Sampling Era - Yet Unexplored
Digital signal processing (DSP) is concerned with the representation of signals by a sequence of numbers or symbols and the processing of these signals. Digital signal processing and analog signal processing are subfields of signal processing. DSP includes subfields like: audio and speech signal processing, sonar and radar signal processing, sensor array processing, spectral estimation, statistical signal processing, digital image processing, signal processing for communications, control of systems, biomedical signal processing, seismic data processing, etc. Sampling is the process of selecting units from a population of interest so that by studying the sample we may fairly generalize our results back to the population from which they were chosen. Let's begin by covering some of the key terms in sampling like "population" and "sampling frame." Then, because some types of sampling rely upon quantitative models, we'll talk about some of the statistical terms used in sampling. Finally, we'll discuss the major distinction between probability and Non probability sampling methods and work through the major types in each.
The time to evaluate the effectiveness of a sampling program for a new product introduction is during the planning stages. Sampling consistently is ranked the "most effective" tactic for gaining trial. Sampling programs are being redefined by sophisticated targeting techniques, improved profiling and more efficient distribution methods. Marketers must gain trial to stay competitive with the broad range of new products that continually enter the market.
A product sampling program will impact the rate of trial purchases. Effective advertising and promotion will result in a stable level of trial purchases and an effective sample program will speed up this process
Marketers require a system to verify and measure the effectiveness of their programs. Below are some guidelines to follow to measure the effectiveness of a product launch
All households in a control area should not receive a product sample and all households in a test area should receive a product sample and all households in a test area should receive a product sample. This way you can assure that different responses between two groups are due to the impact of product sampling.
Phone interviews provide information quickly and inexpensively and can be designed to randomly select households within the test and control areas. They also tend to be extremely accurate.
New technologies’, targeting methods and an increase in the number of products entering the marketplace has made sampling a more powerful and widely used tool. Brand managers are using sampling not only for new product introductions, but as product reinforcement strategies that accompany traditional advertising media. There has been a shift away from mass sampling to a more targeted approach utilizing advanced targeted methodologies, resulting in more money being spent in the right market and building equity by communicating with consumers. A targeted program drives brand awareness because it is integrated and leveraged. It is very important to target consumers at the right time and place such as implementing a new baby program in hospitals by providing new mothers with free samples of baby products or products for small children delivered to parents picking up their children at child care centers
Process of Sampling[/b][/b]
The sampling process comprises several stages:
Defining the population of concern
Specifying a sampling frame, a set of items or events possible to measure
Specifying a sampling method for selecting items or events from the frame
Determining the sample size
Implementing the sampling plan
Sampling and data collecting
In signal processing, sampling is the reduction of a continuous signal to a discrete signal. A common example is the conversion of a sound wave (a continuous-time signal) to a sequence of samples (a discrete-time signal). A sample refers to a value or set of values at a point in time and/or space. A sampler is a subsystem or operation that extracts samples from a continuous signal. A theoretical ideal sampler produces samples equivalent to the instantaneous value of the continuous signal at the desired points.
Well the common types of sampling can be represented as follows:[/b][/b]
Probability & Non-Probability Sampling[/b][/b]
Probability Samples
Simple Random Sample
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Systematic Sampling
Random Route Sampling
Stratified Sampling
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Multi-Stage Cluster Sampling
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Non-Probability Samples
Purposive Sampling
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Quota Sampling
Convenience Sampling
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Snowball Sampling
Self-Selection
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Non-Response