-project
Which scenario MAY require the use of substring matches to recode values?
a. Standardizing formats
b. Mapping survey responses
c. Filtering categories
d. Removing outliers
Answer: b. Mapping survey responsessdmx
Which data science stage typically involves recoding?
a. Data collection
b. Data preprocessing
c. Model selection
d. Model deployment
Answer: b. Data preprocessingexamveda+1
Recoding may help transform “Yes”, “No”, and “Maybe” responses into:
a. Numerical, ordinal, or binary values
b. Continuous variables
c. Outliers
d. Missing values
Answer: a. Numerical, ordinal, or binary valuesstudy+1
If recoding is performed wrongly, it can cause:
a. Data leakage
b. Increased accuracy
c. Missing values
d. Both a and c
Answer: d. Both a and cplacementpreparation
Converting “M” and “Male” both to “Male” during data cleaning is what kind of process?
a. Mapping
b. Encoding
c. Recoding
d. Sampling
Answer: c. Recodinganyamemensah+1
In R, logical operators are used in recoding to:
a. Specify conditions for transformation
b. Select random samples
c. Impute missing values
d. Normalize features
Answer: a. Specify conditions for transformationstudy
Recoding is NOT necessary when:
a. Variable values are already in required formats
b. Variables have inconsistent categories
c. Preparing data for machine learning models
d. Dealing with survey responses
Answer: a. Variable values are already in required formatssagepub+1
What is a benefit of recoding variables before visualization?
a. Clearer category groupings
b. Increased dataset size
c. Reduced feature set
d. Higher plotting speed
Answer: a. Clearer category groupingsscribd+1
What’s the first step before recoding any variable?
a. Run frequency distribution
b. Fit machine learning model
c. Drop all missing data
d. Visualize categories
Answer: a. Run frequency distributionsagepub
Recoding usually uses which operator for defining value conditions?
a. If/else
b. Multiply
c. Sum
d. Divide
Answer: a. If/elseanyamemensah+1
In feature engineering, recoding categorical variables can help:
a. Model interpretability
b. Feature removal
c. Outlier detection
d. Sampling bias
Answer: a. Model interpretabilitygyansetu+1
Which recoding approach combines similar categories for analysis?
a. Label encoding
b. Category collapsing
c. Feature extraction
d. String manipulation
Answer: b. Category collapsinglibguides.library.kent+1
Binning continuous data into discrete categories is a form of:
a. Imputation
b. Normalization
c. Recoding
d. Randomization
Answer: c. Recodingstudy+1
What tool is recommended for recoding variables in SPSS?
a. Data Transform
b. Recode
c. Merge Variables
d. Groupby
Answer: b. Recodescribd+1
Recoding is NOT used for:
a. Changing category labels
b. Aggregating rows
c. Merging identical values
d. Correcting inconsistent responses
Answer: b. Aggregating rowssagepub
What is the result of using the pandas map() method?
a. Value recoding in a Series
b. Feature normalization
c. Data transformation only
d. Outlier elimination
Answer: a. Value recoding in a Seriespythonfordatascienceorg.wordpress
After recoding, one should always:
a. Validate the output
b. Impute missing values
c. Cluster the results
d. Increase sample size
Answer: a. Validate the outputanyamemensah+1
In Qualtrics, using recode values enhances:
a. Survey logic control
b. Model performance
c. Survey aesthetics
d. Sample randomness
Answer: a. Survey logic controlqualtrics
Recoding helps deal with:
a. Data inconsistencies
b. Survey logic
c. Categorical variable transformation
d. All of the above
Answer: d. All of the abovequaltrics+1
To combine adjacent categories in SPSS, which step is necessary?
a. Identify the variable
b. Run frequency distribution
c. Recoding using command
d. All of the above
Answer: d. All of the abovelibguides.library.kent+1
Recoding can be reversed in data mapping if:
a. Regular expressions and simple matches are used
b. Only uni-directional mappings are implemented
c. The source structure is discarded
d. Aggregations are present
Answer: a. Regular expressions and simple matches are usedsdmx
Which is NOT a primary goal of recoding data?
a. Simplify categories
b. Increase variable count
c. Enhance analysis
d. Correct data coding errors
Answer: b. Increase variable countsdmx+1
Changing the values of variables into new categories is the definition of:
a. Data randomization
b. Recoding variables
c. Data sampling
d. Data merging
Answer: b. Recoding variableswayground
Which statement about recoding is TRUE?
a. It only applies to categorical data
b. It may apply logical conditions for value changes
c. It increases original dataset complexity
d. It is not useful for analysis
Answer: b. It may apply logical conditions for value changessdmx+1

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