HMDVA81
ASSIGNMENT 4 2026
DUE: 4 AUGUST 2026
SEMESTER 2 2026
,HMDVA81 ASSIGNMENT 4 2026
DUE 4 AUGUST 2026
Question 1: Which specific type of probability sampling method is most
appropriate for Sarah's study? Give reasons for your answer.
Stratified random sampling is the most appropriate probability sampling method for
Sarah's study.
1. Heterogeneity of the population
The population of unemployed youth aged 18–30 years in the North West province is
likely diverse in terms of age groups, gender, education levels, and geographic
location. Stratified random sampling allows Sarah to divide this heterogeneous
population into homogeneous subgroups (strata) based on key characteristics
relevant to her study.
2. Ensuring adequate representation of key subgroups
Sarah's study includes variables such as age groups (18–20; 21–24; 25–30), gender,
and level of education. Stratified sampling ensures that each of these subgroups is
adequately represented in the sample. Without stratification, simple random sampling
might by chance under-represent certain age groups or genders, compromising the
study's ability to draw meaningful conclusions about these variables.
, 3. Improved precision and reduced sampling error
Because stratified random sampling divides the population into homogeneous strata
and then samples from each stratum, it typically yields more precise estimates with
smaller sampling errors than simple random sampling of the same size. This is
particularly important for Sarah's quantitative, positivist approach, which aims to use
statistical analysis to identify the main drivers of youth unemployment.
4. Allows for analysis of subgroups
Sarah plans to measure age groups, gender, and education levels as variables.
Stratified sampling enables her not only to analyse the overall sample but also to
make valid comparisons across these subgroups for example, comparing
unemployment drivers for young women versus young men, or for different age
cohorts.
5. Feasibility and practical considerations
Sarah has a defined target population (unemployed youth aged 18–30 in the North
West province and a sample size of 500. She can obtain a sampling frame e.g. from
the Department of Employment and Labour, StatsSA, or provincial databases) that
lists unemployed youth, allowing her to stratify by relevant characteristics before
randomly selecting participants from each stratum.
Implementation:
Sarah should:
ASSIGNMENT 4 2026
DUE: 4 AUGUST 2026
SEMESTER 2 2026
,HMDVA81 ASSIGNMENT 4 2026
DUE 4 AUGUST 2026
Question 1: Which specific type of probability sampling method is most
appropriate for Sarah's study? Give reasons for your answer.
Stratified random sampling is the most appropriate probability sampling method for
Sarah's study.
1. Heterogeneity of the population
The population of unemployed youth aged 18–30 years in the North West province is
likely diverse in terms of age groups, gender, education levels, and geographic
location. Stratified random sampling allows Sarah to divide this heterogeneous
population into homogeneous subgroups (strata) based on key characteristics
relevant to her study.
2. Ensuring adequate representation of key subgroups
Sarah's study includes variables such as age groups (18–20; 21–24; 25–30), gender,
and level of education. Stratified sampling ensures that each of these subgroups is
adequately represented in the sample. Without stratification, simple random sampling
might by chance under-represent certain age groups or genders, compromising the
study's ability to draw meaningful conclusions about these variables.
, 3. Improved precision and reduced sampling error
Because stratified random sampling divides the population into homogeneous strata
and then samples from each stratum, it typically yields more precise estimates with
smaller sampling errors than simple random sampling of the same size. This is
particularly important for Sarah's quantitative, positivist approach, which aims to use
statistical analysis to identify the main drivers of youth unemployment.
4. Allows for analysis of subgroups
Sarah plans to measure age groups, gender, and education levels as variables.
Stratified sampling enables her not only to analyse the overall sample but also to
make valid comparisons across these subgroups for example, comparing
unemployment drivers for young women versus young men, or for different age
cohorts.
5. Feasibility and practical considerations
Sarah has a defined target population (unemployed youth aged 18–30 in the North
West province and a sample size of 500. She can obtain a sampling frame e.g. from
the Department of Employment and Labour, StatsSA, or provincial databases) that
lists unemployed youth, allowing her to stratify by relevant characteristics before
randomly selecting participants from each stratum.
Implementation:
Sarah should:
