The Name Zipporah - Jay Steve

#The_Plane_like

Something is warm blooded,

Something has feathers,

Something can fly,

Is God not wonderful,

Z means Zealiance,

I means  Incredible,

P means pleasant,

P means productive,

O means Original,

R means Responsible,

A means Admirable

H means Heart 

No wonder Moses was successful because His wife name was Zipporah and that name is so powerful,

The name is special and unique,

An eagle is a bird that is very strong,

Zipporah also means bird in Hebrew,

If you check it very well Moses flew in the desert without getting weary or tired,

Name is very important people,

Don't go and give a name that don't have a good meaning to your children,

Because a good name can take you to where you can't imagine,

One thing with name is,

The more people call you with it the more it because alive,

Because the power of life and dead lies in the tongue,

So your name becomes alive and follows you in your destiny when people keep calling you that name everyday.

Make sure you give good names to your kids.

@jaysteve

Importance of Strategic and Breakdown of Effective Communication

I am here to Stand against the motion; Breakdown of effective communication in an organization.

Below are my following points:

What is a communication breakdown? 

If communication is the exchange of information between two or more individuals, a communication breakdown means not being able to get your message across properly. 

When communication breakdown occurs, it usually results in a lack of communication.

1. The effects of communication breakdowns on mental health 

Communication breakdowns cause stress and low morale in your employees. 

Namely, according to the 2021 report The Evolution of Communication, 7 in 10 Americans agree that mental health is tied to communication. 

Therefore, when communication fails, employees’ mental health deteriorates, making them more stressed and anxious.   

2.The effects of communication breakdowns on work culture

Communication breakdowns affect your work culture, too. 

They may demotivate your employees and create tension amongst the team. 

Furthermore, that kind of work culture may cost you your clients or business opportunities.

3.Unpredictable Work Environment

Poor communication causes a lack of predictability and stability within the workplace, leading to an uneasy environment for employees to work in. Employees might not clearly understand their objectives for the week or might misunderstand the process for a project, leading to unproductivity and ineffectiveness at their job. Employees and employers have a responsibility to facilitate an active dialogue in order to create a stable work environment to get their best work done.

4.Less Effective Collaboration

Collaboration and communication go hand in hand. If employees are unable to communicate effectively, it is very likely for collaboration to be effective as well. Collaboration in the workplace is important in many ways from promoting self-analysis and resulting in efficient problem solving. The effects of poor communication in the workplace set every collaborative project for failure, and almost everything in the 21st century workplace is a collaboration. 

5. Workplace Conflict:

The effects of poor communication may cause tensions to rise, resulting in a potential conflict between employees. Failure to communicate may cause employees to make the wrong assumptions, such as leaving other employees to pick up their work, when this task was not previously discussed between a team. Good communication prevents workplace from arising in the first place.

6: Low Morale

With poor communication, employees may have a harder time meeting expectations and catching up with their deadlines, resulting in them getting behind. This could leave them with a sense of guilt, embarrassment or even low self-esteem. Low workplace morale should be addressed immediately, so that employees can maintain a healthy work-life balance and continue working efficiently.

I am here to to stand with the motion that says:

Strategy for effective communication.

Strategy Communication is a combination of both theory and practice that seeks to understand the effect of culture on all aspects of marketing communications. Globalization, global branding strategies, and classification models of culture are all issues covered within this study. Strategic Communications studies the dynamics of consumer behavior, trends in marketing strategies, and shifts in global culture.

Points:

1. Enables you to lead your business:

As a CMO you need a comms strategy because it will make your job easier and more rewarding, it will also help you to lead the business and perform your duties at a higher level altogether.

The nature of being responsible for a company’s marketing means you must respond to multiple demands from the business. This can lead to a kind of tactical myopia. Your colleagues in sales will demand qualified leads – and rightly so – but if you haven’t got a mutually agreed understanding of your target sector priorities and a tight definition of your target personas, then the likelihood for disagreement or disappointment is almost inevitable.

