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WEBINAR ON QUANTITATIVE DATA ANALYSIS [PART 2] — Transcript

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0:00the research webinar this afternoon and
0:03for this afternoon let me introduce our
0:06second speaker he is a comlaude graduate
0:10of bachelor of secondary education major
0:13in mathematics at cavite state
0:15university emus campus in year 2018 he
0:19took and pass his licensure examination
0:22for professional teachers in the year
0:242019 he teaches both junior and senior
0:28high school students
0:32mathematics
0:34year
0:37more he is now taking master
0:41of master of arts in mathematics
0:43education with specialization in college
0:46teaching at theppine nor normal normal
0:49university manila at this point let us
0:52all welcome our speaker for this
0:54afternoon m jerome ramos
0:59Um, nung umpisa tayo nagawa ng research,
1:01no, what have we observed is we already
1:04done doing the title, the proposal ba
1:08chapter 1, chapter 2 and chapter 3 until
1:12we have to gather the datas no? And what
1:16happened next? Syempre hindi mawawala sa
1:19pagiging researcher natin yung defense,
1:22no? This is my research and this is my
1:25result. But the problem is when you are
1:28about to present your result there are
1:30some discrepancy happened and the
1:34reaction of the
1:37panel and your research advisors will be
1:40like this no? Yung tipong mapapasabi na
1:43lang sila na what happened? Maganda yung
1:46flow ng title, maganda yung proposal.
1:49pagdating sa sa result there will be
1:52some problems occured. So one thing na
1:55nakita ko doon I am also became a panel
1:59last ano the last 2 years face to face
2:02pa iyon no so there will be some problem
2:05problem in terms of their ano no
2:08treatment used in a given research and
2:11for today let me uh present this no but
2:15uh I want you to have a positive uh mind
2:19in terms of creating the research and in
2:21the end I hope after this no uh after
2:25knowing things about what I'm going to
2:27discuss, you can say this to yourselves.
2:30You can say this to your research
2:31advisor. I have done that research and
2:35now formally started no uh we are going
2:39to discuss two things that is commonly
2:42asked as a researcher especially when
2:44you are dealing with quantitative
2:46research. So what are those? First one
2:49is we're going to ask how to choose the
2:53right inferential statistics treatment.
2:56Ito yung pinaka ano eh pinaka-main goal
2:59natin. And sometimes this is also the
3:01problem uh happened kasi after you
3:04gather the data mali pala yung treatment
3:06na binigay mo. The problem will occure.
3:09Ibig sabihin you need to do all those
3:11things again and again. No of course how
3:15to perform inferential quantitative
3:19analysis. So on this part no heart of
3:22the given of statistics is how we are
3:26going to interpret the data.
3:29Hindi yan about to gather the data lang.
3:31But then the main heart ng mismong
3:34statistics natin is how are you going to
3:37interpret? And sometimes
3:39si statisticians they are about to give
3:41their interpretation on the data but
3:44then si researcher mali ng
3:46pagkakaintindi on that interpretation na
3:49binigay. That is because we lack of this
3:52what we called basic. Kulang tayo ng
3:55basic. And by that I will give you some
3:57tips. I will give you some techniques on
4:00how are we going to choose for those
4:02treatments. Okay, this is somehow
4:05applicable to me when I am about to do
4:08my research about quantitative analysis.
4:11Okay. But before that, let us have first
4:14some recap of what we have discussed no
4:16from the very start about descriptive up
4:19to inferential. Okay. So what would be
4:22the process behind those quantitative
4:25research? Okay. So let me tell you a
4:27given story. Okay.
4:32So for example I am a teacher of course
4:36and uh as you can see I'm going to get
4:39the scores of my students in a class.
4:42And while checking the papers and
4:44knowing the results of my student, what
4:46happened is I have I observed something
4:50from those results. First thing is
4:53majority of my student failed the
4:55examination.
4:57The next is 65% of boys failed the
5:02examination and last one is 35% of girls
5:06failed the given examination. So by
5:10means of this we apply descriptive
5:13statistics. We are just about to
5:15describe what is in the scores that we
5:17have. Okay? That is what we call
5:20descriptive statistics. Now gusto kong
5:22taasan. I need to I need to explain why
5:27does the exam or why does the score of
5:30the student is low. Sobrang baba. So
5:33ngayon I'm going beyond the data. Now
5:37I'm going to have my inferential
5:39statistics. The first thing I will do is
5:41I will going to know if there is a
5:44difference between the scores of male
5:46and female. Baka there will be some
5:48discrepancies on how they understand the
5:51subject or the topic I have. Okay. From
5:54both male and female.
5:57And by that after I I know the
5:59difference between them, no I can say na
6:02pwede nating makuha na talagang mataas
6:04talaga yung scores ng males from that
6:06part from that from that exam or mababa
6:10talaga yung score ni female from that
6:12exam. No by means of comparing
6:15and I need to get uh ng mas mataas pa.
6:20Okay, on this part, ito ay just as uh we
6:23are just getting the difference of those
6:26two groups. Now, taasan natin siya. I
6:28want to know the reason why they are
6:32failing the exam, no? So, on this part,
6:36I'm going to connect or get the
6:38relationship of the given score to one
6:42of the given problem. So, napansin ko
6:44kasi so most of the students have slip
6:46deprivation before the exam. So ngayon
6:49ico-connect ko sila and going to create
6:52a relationship between them. And by that
6:54doon magsisimula yung infer types of
6:58inferential statistics. We are getting
7:00the relationship of them. And I find out
7:03that
7:05uh on this part there is a connection
7:07between the scores and the sleep
7:08deprivation of my students. And by that
7:13lumabas sa result na when the hours of
7:16the sleep is lower what happen to the
7:19scores becomes lower as well. That is
7:22how we apply descriptive statistics and
7:26inferential statistics. That is why they
7:29are connected to one another. We cannot
7:31create any inferential statistics if we
7:34cannot have a descriptive one. Okay,
7:37that is uh that is just a background of
7:39what we have discussed from the from the
7:42morning session. And for today,
7:45we're going to proceed on answering the
7:47two question I gave to you. No, the
7:49first one is how to choose the right
7:53inferential statistics treatment. Okay,
7:56ito yung pinakamahirap gawin. But then
7:58simply para mas mapadali natin siya, you
8:01are just going to answer three
8:03questions. Tatlong tanong lang. So what
8:07are those three questions? The first
8:09question is from your research, what
8:13will you do with the given data you
8:15have? Dapat alam mo yung pinaka-process
8:18na gagamitin mo or ano yung gagawin mo
8:20sa mismong data na meron ka? That would
8:22be the first question. The second
8:24question is what type of data do you
8:28have in a given research? So by means of
8:32knowing the data or what type of data do
8:34you have mas makikita mo yung pinaka
8:37treatment na pwede nating gamitin. And
8:39lastly,
8:42how many samples do you have on this
8:44part? Um, in descriptive part kasi we
8:48are just dealing with one variable that
8:51is being test or that is being described
8:54in a given sample. This time we are
8:57dealing with more to or more for us to
8:59identify those some factors affecting
9:02the others and such and such. No. So
9:04that is the question number three. How
9:06many samples do you have? Okay. And now
9:09let us proceed with discussing the first
9:13question.
9:15What will you do with the data? In terms
9:18of inferential statistics, we are just
9:21dealing with two process. The first one
9:24is are we going to compare the data and
9:28the other one is are we going to create
9:30association between the data. So when we
9:33say comparing the data we are looking
9:36for the significant difference or simply
9:40we are just getting if there is any
9:42similarities between those variables
9:45that we have and of course dito rin in
9:49terms of comparing if you are trying to
9:50compare those datas we can also try to
9:53find out if there is a given treatment
9:56or is that given treatment you have on
9:59the given research for example ito yung
10:00mga researches na ah merong intervention
10:03na ibibigay to cause a given change. We
10:06are going to compare those changes.
10:08Okay? If it is effective or not. Okay?
10:11That is uh th are the goals of the first
10:14um inferential statistics that we have,
10:17no? Uh inferential statistic in a form
10:20of comparison. The next one is in a form
10:23of association. So in terms of
10:25associating the datas that we have, the
10:28first thing is we just we trying to find
10:30out how does one variable affects the
10:34other variable that we have. We are just
10:36going to know gaano ba ano ba gaano ba
10:40nakakaapekto iyung first variable from
10:42the other. Later we're going to give an
10:44example about that. The next one, is
10:47there any correlation? Ito yung madalas
10:48nating nakikita sa mga research
10:50questions, no? Is there any correlation
10:53between two variables? Next one is does
10:57a given variable affects the other
10:59variable? So on this part we are just
11:02trying to find out if there is a
11:03relationship. No. Lastly, is there a
11:06significant relationship between
11:09variables? So that is the ano no uh this
11:12on how are we going to distinguish the
11:14two is when we are comparing we are just
11:17we're just getting the difference
11:18between two sets. Okay. And when we are
11:21associating we are getting the
11:24connection between variables. Okay? That
11:26is associations of the given inferential
11:30statistics. Okay? That is the question
11:33number one. If you can identify kung
11:35kino-compare mo lang ba yung data or
11:37nag-a-associate ka ng data, mas maganda
11:40kasi mas magiging concise yung magiging
11:42data niyo. There's a lot of treatment na
11:45possible nating gamitin. But then what
11:47we can do is we can reduce the choices.
11:50Okay. Next one is question number two.
11:55What type of data do you have? This one
11:57is already discussed by Sir Gab, no the
11:59first session natin. So on this part we
12:02have four specific datas no types of
12:04datas right ratio, interval, ordinal and
12:08nominal we are just going to some of
12:10things about what we have discussed from
12:12the morning session first thing is all
12:15of them can be in a form of label they
12:17have their own label kumbaga if we have
12:21nominal dat there is a specific label
12:24given on a given variable binibigyan
12:26natin siya ng pangalan and everything in
12:29terms of the scores even the scores if
12:31you got the scores of 35 that 35 is
12:34considered to have a label okay may
12:37pangalan siya and of course in terms of
12:40meaningful order sino lang ba dito sa
12:42apat na to considered to have meaningful
12:44order or we can rank them kung sino
12:47iyung mataas sa mababa kasi in this one
12:51nominal data don't have that one okay
12:54kasi hindi natin siya maa-arrange kung
12:56sino doon yung mataas or mababa for
12:58example we have the gender we cannot
13:00identify sino doon yung mas mataas okay
13:02in terms of female and male. So
13:05therefore in terms of meaningful order
13:07we have ratio, interval and ordinal.
13:10Okay? In terms of measurable measurable
13:13difference naman, um we are getting the
13:16ano no the interval between two sets of
13:18scores that we have. No, if we get that
13:21we have this what we called ratio and
13:23interval datas. Okay. If there is a uh
13:26if there is a measurable difference
13:28between those uh
13:31uh variables that we have or those
13:33scores that we have, it is on the part
13:36of ratio and interval. Ano yung
13:38pagkakaiba lang ni ratio and interval is
13:41we have true zero value pagdating kay
13:44ratio. Nag-e-exist yung zero value
13:48pagdating kay ratio. And I just want you
13:50to analyze then as well on how are you
13:53going to find the treatment if you are
13:55using parametric study or or parametric
13:59uh data or you are using nonparametric
14:02data. And for this, if you identify your
14:06data as an ordinal and nominal, we can
14:08say it is nonparametric.
14:11And if it is in ratio and interval we
14:13can say it is parametric. Okay, that is
14:16the second question. What type of data
14:18do you have? Okay, let us proceed with
14:21the third one.
14:24Okay, for the third one, how many
14:26samples do you have? How are you going
14:28to know the number of samples do you
14:30have? Simply, it is it is two or more
14:32samples lang naman ang titingnan natin
14:34dito, no? So, here, as you can see, we
14:38can uh differentiate them by one sample
14:41and two or more samples. In one samples
14:43of course we are just dealing with one
14:46sample or one in the given population
14:48hindi na natin dine-define or hinahati
14:51iyung pinaka-ismong sample natin. Okay?
14:54And the other one is on a given
14:55population after we got the given sample
14:58hinahati natin sila sa dalawa, sa tatlo
15:01or even more. Okay? So kapag ganon, we
15:04can consider it as dependent or
15:06independent sample. Okay. So, how are we
15:09going to distinguish if it is dependent
15:12or independent? Isa-isahin natin. Let us
15:15start first with dependent. No, for the
15:19dependent, we are just dealing with one
15:21group. Okay, sir, para lang din siya
15:24palang one sample. Yes, but then on that
15:26one group, we are dealing with two
15:28variables connected on one group. Okay,
15:31kino-compare natin if you are having
15:34this dependent sample, we are trying to
15:37compare two results from one person. So
15:40kumbaga kapag nagpasagot ka sa mga
15:43respondents mo at kumukuha ka ng
15:45dalawang measurement on that person, it
15:49means that you are using dependent
15:51samples. Okay? Because that uh those two
15:54datas you are getting from one person is
15:58galing lang sa kanya and dependent yun.
16:00dalawa sa kanya. Okay? That is what we
16:03call dependent sample. The next one is
16:05independent. So this part kapag ang
16:08sample mo naman is you are going to
16:10divide the sample into three or more
16:13groups ang tawag na or two or more
16:15groups ang tawag natin doon ay
16:16independent sample. on this part is for
16:20example a given sample of Unida
16:22Christian Colleges students. We are
16:24going to divide that or split that into
16:26two in terms of gender male and female.
16:30So on that part they are not connected
16:32to one another. Hindi sila mag ah uh ah
16:36there is no connection in terms of those
16:39two groups. Okay? So therefore it is in
16:41a form of independent. hindi
16:43makakaapekto iyung unang grupo sa
16:45pangalawang grupo. That is why it is
16:48called independent. Okay? And it could
16:50lead to two or more samples depend on
16:53the given research that you have. Okay?
16:56So that is question number three. Now
16:58let us have to summarize those three
17:01questions that I gave to you no to
17:03identify the given s uh the given
17:07treatment that you're going to have. do
17:09not ano no overwhelm with the names of
17:11the given treatment. So here we have one
17:14sample test dependent sample Test
17:18independent sample analysis of variants
17:20which is iyung kilalang-kilala prison R
17:23correlation for those things. Okay? So
17:25as you can see here we are going to
17:27answer first question what are you going
17:29to do with the data? Simply you are just
17:31going to compare or associate. Okay,
17:34after knowing that, you are going to
17:36find also what type of data do you have
17:39is either in a form of parametric or in
17:41a form of non-parametric data. Kapag
17:44parametric, we have ratio and interval.
17:47Kapag nonparametric, we have nominal and
17:50ordinal. Okay? And number three
17:53question, how many samples do you have?
17:55Is either one sample or two sample.
17:57Pagdating kay two sample, we're going to
17:59distinguish if it is dependent sample or
18:03independent sample. Now, we are going to
18:06discuss all those uh parametric studies.
18:10But then later, bakit hindi ko
18:12idi-discuss nonparametric? Malalaman
18:14niyo rin. Okay. So, start tayo kay
18:17parametric studies, no? Or treatment
18:19rather.
18:21So, let us start first on one sample
18:22Test. Kailan natin pwedeng gamitin si
18:25one sample? First is we are just going
18:27to compare the data. If you are about to
18:29compare the data, we are using one
18:31sample Test. Okay? And if we are dealing
18:34with parametric data, okay, the type of
18:37the data that we have is parametric
18:39either ratio or interval we can use one
18:42sample Test. Particularly depends on the
18:46given number of sample. If you are
18:48dealing with one just one sample
18:52it means that
18:54obviously you are dealing with one
18:55sample Test. Okay? So by means of
18:58answering those three question kaya na
19:00nating ma-distinguish kung ano iyung
19:02treatment na pwede niyong pagsimulan ng
19:04research. Okay? That is the first one.
19:07One sample Test. Okay, so discuss natin
19:10ano bang meron kay one sample Test?
19:12Nono. On this part you have the main
19:15data. Okay, you are going to get the
19:16scores and the given data for example
19:19the height of the given person okay on
19:21the on emus for example and you are
19:25going to connect that on specific data
19:27sir isa lang naman yung kinuha mong data
19:29from that given sample kanino mo siya
19:32ico-connect so in one sample t test we
19:34are trying to find the uh statistical
19:38difference between a given mean and a
19:40given hyp hypothesized value. Okay? Saan
19:44natin kukuhain yung hypothesis value na
19:46iyon? For example, kinuha natin yung ano
19:48unexpected value. For example, in here
19:51in UCC, we have this what we called UCAT
19:54exam, no? And that exam, we have this
19:57what we call target score. Okay. We are
20:01trying to find out if iyung scores ng
20:03mga bata ngayon if they are about to
20:05take that exam is
20:08comparable doon sa ating target score or
20:11kung same ba sila or there is a
20:13difference ba doon sa given expected
20:16value na gusto nating makuha sa mga
20:17bata. On by that we are dealing with one
20:20sample teeters. Aside from that, we can
20:23have the benchmark no'm
20:27standards naman. Kung may bibigay tayong
20:29standard na uh for example according to
20:32the research no we can connect it with
20:34research then or literature reviews no
20:37according to the research the height of
20:39the Filipino people in the Philippines
20:41syempre is in in terms of male is in 54
20:45for example just like that no there is a
20:48benchmark or there is a standard na
20:49kino-compare natin based ah yung data na
20:53kinukuha natin ngayon okay and lastly we
20:57can have it hypothetically. Okay? So,
20:59mag-a-assume lang tayo na iteng ito
21:03iyung maging result niya afterwards.
21:05Okay? So, that is on how we are going to
21:08use one sample Test. We're just going to
21:10get a scores and connect it with just
21:13only one value. Okay? Isang value lang
21:16iyung tinitignan natin considered to be
21:18it is standard. It is expected a form of
21:21benchmark on a given company for example
21:24or is a form of just a hypothetical
21:27value. Okay? Just a guest lang no. So
21:30another thing na pwede rin dito in terms
21:32of one sample Test is uh by identifying
21:36the statistical difference between
21:38change scores and zero. In this part for
21:40example ako magte-take ako together with
21:43me my sample magta-take kami ng exam
21:47post test and pretest. I'm going to get
21:50the difference of that scores and I'm
21:52going to compare it to zero. Bakit kay
21:55zero natin ico-compare? We know if we
21:57have the difference that is zero, it
21:59means that there's no totally difference
22:01at all. But then if there will be exact
22:03value, for example, it is negative or
22:05positive. Kapag negative ibig sabihin
22:07mas bumaba 'yung score mo. Kapag
22:09positive naman 'yung nakuha nating
22:12difference ibig sabihin mas tumaas yung
22:14score mo. Nagkaroon ka ng gain scores.
22:16By that we can use one sample Test. We
22:19are just comparing the difference of
22:21those scores to the given zero value.
22:25Okay? Kay zero lang natin siya
22:27kino-compare. So be careful on using one
22:29sample Test.
22:31Next one is Test dependent. So paano
22:34natin mao-consider na you are going to
22:36use test dependent sample? First, how
22:39are you going to uh what are you going
22:41to do with the data? The first one is
22:43you're going to compare the data. The
22:45next one is what type of data do you
22:47have in this? It should be in parametric
22:50data. And lastly is how many samples do
22:54you have? And this part it is two sample
22:58specifically dependent sample. By that
23:01we can say it is a test dependent sample
23:05yung gagamitin nating treatment in a
23:07given research. Let me give you the
23:09process on how are we going to deal with
23:11Test dependent sample.
23:14Okay.
23:16So for example we have here one group.
23:19We know ba pag sinabi nating test
23:21dependent kagaya ng sinabi ko kanina
23:22kapag dependent test dependent sample we
23:25are just dealing with one group. Okay.
23:28Uh but then we are going to get two
23:30samples from that two variables from
23:32that one group. Let us have a given
23:35example.
23:36On ttest dependent sample we are trying
23:38to find out statistical difference
23:40between two time points. For example the
23:45after scorse. Okay? So we are going ako
23:49magte-take ako ng exam but then this
23:51time ang ico-compare ko is kung may
23:54pagbabago ba from before and after. Ano
23:57yung pagkakaiba niya kanina sa one
23:59sample Test? Kanina kinuha muna natin
24:01yung difference. Pinag-subtract muna
24:03natin yung dalawa. 'yung before score
24:05and after scores. Okay? Pero dito hindi
24:08natin siya ipagsu-subtract. We are just
24:10going to compare those two before and
24:15after scores. Okay?
24:18Next one is statistical difference
24:21between two conditions. Dito papasok
24:23'yung experimental no? There is a
24:26control variable and the other one is
24:28experimental variable. So dito
24:31mapapansin niyo si TT's dependent sample
24:33possible siyang gamitin as a part of
24:36experimental. Bakit? For example, if I'm
24:38going to find out the effectivity of a
24:41lotion sa skin ko. Okay? So what I can
24:45do is from my left hand or left arm, ang
24:49gagamitin ko is commercial na lotion.
24:53And from the right arm, ang gagamitin ko
24:55naman is yung pinaka-experiment ah 'yung
24:57pinaka ginawa kong lotion. Okay. So now,
25:00if I'm going to compare the two, I am
25:02going to use Test dependent. Isa lang
25:05yung ginamit kong tao pero dalawang
25:06treatment yung binigay sa akin. Okay? So
25:09that is statistical difference between
25:12two condition. Aside from that, pwede
25:15rin naman for example in terms of
25:16fruits, gusto mo mas ah in terms of
25:20plants for example in scientific kasi
25:22to. So in terms of plant naman ah
25:25dalawang klaseng plant uh you are going
25:27to deal with only one plant for example
25:30um
25:34malunggay plant for example. So in terms
25:36of malunggay no so magbibigay ka ng
25:38treatment on those two. Ah two groups pa
25:41rin siya pero we are going to consider
25:42it one. Bakit? Kasi parehas naman siyang
25:45malunggay plant. Walang pagkakaiba in
25:47terms of their characteristics. So
25:49gagamitan natin sila ng parehas na tre
25:50ah ng magkaibang treatment. control
25:53variable na treatment and experimental
25:55variable and we are going to compare by
25:57that we are going to use ttest dependent
26:00sample
26:02let us have proceed with the next one
26:04statistical difference between two
26:06measurements okay so on this part we are
26:09dealing with trial trial one trial 2
26:12trial 3 we're going to compare. Okay.
26:14For example, from trial trial one, iyung
26:16una mong try for example um running for
26:21example no. So in terms of sprint for
26:24example, you're going to get the time uh
26:26involved for the trial one and you're
26:28going to compare that on the time nung
26:30nag-try ka ah nag ah nag-trial to ka. So
26:33you're going to compare. May pagbabago
26:35pa after kong tumakbo ng pangalawang
26:42ako lang din ang tumakbo. Ah ako lang
26:44din yung from the trial one. Ako lang
26:46din ang gumawa for the trial 2.
26:50Next
26:51is statistical difference between match
26:54pairs. This time may pagbabago lang
26:56tayo. Okay? We are going to get one
26:58thing from a given sample considered to
27:01be paired. What are those examples? No?
27:03So ngayon makikita niyo doon sa ating
27:06illustration they are connected with the
27:08given uh
27:11data iyung dalawang tao na tinutukoy ko
27:13dito considered to be paired. For
27:15example I'm dealing with husband and
27:18wife. Okay. If I'm dealing with those
27:20two and ang kinukuha ko ay mar um their
27:25marital happiness nila no? So what will
27:28happen is I'm going to compare kung
27:30masaya pa ba yung babae or iyung wife
27:33doun sa husband niya or masaya pa ba
27:34iyung husband niya doun sa wife niya.
27:36Okay? By that we are going to compare
27:38them in pair. Okay? In pair natin siya.
27:41Or pwede rin namang their idea about
27:44abortion. Okay, just like that no uh
27:46what would be their ano no perspective
27:48in terms of abortion and you're going to
27:50create uh likeart scale about that? We
27:53can compare their ano no perspective
27:55from the side of the female wife and the
27:58side of the male which is the husband.
28:01So by that we are going to deal with
28:02statistical difference between match
28:05pairs. Okay? That is t dependent sample.
28:08Isa lang pero dalawang treatment,
28:11dalawang intervention ang binigay sa
28:13kanya. Okay, the next one we are dealing
28:16with ttest independent sample. So kapag
28:20sinabi nating test independent sample,
28:23first of course we're going to compare
28:25the data. We're just going to compare
28:26the data and it is parametric. Okay?
28:30Parametric yung pinaka-data natin. And
28:32lastly, how many samples do we have? We
28:34have two samples particularly
28:37independent sample. But then this part
28:40we are dealing with just exactly two
28:43samples. Okay? Tatandaan two samples
28:46lang tayo. Okay? Kapag lumagpas ng tatlo
28:48yan, iba na yung gagamitin nating
28:50treatment. Okay? Ang tawag natin diyan
28:51ay test independent sample. Compare
28:56parametric uh data independent sample
29:00with two groups. Okay. So proceed tayo.
29:03How are we going to uh
29:06give the process for the Test
29:07independent sample?
29:10Okay.
29:12So the first thing is statistical
29:13difference between two groups. Ito iyung
29:15pinakamadaling madalas na ginagawa nono.
29:17For example, we have two different
29:19groups, male and female. Magte-take ng
29:22exam. same exam ang ang ite-take. So
29:25we're just going to compare uh about
29:28there is there any statistical
29:30difference between their scores? Okay?
29:32As simple as that. Yun yung
29:33pinakamadaling test independent sample.
29:36The second one is yung considered to be
29:38statistical difference between means of
29:40two intervention. Ito yung medyo malala
29:43or kailangan ng ano no tamang process
29:46for us to have a
29:49correct result in the end. So paano po
29:51ito non? On this part we have one group.
29:54Isa lang yung pinaka-group natin. We are
29:56going to split that into two groups.
29:58Okay? Hahatiin natin sila randomly.
30:02Okay? Hindi tayo pwedeng mamili no na
30:04nandito si crush niya pagsasamahin mo
30:06sila. Hindi pwede. So random natin
30:08siyang gagawin. Okay. So what we are
30:10going to do here is from that two ah two
30:14sets of group in consider to be ano no
30:17defined. Ibig sabihin hindi natin siya
30:19hinahati into category. Nawala po yung
30:22mic. Nawala po yung mic. Hello
30:26meron. Okay na hindi lang ako sanay.
30:29Okay. So this part we are going to deal
30:32with two interventions. Okay? So anong
30:35gagawin nila? Magta-take sila ng
30:37different intervention. The first group
30:39is
30:41meron naman. Okay. The first group is
30:44we're uh we're going to have condition
30:47controlled group. Okay? And the other
30:48one is experimental group. Okay. So what
30:51we are going to do is they are going to
30:53take an exam afterwards after the
30:55intervention happened. So ico-compare
30:58natin kung may pagbabago pa on those two
31:02groups natin. Okay? If there will be ang
31:05ginagamit natin dito is test independent
31:08sample. If you are trying to find out if
31:10those two intervention has uh the same
31:14or different in terms of the result,
31:16okay, magbabago ba yung idea or
31:19magbabago ba yung ah comprehension nila
31:22for example after watching video and
31:24yung isa naman after reading. Okay? So
31:26those are the things na pwede nating
31:28gawin. The next one is statistical
31:32difference between the mean of two
31:34change scores. So ano iyung tinutukoy
31:36natin dito? It is just the same as the
31:38first one. We're going to have two
31:41groups. We're just going to take the
31:42exam, magkaparehas na exam. But then
31:45this time they are going to what? They
31:48are going to get the pretest and post
31:51test. Okay? Preest score and the post
31:54test score on both group. Okay? And
31:57after that, ico-compare ngayon natin in
32:00terms of their change scores. Tingnan
32:02natin kung sino sa kanilang dalawa. For
32:04example, si ABM students and the other
32:06one is TEM students. Tinignan natin kung
32:09magkakaroon ba ng gain ng knowledge.
32:11Sino yung may mas mataas na gain in
32:13terms of knowledge or learning pagdating
32:15sa general mathematics. So umpisa bago
32:18magpasukan is nagte-take sila ng exam.
32:21Okay? Then afterwards after the
32:23intervention no after the teaching
32:25process na nangyari ah sa pagtuturo ni
32:27teacher, parehas na teacher tayo doon.
32:30So what will happen is in the end
32:32magte-take ulit sila ng another exam.
32:34Okay? Yung ABM ico-compare natin yung
32:37change ng scores nila doon sa scores ng
32:40mga STEM student. And titingnan natin
32:42sino yung may mas mataas na gain. And
32:44titingnan natin if there is significant
32:46on that change. Okay? So that is test
32:50independent sample. Okay. Dalawang grupo
32:53na considered to be defined. Okay. Just
32:57like um in terms of alcohol alcohol
33:01consumption drinkers and nondrinkers
33:04just like that no? So we are going to
33:06define the two the one group. Okay.
33:10So this one sabi ko nga merong
33:12involvement of time. So the past and the
33:14present scores.
33:17Okay. Proceed tayo kay analysis of
33:19variance. So anong meron pag sinabi
33:21nating analysis of variance? Ito iyung
33:24mas kilala. First one, they are about to
33:26compare the data in a parametric data.
