How do you choose the starting point for systematic sampling?

Systematic sampling is a popular and simple method of selecting a random sample from a population. It involves choosing a fixed interval or gap between the units in the sample, such as every nth element. However, before you can apply this method, you need to choose a starting point for your interval. How do you do that? In this article, you will learn the basics of systematic sampling, the advantages and disadvantages of different ways of choosing a starting point, and some tips to avoid bias and error.

1 What is systematic sampling?

Systematic sampling is a type of probability sampling, which means that every unit in the population has a known and equal chance of being selected. To perform systematic sampling, you need to know the size of your population (N) and the size of your sample (n). Then, you calculate the sampling interval (k) by dividing N by n. For example, if you have a population of 1000 and a sample of 100, your interval is 10. This means that you will select every 10th unit from the population for your sample.

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Rodah K.

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Systematic sampling is a probability sampling that choses a data point according to a predetermined interval, this is usually according to the screening qualifications. This can be the sex of the respondents, the age, level of education, income level etc. There are different ways to pick a starting point, one ways is picking number like every 10th person and using that as your starting point. This eliminates bias but it still has more bias than the simple random method. The systematic method works for extremely large target groups to save time while decreasing bias.

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Ankesh Saxena

Podcaster @ The Collaborative Canvas | Founder - TRP Magazine | Learn and Grow | Market Research | Personal Branding | Consumer Insights | Sales | ESOMAR member | MRSI Member |
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To choose the starting point for systematic sampling, first determine the population size (N) and desired sample size (n). Calculate the sampling interval (k = N/n). Randomly select a number between 1 and k as the starting point. Ensure randomness and avoid bias by using random number generators or starting at a random point in the population list. This method provides an unbiased representation of the population for systematic sampling.

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Milene Solano

Digital Strategy Expert | Lead Generation & B2B Sales Specialist | Turning Data into Business Success
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El muestreo sistemático es un método de selección de una muestra de una población en el que se elige un elemento al azar y luego se seleccionan elementos adicionales a intervalos regulares. Es una técnica de muestreo probabilístico que busca obtener una muestra representativa de la población objetivo. Es una técnica muy sencilla de realizar, un ejemplo sería el siguiente: Se quiere realizar una encuesta a 100 estudiantes de una escuela con 1000 estudiantes. Se utiliza un intervalo de muestreo de 10, por lo que se seleccionarán los estudiantes 1, 11, 21, 31, etc.

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TARIQ EL YASSOURI

Group Director - Customer Centricity. Ex-Maserati, Ex-Mercedes-Benz. MIT Certified.
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Systematic sampling is a method of selecting participants or units from a population in a systematic and structured way, where every unit has an equal chance of being selected. In systematic sampling, the researcher starts by randomly selecting the first unit from the population, and then selects every kth unit thereafter until the desired sample size is reached. This method ensures that each unit in the population has a known and equal probability of being included in the sample, making it a type of probability sampling.

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2 How to choose a starting point?

When selecting a starting point for your sample, you can choose any number between 1 and k. For example, if your interval is 10, your starting point can be any number from 1 to 10. There are various ways to choose a starting point, such as randomly using a number generator or table of random numbers, systematically using a fixed rule or formula, or stratified by dividing the population into subgroups and applying systematic sampling within each stratum. Random selection ensures that the selection is unbiased, systematic selection makes it consistent and easy to replicate, while stratified selection ensures representativeness and balance. However, each method has its own advantages and drawbacks.

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TARIQ EL YASSOURI

Group Director - Customer Centricity. Ex-Maserati, Ex-Mercedes-Benz. MIT Certified.
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Random sampling is essential for ensuring that each element in the population has an equal chance of being included in the sample, thus eliminating bias. It helps to generalize the findings to the entire population. On the other hand, Systematic sampling involves selecting every nth element from the population, making the process consistent, structured, and easy to replicate. While it may introduce bias if there is a systematic pattern in the population, it is efficient and straightforward for large populations.

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3 What are the pros and cons of each method?