2.    Provides focus and efficiency:

Low hanging fruitA well-formed comms strategy keeps you both focussed and aligned. It ensures you are more efficient, by focussing your time and money on the strategic priorities. The natural optimism, energy and opportunism characteristic of salespeople means that they can be distracted by whoever or whatever appears to be a quick win. That’s not to say low-hanging fruit shouldn’t be picked if it falls outside the specified target – but it must be recognised for what it is and should not distract everyone from the wider strategic intent.

A comms strategy provides a yardstick by which every effort and initiative can be assessed for efficacy. Everyone needs to understand when the pursuit of low hanging fruit has turned into an unhelpful distraction and drain on resources. If you own a comms plan, this will be as clear as the nose on your face.

3.    Results in more effective messaging:

Your audience is ...article quoteYour target audience will rarely comprise a disciplined cohort equipped with all the information they need to select your products and services. Your audience is most likely made up of broad groups of people, with similar sets of responsibilities, who are at different stages of the buying cycle. They are probably ill-informed, confused and/or insecure in their knowledge or options. Creating typical, representative personas helps to focus attention and effort. Furthermore, understanding that your target persona will be at different stages of the buying cycle enables you to develop messaging which talks to their individual information needs, while addressing their fears, motivations and irritations. This approach, which respects your target audiences’ differences, will be more persuasive because you are telling them things that matter to them in a way that helps them to move along their buying journey.

4.    It forearms you:

Your comms strategy captures your sales needs, sector priorities, personas, positioning and messaging. It provides you, and your extended team and colleagues, with many of the essential tools you need to enable you to deliver your business objectives. I’m a great believer in active decision-making. The process of formulating your comms strategy will enable you to identify knowledge gaps. Then, you can decide whether you need to secure the missing information or not.  It’s important to recognise and understand the implications of your action or inaction so that outcomes don’t come as a surprise – forewarned is forearmed.

 

5.    Brings clarity of purpose:

a communications strategy quoteWhatever your objectives and whatever your requirements, a communication strategy just makes the process of getting there more efficient, more effective and the journey so much more rewarding. Having clarity of purpose also allows you to lead the business, fend off unnecessary or irrelevant requests and direct your resources with intent.

6. Employees are more productive when they have all the necessary information in a clear, concise format.

7. All employees receive the information they need to do their jobs correctly and to meet deadlines.

8. Collaboration improves because employees bounce ideas off one another and build off each other’s thoughts and experiences. 

9. Employees can better handle conflict when they know how to communicate their ideas and listen to their peers to create a solution.

10  Effectively sharing ideas can enhance creativity and innovation, which can help grow your business.

11. The company culture improves because people feel heard and know what’s happening.

Relationships between employees and with clients are strengthened.

 

Conclusion:

Reference;

https://www.indeed.com/recruitment/c/info/business-communication-strategies

https://pumble.com/blog/communication-breakdown/

https://www.simpplr.com/blog/2021/causes-effects-poor-communication-workplace/

https://ec-pr.com/six-reasons-why-you-need-a-communication-strategy/

Sampling Techniques in Statistics

           Table of contents

Sampling Techniques: Introduction

Sampling

Different types of Sampling techniques

Choosing Between Probability and Non-Probability Samples

Probability Sampling 

Non-probability sampling

Sampling errors and biases

Introduction: 

Let’s take an example of COVID-19 vaccine clinical trials. It is very difficult to conduct the trials on the entire population, as it deals with time, money, and resources. So in research methodologies, sampling is a method that helps researchers to infer information about a population based on results from a subset of the population, without having to investigate every individual. 

A telecom company planning to build a machine learning model to predict, churn customers from their network. One way is to collect all the customers’ information and build a prediction model. This method requires high computational power and resources. So the best way is to take a sample (Subset of customers) from the population (All customers) which represents the population and build the machine learning model. This saves money and effort.

Sampling: 

Sampling is the process of selecting a group of individuals from a population to study them and characterize the population as a whole.

sampling is the selection of a subset (a statistical sample) of individuals from within a statistical population to estimate characteristics of the whole population.

The population includes all members from a specified group, all possible outcomes or measurements that are of interest. The exact population will depend on the scope of the study.

The sample consists of some observations drawn from the population, so a part of a subset of the population. The sample is the group of elements who participated in the study.

The sampling frame is the information that locates and defines the dimensions of the universe.

             A good sample should satisfy the below conditions-

             Representativeness: The sample should be the best representative of the population                         

             under study.

             Accuracy: Accuracy is defined as the degree to which bias is absent from the sample. An  

             accurate (unbiased) sample is one that exactly represents the population.

Size: A good sample must be adequate in size and reliability.

Different types of Sampling techniques:

There are several different sampling techniques available, and they can be subdivided into two groups-

1. Probability sampling involves random selection, allowing you to make statistical inferences about the whole group.

There are four types of probability sampling techniques

Simple random sampling

Systematic Sampling

Stratified random sampling

Cluster sampling

 Non-probability sampling involves non-random selection based on convenience or other criteria, allowing you to easily collect initial data. 

There are four types of Non-probability sampling techniques.

Convenience sampling

Quota Sampling

Judgmental or purposive sampling

Snowball sampling

Choosing Between Probability and Non-Probability Samples

The choice between using a probability or a non-probability approach to sampling depends on a variety of factors:

- Objectives and scope of the study

- Method of data collection 

- Precision of the results 

- Availability of a sampling frame and resources required to maintain the frame

- Availability of extra information about the members of the population

Probability Sampling 

Probability sampling is normally preferred when conducting major studies, especially when a population frame is available, ensuring that we can select and contact each unit in the population. Probability sampling allows us to quantify the standard error of estimates, confidence intervals to be formed and hypotheses to be formally tested. 

The main disadvantage is Bias in selecting the sample and the costs involved in the survey.

Simple random sampling 

In Simple Random Sampling, each observation in the population is given an equal probability of selection, and every possible sample of a given size has the same probability of being selected. One possible method of selecting a simple random sample is to number each unit on the sampling frame sequentially and make the selections by generating numbers from a random number generator.

Simple random sampling can involve the units being selected either with or without replacement. Replacement sampling allows the units to be selected multiple times whilst without replacement only allows a unit to be selected once. Without replacement, sampling is the most commonly used method.

Ex: If a sample of 20 needs be collected from a population of 100. Assign unique numbers to population members and randomly select 20 members with a random generator. Train and test split in ML problems. 

Applications

- Train and test split in machine learning problems

- Lottery methods 

Advantages

Minimum sampling bias as the samples are collected randomly.

Selection of samples is simple as random generators are used.

The results can be generalized due to representativeness.

Disadvantages

The potential availability of all respondents can be costly and time consuming.

Larger sample sizes.

Systematic sampling

In systematic random sampling, the researcher first randomly picks the first item from the population. Then, the researcher will select each nth item from the list. The procedure involved in systematic random sampling is very easy and can be done manually. The results are representative of the population unless certain characteristics of the population are repeated for every nth individual.

Steps in selecting a systematic random sample:

Calculate the sampling interval (the number of observations in the population divided by the number of observations needed for the sample).

Select a random start between 1 and sampling interval

Repeatedly add sampling interval to select subsequent households

Ex: If a sample of 20 needs to be collected from a population of 100. Divide the population into 20 groups with a members of (100/20) = 5. Select a random number from the first group and get every 5th member from the random number.

Applications

Quality Control: The systematic sampling is extensively used in manufacturing industries for statistical quality control of their products. Here a sample is obtained by taking an item from the current production stream at regular intervals.

In Auditing: In auditing the savings accounts, the most natural way to sample a list of accounts to check compliance with accounting procedures.

Advantages

Cost and time efficient.

Spreads the sample more evenly over the population.

Disadvantages

Complete population should be known.

Sample bias If there are periodic patterns within the dataset.

Stratified random sampling

In Stratified random sampling, the entire population is divided into multiple non-overlapping, homogeneous groups (strata) and randomly choose final members from the various strata for research. Members in each of these groups should be distinct so that every member of all groups get equal opportunity to be selected using simple probability. 

There are three types of stratified random sampling-

1. Proportionate Stratified Random Sampling

The sample size of each stratum in this technique is proportionate to the population size of the stratum when viewed against the entire population. For example, you have 3 strata with 10, 20 and 30 population sizes respectively and the sampling fraction is 0.5 then the random samples are 5, 10 and 15 from each stratum respectively.

2. Disproportionate Stratified Random Sampling

The only difference between proportionate and disproportionate stratified random sampling is their sampling fractions. With disproportionate sampling, the different strata have different sampling fractions.

3. Optimal stratified sampling

The size of the strata is proportional to the standard deviation of the variables being studied.

Ex: A company wants to do an employee satisfaction survey and the company has 300k employees and planned to collect a sample of 1000 employees for the survey. So the sample should contain all the levels of employees and from all the locations. So create different strata or groups and select the sample from each strata. 

Advantages

Greater level of representation from all the groups.

If there is homogeneity within strata and heterogeneity between strata, the estimates can be as accurate.

Disadvantages

Requires the knowledge of strata membership.

Might take longer and more expensive

Complex methodology.

Cluster sampling

Cluster sampling divides the population into multiple clusters for research. Researchers then select random groups with a simple random or systematic random sampling technique for data collection and data analysis.

Steps involved in cluster sampling:

Create the clusters from the population data.

Select each cluster as a sampling frame.

Number each cluster.

Select the random clusters. 

After selecting the clusters, either complete clusters will be used for the study or apply the other sampling methods to pick the sample elements from the clusters.

Ex: A researcher wants to conduct an academic performance of engineering students under a particular university. He can divide the entire population into multiple engineering colleges (Which are clusters) and randomly pick up some clusters for the study. 

Types of cluster sampling:

One-stage cluster : From the above example, selecting the entire students from the random engineering colleges is one stage cluster

Two-Stage Cluster: From the same example, picking up the random students from the each cluster by random or systematic sampling is Two-Stage Cluster

Advantages

Saves time and money.

It is very easy to use from the practical standpoint

Larger sample sizes can be used

Disadvantages

High sampling error

May fail to reflect the diversity in the sampling frame

Non-probability sampling

Non-Probability samples are preferred when accuracy in the results is not important. These are inexpensive, easy to run and no frame is required. If a non-probability sample is carried out carefully, then the bias in the results can be reduced.

The main disadvantage of Non-Probability sampling is “dangerous to make inferences about the whole population.”

Convenience sampling 

Convenience sampling is the easiest method of sampling and the participants are selected based on availability and willingness to participate in the survey. The results are prone to significant bias as the sample may not be a representative of population.

Applications

Surveys conducted in social networking sites and offices

Examples: The polls conducted in Facebook or Youtube. The people who are interested in taking the survey or polls will attend the survey and the results may not be accurate as the results are prone to significant bias.

Advantages

It is easy to get the sample

Low cost and participants are readily available

Disadvantages

Can’t generalize the results

Possibility of under or over representation of the population

Significant bias

Quota sampling 

This method is mainly used by market researchers. The researchers divide the survey population into mutually exclusive subgroups. These subgroups are selected with respect to certain known features, traits, or interests. Samples from each subgroup are selected by the researcher.

Quota sampling can be divided into two groups-

Controlled quota sampling involves introduction of certain restrictions in order to limit researcher’s choice of samples.

Uncontrolled quota sampling resembles convenience sampling method in a way that researcher is free to choose sample group members.

Steps involved in Quota Sampling

Divide the population into exclusive sub groups.

Identify the proportion of sub groups in the population.

Select the subjects for each subgroup.

Ensure the sample is the representative of population.

Ex: A painting company wants to do research on one of their products. So the researcher uses the quota sampling methods to pick up painters, builders, agents and retail painting shop owners.

Advantages

Cost effective.

Doesn’t depend on sampling frames.

Allows the researchers to sample a subgroup that is of great interest to the study.

Disadvantages

sample may be overrepresented

Unable to calculate the sampling error

Great potential for researcher bias and the quality of work may suffer due to researcher incompetency and/or lack of experience

Judgement (or Purposive) Sampling

In Judgement (or Purposive) Sampling, a researcher relies on his or her judgment when choosing members of the population to participate in the study. Researchers often believe that they can obtain a representative sample by using sound judgment, which will result in saving time and money.

As the researcher’s knowledge is instrumental in creating a sample in this sampling technique, there are chances that the results obtained will be highly accurate with a minimum margin of error.

Ex: A broadcasting company wants to research one of the TV shows. The researcher has an idea of the target audience and he can choose the members of the population to participate in the study.

Advantages

a  Cost and time effective sampling method.

Allows researchers to approach their target market directly.

Almost real-time results.

Disadvantages

Vulnerability to errors in judgment by researcher

Low level of reliability and high levels of bias

Inability to generalize research findings

Snowball sampling

This method is commonly used in social sciences when investigating hard-to-reach groups. Existing subjects are asked to nominate further subjects known to them, so the sample increases in size like a rolling snowball. For example, when surveying risk behaviors amongst intravenous drug users, participants may be asked to nominate other users to be interviewed.

This sampling method involves primary data sources nominating other potential primary data sources to be used in the research. So the snowball sampling method is based on referrals from initial subjects to generate additional subjects. Therefore, when applying this sampling method members of the sample group are recruited via chain referral.

There are three patterns of Snowball Sampling-

Linear snowball sampling; Recruit only one subject and the subject provides only one referral.

Exponential non-discriminative snowball sampling; Recruit only one subject and the subject provides multiple referrals.

Exponential discriminative snowball sampling; Recruit only one subject and the subject provides multiple referrals. But only one subject is picked up from the referrals.

Ex: Individuals with rare diseases. If a drug company is interested in doing research on the individuals with rare diseases, it may be difficult to find these individuals. So the drug company can find few individuals to participate in the study and request them to refer the individuals from their contacts.

Advantages

Researchers can reach rare subjects in a particular population 

Low-cost and easy to implement

It doesn’t require a recruitment team to recruit the additional subjects

Disadvantages

The sample may not be a representative

Sampling bias may occur

Because the sample is likely to be biased, it can be hard to draw conclusions about the larger population with any confidence.

Sampling errors and biases

             Sampling errors and biases are induced by the sample design. They include:

- Selection bias: When the true selection probabilities differ from those assumed in calculating the results.

- Random sampling error: Random variation in the results due to the elements in the sample being selected at random.

- Non-sampling error

Non-sampling errors are other errors which can impact final survey estimates, caused by problems in data collection, processing, or sample design. Such errors may include:

- Over-coverage: inclusion of data from outside of the population

- Under-coverage: sampling frame does not include elements in the population.

- Measurement error: e.g. when respondents misunderstand a question, or find it difficult to answer

- Processing error: mistakes in data coding

- Non-response or Participation bias: failure to obtain complete data from all selected individuals

Conclusion;

Reducing sampling error is the major goal of any selection technique.

A sample should be big enough to answer the research question, but not so big that the process of sampling becomes uneconomical.

In general, the larger the sample, the smaller the sampling error, and the better job you can do.

Decide the appropriate sampling method based on the study or use case.

Reference;

 • Lance, P.; Hattori, A. (2016). Sampling and Evaluation. Web: MEASURE Evaluation. pp. 6–8, 62–64.

• Salant, Priscilla, I. Dillman, and A. Don. How to conduct your own survey. No. 300.723 S3. 1994.

• Robert M. Groves; et al. (2009). Survey methodology. ISBN 978-0470465462.

• Lohr, Sharon L. Sampling: Design and analysis.

• Särndal, Carl-Erik; Swensson, Bengt; Wretman, Jan. Model Assisted Survey Sampling.

• Scheaffer, Richard L.; William Mendenhal; R. Lyman Ott. (2006). Elementary survey sampling.

• Shahrokh Esfahani, Mohammad; Dougherty, Edward (2014). "Effect of separate sampling on classification accuracy". Bioinformatics. 30 (2): 242–250. doi:10.1093/bioinformatics/btt662. PMID 24257187.

• Scott, A.J.; Wild, C.J. (1986). "Fitting logistic models under case-control or choice-based sampling". Journal of the Royal Statistical Society, Series B. 48 (2): 170–182. JSTOR 2345712.

• https://www.mygreatlearning.com/blog/introduction-to-sampling-techniques/