4.7 Planes de muestreo y muestras probabilísticas: muestreo al azar simple

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Из курса от партнера IE Business School

Investigación de mercados y comportamiento del consumidor

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IE Business School

Investigación de mercados y comportamiento del consumidor

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Курс 1 из 5 — Specialization Estrategia de marketing

El primer curso de este especialización consta de dos bloques que te proveerán de los conocimientos necesarios para comenzar tu trayectoria en Marketing. Aprende las herramientas y técnicas para convertir una decisión en una investigación real, esto lo verás en el primer módulo: Investigación de Mercado. Cómo plantear la investigación, analizar los datos recogidos, interpretarlos correctamente y comunicarlos a través de recomendaciones precisas serán algunas de las cuestiones tratadas en este curso. El segundo módulo se enfoca en el proceso de toma de decisiones por parte del consumidor, resaltando los momentos clave en el proceso de compra y consumo de un producto. Adapta tu enfoque “cliente-céntrico” en decisiones de marketing mediante el análisis de cómo tus clientes toman sus decisiones, reflexionando en qué sucede en sus corazones y mentes al momento de tomar decisiones y analizando las variables que son influyentes para tomarlas.

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Marketing Research Data Collection & Analysis

En el último Módulo de este curso, aprenderá con mayor detalle los distintos tipos de datos recopilados durante el proceso de estudio de mercado y cómo evaluarlos. También se explicará la importancia de una encuesta eficaz y cómo crearla. En la segunda mitad del curso, explorará el mundo de los estudios de mercado.

4.6 Definición de muestreo y tamaño4:10

4.7 Planes de muestreo y muestras probabilísticas: muestreo al azar simple5:28

4.8 Muestras probabilísticas: muestreo estratificado y de tipo clúster8:49

Познакомьтесь с преподавателями

Shameek Sinha
Professor
Marketing

[MUSIC]

We have so far looked at the different aspects of sampling and

at the different stages on how you should collect elements from the sampling frame.

Today, we are going to start discussing a few important aspects in more depth.

The first thing you have to consider when considering a sample

is a target population.

So the question is, which units need to be included in a sample?

Is your target population narrow or broad?

Of course, if your target population is very narrow,

it's very difficult and costlier to collect your sample from that,

because you do not have many units to choose from.

On the other hand, you have to remember,

what is the difference between your target population and target market?

Usually sampling is done from your target population

rather than the target market because target market is determined

based on other aspects like your segmentation and positioning.

Now, the next important element of sampling which you have to consider

Is the sampling frame.

What is a sampling frame?

As I mentioned in the last video it is a listing from

which a sample is usually drawn.

However, you have to remember that not always a sampling frame is used,

especially when you are looking at non probability samples.

We are going to discuss this later on.

You do not use a sampling frame.

Now the question is, what is the difference between population and sample?

In population and sample,

there are certain important statistical measures which you have to remember.

First, when you’re looking at the population,

the first element of the population is the population mean.

What is the population mean?

Population mean is the measure of central tendency in the population.

The second important thing for a population is the population variant or,

for that matter, population standard deviation.

This is the measure of variation in the population or

between the units of the population.

When we look at a sample instead of a population,

we have to also consider a few statistical measures which are important to consider.

First of all, is a sample mean.

What is a sample mean?

Sample mean is noting but

an estimate of the population mean, which you do using your sample elements.

The second important thing is your sample variance or for

that matter sample standard deviation.

Sample standard deviation is the estimate of the population variance or

the population standard deviation as the case may be.

Now once you look at the sample mean and the sample standard deviation,

you have to also consider what are the variation in the sample mean?

So variation in the sample mean is basically captured through

standard error of the sample mean.

Finally, when you're drawing a sample, you have to look at the confidence interval

of the sample mean, this determines the reliability aspect of the sample.

So basically,

if you have a unreliable sample, your confidence interval will be larger,

whereas if you have a reliable sample, your confidence interval will be narrower.

Now, we start looking at, the different sampling plans,

which researchers use when drawing a sample.

The first type of sampling plan is called probability samples.

Here, under probability samples, each unit, which you select from

your population in the sample, has a known probability of being included.

This, an example of probability sample, is a simple random sample.

In this kind of sampling procedure your sampling error,

remember we discussed about how calculate the variation, this sampling error can

actually be inferred in terms of a mathematical quantity.

The second is you usually definitely need a sampling frame

while drawing a probability sample.

However, because this method is more formal, it requires more funds.

So it's usually more expensive than the other type of sampling procedure,

which is called non-probability samples.

Now, under non-probability samples,

the probability of units being included in the sample is not known.

What is an example?

Think about you being stopped in a mall for a survey.

A mall intercept is example of a non-probability sample

because you are just picked randomly

from a population of people who are walking around in the mall.

In this case, under non-probability sample,

the sampling error cannot be inferred because, in most cases,

a sampling frame is not used under non-probability sample.

Of course, this kind of sampling is done in a rather ad hoc manner.

For that reason, often it's much less expensive than any other

sampling procedure, especially compared to probability sample.

We are going to talk about different probability sampling procedures

in the next video, so looking forward to talking to you then.

Thank you.

[MUSIC]

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