When choosing a starting point for research, each method has its own advantages and disadvantages depending on the nature and purpose. For example, random selection is the most objective and unbiased, minimizing risk of sampling error. However, it may be time-consuming and difficult to implement. Systematic selection is convenient and practical, maximizing efficiency and precision. Yet, it could be more prone to bias and error if the population has a periodic or cyclical pattern. Stratified selection ensures the sample reflects the diversity and proportion of the population, but it requires more information and calculation than other methods. It could also introduce bias or error if strata are not mutually exclusive or exhaustive, or if the sampling interval is not adjusted for each stratum.

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Milene Solano

Digital Strategy Expert | Lead Generation & B2B Sales Specialist | Turning Data into Business Success
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Ventajas del muestreo sistemático: Es un método sencillo y fácil de aplicar. Es menos costoso y requiere menos tiempo que otros métodos de muestreo. Puede ser más preciso que el muestreo aleatorio simple, especialmente si la población está ordenada de alguna manera. Desventajas: Puede ser susceptible a errores si la población no está ordenada de forma aleatoria. Puede ser difícil de aplicar si la población no está bien definida.

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4 How to avoid bias and error?

Bias and error are the main threats to the validity and reliability of your research. Bias is systematic deviation of sample statistics from population parameters due to a flaw or influence in sampling, while error is random variation of sample statistics around population parameters due to chance or uncertainty. To avoid or minimize bias and error, it is important to define your population and sample clearly and precisely, making sure they are relevant and appropriate for your research question and objectives. Additionally, use a random or stratified method of choosing a starting point, if possible, or check for any pattern or order in the population that may affect your systematic method. Additionally, use a large and adequate sample size based on confidence level and margin of error, adjusting it for any non-response or missing data. Furthermore, use a random or stratified method of selecting units within each interval, if possible, or check for any variation or heterogeneity within the interval that may affect your systematic method. Moreover, use a valid and reliable measurement instrument such as a survey or questionnaire, testing it for errors or biases before applying it to your sample. Finally, use appropriate and robust statistical methods and techniques to analyze and interpret data while reporting results and limitations honestly and transparently.

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5 How to apply systematic sampling in practice?

Systematic sampling is a useful and versatile method of sampling that can be applied to various types of data and research. For instance, if you want to measure customer satisfaction of a large online retailer, you can use systematic sampling to select a random sample of customers from their database. You can also use the method to evaluate the performance of a new product launch by dividing the outlets into strata and selecting every nth outlet for your sample. Additionally, if you want to study the impact of a social media campaign, you can use systematic sampling to select a random sample of posts or comments from the platform. A systematic method can be used to choose a starting point and an interval based on the date or time of the posts or comments, and then select every nth post or comment for your sample. This will allow you to analyze and code the content and sentiment of the posts or comments, and measure their reach and engagement.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Milene Solano

Digital Strategy Expert | Lead Generation & B2B Sales Specialist | Turning Data into Business Success
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Este tipo de muestreo es útil en la investigación científica, un ejemplo puede ser el siguiente: Un equipo de investigadores quiere realizar un estudio sobre la relación entre la dieta y la salud cardiovascular. Se selecciona una muestra de 1000 personas de una población de 100.000 personas. Se utiliza un intervalo de muestreo de 100, por lo que se seleccionarán las personas 1, 101, 201, 301, etc. Se recolecta información sobre la dieta y la salud cardiovascular de las personas de la muestra.

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How do you choose the starting point for systematic sampling?

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1 What is systematic sampling?

2 How to choose a starting point?

3 What are the pros and cons of each method?

4 How to avoid bias and error?

5 How to apply systematic sampling in practice?

6 Here’s what else to consider

Market Research

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How do you choose the starting point for systematic sampling?

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1 What is systematic sampling?

2 How to choose a starting point?

3 What are the pros and cons of each method?

4 How to avoid bias and error?

5 How to apply systematic sampling in practice?

6 Here’s what else to consider

Market Research

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