Discuss IIBA CBDA Exam Topic 2 Question 51

While creating a dataset for analysis, the analyst reviews the data collected and finds a large percentage of records are missing values. Which activity would the analyst perform in order to use this dataset?

AClustering

BScale validation

CWeighting

DFactor analysis

Suggested Answer: C

Weighting is a technique that assigns different values or weights to different records or variables in a dataset, based on their importance or relevance. Weighting can be used to handle missing values by giving them a lower weight or imputing them with a weighted average of other values. Weighting can also help to adjust for sampling bias or non-response bias in the data collection process. Reference:

* Understanding the Guide to Business Data Analytics, page 16

* Business Analysis Certification in Data Analytics, CBDA | IIBA, CBDA Competencies, Domain 3: Analyze Data

* CERTIFICATION IN BUSINESS DATA ANALYTICS HANDBOOK - IIBA, page 8, CBDA Exam Sample Questions and Self-Assessment, Question 4

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16 comments · newest first

Alpha 2 months ago

Right! It’s crucial to handle missing data before analysis.

Carla 2 months ago

Exactly! Weighting adjusts for those gaps effectively.

Nathalie 2 months ago

True, but without addressing missing values, validation won't help much.

Art 2 months ago

But what about B) Scale validation? It checks data quality too.

Cathern 2 months ago

Weighting seems best. It can make the dataset more reliable.

Carla 3 months ago

I agree, but A) Clustering could also work if they group similar data.

Alpha 3 months ago

I think the analyst should go for C) Weighting. It helps adjust for missing values.

Christiane 3 months ago

Factor analysis might still work if the missing data is random.

Valentin 3 months ago

Wait, can you really use a dataset with that many missing values?

Maryann 3 months ago

I think weighting could help adjust for those gaps.

Vi 3 months ago

Definitely not clustering with so many missing values!

Evangelina 4 months ago

Missing values need to be addressed before analysis.

Lavonda 4 months ago

Factor analysis sounds familiar, but I think it’s more about reducing dimensions rather than fixing missing data.

Theron 4 months ago

I feel like weighting could be relevant here, but I’m not entirely confident about how it applies to missing values specifically.

Thersa 4 months ago

I remember practicing a question where we had to decide on methods for dealing with missing data, but I can't recall if clustering was the right choice.

Alease 4 months ago

I think the analyst might need to handle the missing values first, but I'm not sure which option directly addresses that.

IIBA CBDA Exam - Topic 2 Question 51 Discussion