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Meet the moderators

Meet the moderators

Hi everyone,

As our team of moderators grow, I thought it would be good timing to create this thread so you can get to know us.

SANE Australia collaborates with organisations around Australia to provide this space. Forum moderators come from SANE and the partner organisations, bringing a variety of experiences, knowledge and backgrounds.

For those who aren't too sure what moderators do, the best way to sum it up is that we aim to ensure the community is a welcoming, productive and safe place for all.

You may see us around the place time to time. Often we lurk behind the scenes (that's not as creepy as it sounds!). We want to allow space for peers to connect with each other and believe in the power of peer to peer support.

So I will start with my introduction

I'm Nik and I'm the Community Manager at SANE Australia. My favourite colour is yellow, favourite food is avocado and could happily live on a tropical island (as long as I could watch all Manly Sea Eagles games each season).

What drives me to do this job is my strong belief in peer support and the benefits it has, and also the role technology can play in improving mental health and wellbeing.

Also.. just so you're aware.. that's not me in my profile pic

So - over to the other moderators. Perhaps you could share what organisation you're from and some random facts about yourself

Nik

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In response to: Meet the moderators

Re: Meet the moderators

Not complex sampling but:

As power decreses - what happens to type II errors?

  1. Determining the sample interval (represented by k), randomly selecting a number between 1 and k, and including each kth element in your sample are the steps for which form of sampling?

Systematic Sampling

Simple Random Sampling

Stratified Random Sampling

Cluster sampling

  1. Which statements are true about complex sampling involving clustering compared to simple random sampling?

Complex sampling may increase logistic efficiency

Complex sampling increases statistical efficiency

Complex sampling decreases statistical efficiency

To retain the same Confidence Interval Width as SRS you need to decrease the sample size

In response to: Re: Meet the moderators

Re: Meet the moderators

Off the top of my head @Former-Member - no chance of answering either question accurately but give me a little time to try and find some of my old books.

In response to: Re: Meet the moderators

Re: Meet the moderators

That's okay. 😊
What did you study again? Psych?

In response to: Re: Meet the moderators

Re: Meet the moderators

Arts first then Social Research then Teaching @Former-Member

In response to: Re: Meet the moderators

Re: Meet the moderators

That's a lot of study!
Yay for study! 🤓 📖 ☕️ 📝

In response to: Re: Meet the moderators

Re: Meet the moderators

It's been a long time since I have done any statistical work to this extent @Former-Member - from memory (which isn't that great atm ) I am pretty sure that Question 1 is Systemic Sampling as you are choosing the same random number in each interval. All the others (simple, stratified, cluster) use any random numbers generated/chosen across the whole sample being used without dividing the samples into equal/like intervals.

In response to: Re: Meet the moderators

Re: Meet the moderators

Yep @Former-Member it is alot - 7 years all up.. but I did go back 'later in life' to do teaching after working in a couple of different fields first.

In response to: Re: Meet the moderators

Re: Meet the moderators

@Former-Member Question 2: complex sampling involving clustering may 1. increase logistic efficiency and 2. decrease statistical efficiency

I am not 100% sure these are correct as it has been a long time since I have done statistical analysis to this depth but by making the sample groups 'chosen' smaller (into clusters) it makes sense.

In response to: Re: Meet the moderators

Re: Meet the moderators

Thanks @Zoe7.
I'm not sure I understand the answer..... more reading for me I guess!
Thank you.

In response to: Re: Meet the moderators

Re: Meet the moderators

@Former-Member complex sampling involving clustering is where there is a very large range/number to be sampled from. Instead of spending time/resources to research ALL possible people/objects etc. a smaller representative group is selected therefore it is easier to to do in terms of time/resources (the logistical stuff). This type of sampling can be less efficient statistically as it may not cover enough of a wide range of people/variables etc. to gain true and reliable results in whatever area is being researched.

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