33:31And how many samples do we have in
33:33analysis of data? In analysis of data,
33:36it is independent sample. But then this
33:39time we are going to deal with two or
33:42more sample. Okay, independent sample
33:45with two or more groups. Okay, we are
33:48going to define two or more groups.
33:50Okay, it is just like how Test
33:53independent sample works. Parehas sila
33:56but then this time, mas marami lang kasi
33:58yung kaya niyang i-compare. Okay? That
34:00is analysis of variance. And we have two
34:04types of analysis of variance. We have
34:07one way ANOVA and we have two way ANOVA.
34:11What would be the difference no in terms
34:13of one way anob we have one group that
34:17is being defined by means of for example
34:20in terms of BMI nila overweight
34:23underweight okay and so on and so forth
34:25non and they are going to compare with
34:27one data okay for example ah yun nga ah
34:32about their height okay kung naapektuhan
34:35ba sila yyung BMI nila
34:38about for example like their lifestyle.
34:42Okay, in terms of their lifestyle, no,
34:43we're going to connect on that. So,
34:45we're going to use one way ANOVA kasi
34:47isa lang yung test na binibigay natin
34:49doon sa tatlong group, okay? Or three or
34:51more groups. Okay. And how about Twoway
34:55ANOVA? So, pagdating kay Two ANOVA, we
34:58have two or more groups na kino-compare
35:01which is considered to be defined. Okay?
35:03Two more groups na considered to be
35:05defined. What does it mean po? For
35:07example, kino-compare natin 'yung BMI.
35:10Okay.
35:12Kaya to. Kino-compare natin yung BMI ng
35:15groups natin. Okay. The first one is um
35:20alcohol consumption nonrinkers and
35:22drinkers. Co-compare natin kung
35:24naapektuhan ba yung BMI nila. The other
35:26one is uh smokers naman, non-smokers and
35:31smokers. No, Ico-compare din natin yung
35:33BMI nila and at the same time yung
35:35interaction nung dalawa. Ico-compare
35:37natin kung ikaw ba ay nag-iinom at ikaw
35:40din ay naninigarilyo. Maapektuhan 'yung
35:42BMI mo? Or kapag ikaw ay naninigarilyo
35:44lang, maapektuhan din ba 'yung BMI mo?
35:47Or kapag ikaw ay ah nainom ah nag-iinom
35:50lang talaga maapektuhan din ba yung BMI
35:52mo? So by that is two way ANOVA. Okay?
35:57So that is analysis of variants one way
36:00and two way ANOVA.
36:03Okay, let us now proceed with the next
36:05one. Pearon R correlation. So, punta na
36:08tayo in terms of getting the
36:09relationship. Okay, this time we're
36:11going to associate the data. And the
36:14next one is it is in a form of
36:17parametric data. Kailangan ratio and
36:19interval kapag gagamit tayo ng pearson r
36:23correlation. Okay.
36:26So, that will be our treatment.
36:29Let us start. The first one is
36:31correlation with uh and between the set
36:33of variables. No so we have going to
36:36have one group. Okay? Dalawang treatment
36:39or dalawang variable ang kino-compare
36:42natin just like kanina no? Yung scores
36:44ng bata and the other one is sleep
36:46deprivation. So titignan natin if ah
36:50paano naapektuhan or uh what happened to
36:53the given data kapag mataas yung scores
36:56ng mga bata. Okay. What happen kapag
36:59kaunti 'yung tulog ng bata? What will
37:00happen to the scores ng bata? So by that
37:03we're going to use pearson r
37:05correlation. Okay? For example naman din
37:08ah height and weight. Paano nakakaapekto
37:10'yung height doun sa given weight natin
37:12no'no. So kapag ba mas mataas 'yung
37:14height natin, mas mabigat tayo or kapag
37:16mas mababa iyung height natin, mas
37:18magaan tayo. So those are the things
37:20that we are going to have in terms of
37:22person R correlation. Sometimes in
37:25person R correlation um we could get uh
37:28the correlation of them but then there
37:31are now there are no relationship at
37:33all. Kaya nating kuhain yung correlation
37:36nila yung relationship nila in terms of
37:38kung tumaas ba or bumaba yung data
37:40natin. Okay? Kung parehas ba tumaas yung
37:43data natin kapag inapply natin yung
37:46dalawang iyon or yung isa ay bumaba.
37:48Okay. So, the first variable increases,
37:51the second variable decreases. So kung
37:54ganon yung connection nila or
37:55relationship nila, kahit wala silang ah
37:59connection sa isa't isa, we still can
38:02get the value. Okay? Ito yung pagkakaiba
38:04in terms of the other tests na gagamitin
38:08natin. Okay, pwede rin namang kuhain
38:10muna natin if there is such connection
38:12talaga bago natin hanapin yyung ah what
38:16type of correlation happened to them.
38:18Okay? Later, we still uh we still have
38:20how many time pa naman kaya pa no?
38:22Marami pang oras para ma-discuss in
38:25terms of how are we going to interpret
38:27the data. Okay? This one is person R
38:30correlation. We're just getting the
38:32relationship or correlation between a
38:34pair of variable na na mayro'n tayo.
38:38Okay? Any question? Ay sorry akala ko
38:40nasa klase ako. Okay. Proceed tayo sa
38:43next one.
38:46Okay. The next uh question ' ba aside
38:49from the treatment no we already done
38:51those treatments uh from parametric and
38:54non parametric side. So later tayo kay
38:57nonparametric. We're going to answer
38:59this question. How to perform
39:02inferential quantitative analysis. So to
39:05do this simply ano yung mga kailangan
39:07nating malaman? First one, how to use an
39:11SPSS. Okay? Ito ay malaking tulong no?
39:13SPSS baka naman joke lang. So aside from
39:16that, we need to know about P values.
39:20Okay? So what how are we going to
39:22interpret a P values? And next one we
39:25need to know about alternative and null
39:27hypothesis.
39:29And last one is we need to know about uh
39:32different types of correlation. So by
39:35knowing those things we can easily yes
39:38the word easily interpret the given data
39:41that we have. Okay? Based on dun sa mga
39:44treatment na binigay natin. Okay? So let
39:46us start.
39:48Let me ask you a question. No, how can
39:51you prove a given statement is true?
39:54Okay. Ano ba yung isang bagay na
39:57given a statement? Paano mo siya
39:58masasabi na siya ay totoo? Let me give
40:00you an example of the given statement.
40:03Alamin natin
40:06what if the statement is this one.
40:10Okay. How are going to prove na mahal ka
40:13talaga niya? Okay. This one is the
40:16really the hardest one na ma-prove no
40:19kung paano natin masasabi na mahal ka na
40:20talaga ng isang tao. Okay? So paano nga
40:23ba natin mapo-prove yan? Simply
40:26in in in a given inferential statistic
40:29way. Okay. Paano natin mapo-prove iyan?
40:32Kailangan nating i-prove na
40:35hindi ka niya mahal. O instead of
40:38proving it na mahal ka niya, ipo-prove
40:40natin siya ng hindi ka niya mahal.
40:44Bakit? Kasi the more the more na
40:45pino-prove natin is yung effort niya na
40:48na binibigay na nagpapatunay na mahal ka
40:50niya, paano kapag natigil yon? Paano
40:53pagdating sa future hindi niya na
40:54ginawa? Masasabi mo ba na mahal ka pa
40:56rin niya? Sakit no? So ngayon in
41:01inferential statistic way we are going
41:05to deal with the negative one. Okay.
41:08Kaya kaya kasi natin magbigay ng idea or
41:11factors para masabing hindi ka niya
41:13mahal. Halimbawa hindi ka binigyan ng
41:15chicken skin nung kumakain kayo. 'Di ba?
41:18O halimbawa hindi ka hinatid sa bahay
41:20niyo, okay? Hindi ka tinawagan
41:22gabi-gabi, tinulugan ka. So those are
41:25some examples na pwede nating ibigay no
41:27para masabing hindi ka niya mahal. Okay?
41:30So mas madaling mag-prove ng isang bagay
41:33in terms na gagawin natin siyang mali.
41:36Okay. So yun po. So let us connect this
41:39no on statistics.
41:42Okay? Yung hindi kita mahal that is what
41:45we called null hypothesis. Ang tawag
41:47natin doon ay null hypothesis. And yung
41:50pino-prove natin kung mahal ka talaga
41:52niya ang tawag natin doon ay alternative
41:56hypothesis. And this one is chances. No,
41:59hindi ibig sabihin na hindi ka binigyan
42:00ng chicken skin nung kumakain kayo hindi
42:03ka na agad niya mahal. There is always
42:06certain chances ba sin popoy ah anong
42:10pangalan non si popoy sa basya ba ilang
42:13chances yung binigay first chance second
42:16chance pero pagdating kay inferential
42:18ilang beses or ilang chances yung
42:20binibigay let us see. For example um
42:25patutunayan natin ang pinaka-goal natin
42:27dito is to prove na hindi ka niya mahal.
42:29Okay? Yun yung chances. What would be
42:31the chance na hindi ka talaga niya
42:33mahal? Okay, first uh first uh factor
42:37okay na napansin mo. Hindi ka binibigyan
42:39ng ah time. Okay? Hindi ka binibigyan ng
42:43time. So possible ba na masabi mo na
42:45mahal ka pa rin niya kahit hindi ka
42:47binibigyan ng time? Possible. Baka
42:50binubuo niya lang yung future niyo. Sana
42:53all. Okay no? So there is just 1% chance
42:56na hindi ka niya mahal. Mas malaki pa
42:58rin yung chance na mahal ka niya. Ilang
43:00percent? 99% pa. Okay? Eh ngayon nung
43:04kumain kayo hindi ka binigyan ng chicken
43:06skin. May galit talaga ako sa hindi
43:07nagbibigay ng chicken skin. Okay. So
43:10ngayon nadagdagan siya naging 2%. Okay?
43:142% yung chance na hindi ka niya mahal.
43:17So we still have 98% no? So pwede pa rin
43:19natin i-accept. Sige palag mahal pa ako
43:22niyan. Baka gutom lang talaga siya.
43:25Okay. Okay. Aside from that,
43:28ngayon, hindi ka hinatid pauwi. Okay?
43:32Hindi ka hinatid pauwi. Tumaas 'yung
43:34chance na hindi ka niya mahal. 3% ilang
43:37percent na lang? 97% na lang para
43:40masabing mahal ka niya. Okay. Pero dito
43:43syempre baka naman may ah may emergency
43:45kaya hindi ka nahatid. Sige pagbigyan.
43:48Mahal pa ako niyan no. So sobrang
43:51magmahal no kahit anong nangyari no.
43:54Okay. The next one is ano pa ba? Ano pa
43:58ba yung mga reasons para masabing hindi
43:59ka niya mahal?
44:01Um,
44:06nagcha-chat ng ibang babae. Ayan. Naku,
44:09sasabi ko sa inyo talaga. So,
44:13ay sorry sorry. sa akin. Sinasabihan ako
44:16ditong judgmental ng mga kasama ko.
44:18Okay. So ngayon, what happened? 4% na
44:21yung chance na na hindi ka niya namahal
44:23kasi naghahanap na siya ng ibang
44:24atensyon eh. Oo. O sana walang
44:27nasasaktan ngayon no habang
44:28nagdi-discuss
44:30tayo ngayon. So 4% na pero still accept
44:34pa rin natin. Baka naman gusto niya lang
44:36ano gusto niya lang maglibang.
44:38Sobrang ano no, gusto niya lang talaga
44:41maglibang. Okay. The next one. Bigay pa
44:45tayo ng isang 'no. Isa pang dahilan para
44:48masabing hindi ka niya mahal. Uh hindi
44:52ka niloadan nung isang araw. Okay?
44:54Kailangan na kailangan mo 'yung load
44:55pero hindi ka binigyan ng pan-load. O
44:57for example no? So 5% na 'yung chance.
45:00Pero syempre accept mo pa rin.
45:03Napakasimpleng bagay lang non. Kaya ko
45:05na magpa-load next time. Next time ako
45:07na lang magpapa-load sa sarili ko. Okay?
45:09So 'yun no. So we still prove na kaya pa
45:13rin. Okay? But then paano kapag lumagpas
45:16ng 5%? What will happen? Okay then na
45:19'yung papasok na um
45:24aside from may kausap na iba is
45:26kinakausap niya na ano kapuyatan niya na
45:28no ka-ML niya na. Okay so kapag ganyan
45:316%
45:33X na 'yan. Kapag ano, mas marami pa
45:35'yung time niya dun sa isa kes sa'yo. O
45:38kapag ganyan, pwede na nating i-accept.
45:41Okay? Hindi mo na kailangan ng kalahati.
45:44Hindi na kailangan umabot ng 50% bago mo
45:46i-accept na hindi ka niya namahal. 5%
45:49lang ang kaya mong ibigay sa kanya.
45:51Kapag lumagpas doon, accept mo na hindi
45:54ka talaga niya mahal. Okay eh. Nag-cheat
45:57pa lalo
46:02and nagkaroon pa kayo ng pagtatalo pero
46:05hindi ka na pinansin talaga. Okay? Wala
46:07ng pakialam sao totally. So naging 98%
46:10yung chance na hindi ka mahal this time
46:12totally accept na accept mo na dapat yan
46:15na hindi ka na niya talaga mahal. So
46:16that is how statistic works in terms of
46:19love. Bakit? Kasi February na. Baka
46:21naman. Okay, proceed tayo.
46:26So let us now proceed on how it is
46:28connected in terms of ano no statistics
46:31or inferential statistics. That 5% that
46:33I'm telling you is what we called the P
46:36value. Okay? Yan iyung standard P value
46:38natin. Okay?
46:41So ang goal natin in every researches
46:43specifically in inferential
46:46when to reject the null hypothesises. So
46:49yun yung titignan natin. kailan natin
46:51pwedeng i-reject iyung null hypothesis
46:53and and it is just 5% chance. Okay?
46:58So on this part, if it is greater than
47:015% or greater than 0.05
47:04ibig sabihin non kailangan mo ng
47:06i-accept na hindi ka niya mahal or on
47:07this part kailangan mo ng i-accept ang
47:10null hypothesis. Okay? Ang pipiliin mo
47:13ngayon d sa dalawa, alternative or null,
47:15ang pipiliin mo will be yung null
47:17hypothesis.
47:19Okay. Now, in statistical way we are
47:23dealing with failed to reject the given
47:26null hypothesis. Ito yung nakikita niyo
47:28doon. Okay? Ito yung term na ginagamit
47:30natin for statistical way. Hindi niyo
47:32pwedeng ilagay doon yung mahal ka niya,
47:34hindi ka niya mahal. Hindi niyo ilalagay
47:35niyo. Ang ilalagay niyo will be failed
47:37to reject the null hypothesis. Okay? How
47:41about kapag less than or equal to 0.05?
47:4405. So dito you can now ah you still
47:49have to reject the null hypothesis kasi
47:51less than pa siya ng 5%. So this time
47:54you're going to reject the null
47:56hypothesis and accept the alternative
48:00hypothesis. Okay. So that is um how are
48:04we going to interpret a given uh P value
48:08na meron tayo. And usually ito yung
48:11ginagamit nating standard on how we are
48:13going to interpret the given data. Let
48:16us proceed no. Let us now try to analyze
48:20a given data in terms of comparison by
48:23getting significant difference. Okay. So
48:25ano 'yung mga kailangan natin do? Of
48:27course the P value. Okay. In getting the
48:30P value, sabi natin sa null hypothesis
48:33natin if it is less than 0.05. And sabi
48:36ko rin kanina kapag magpo-prove tayo
48:38instead of proving it na tama ipo-prove
48:41natin siya na mali. On this part instead
48:44of proving it na significant difference
48:46or there is a significant difference
48:48ipo-prove natin siya ng mali. So we are
48:51going to consider the null hypothesis as
48:53is there a uh is there is no statistical
48:57difference at all. So 'yun yung ilalagay
48:59natin for null hypothesis. Okay? And for
49:02the alternative hypothesis of course
49:05with difference with statistical
49:07significant difference. Okay? So ito
49:10yyung basis natin. Kapag no difference
49:13greater than dapat siya ng 0.05 05 ang P
49:15value and kapag with difference less
49:18than or equal dapat siya ng 0.05.
49:22And before we proceed on getting this,
49:25okay? Or before we proceed on on finding
49:28the treatment or or on dealing with the
49:32data, kailangan may dalawa muna tayong
49:34gawin. Okay? Dalawang criteria bago tayo
49:36mag-proceed on the specific treatment na
49:39gagawin natin. First one, we need to
49:41test the normality of the given data.
49:44And and next one is we need to to test
49:47the homogeneity of the given data. So
49:50what are those two? First let us have
49:52the test of normality. So kapag sinabi
49:55nating in terms of test of normality we
49:57need to find out if it is normally
49:59distributed ang given sample. So paano
50:02natin madi-distinguish if it is normally
50:04distributed? So in testing the
50:07normality, ano ano ba talaga yung
50:08ginagawa natin dito? Simply we are just
50:12comparing the normal distribution ng
50:14given population na meron tayo to the
50:17given sample na kinuha niyo. Remember
50:20hindi tayo kumuha ng buong sample ah ng
50:22buong population. Kumuha lang tayo ng
50:24part of the given population. And we are
50:27going to find out equal pa rin ba silang
50:30dalawa. Kahit na kumuha lang tayo ng
50:32sample, still normally distributed pa
50:35rin ba siya? Okay. So yun yung titignan
50:37natin. And to find that again we're
50:40going to use the P value. Okay? So this
50:43time kapag less than 0.05
50:45greater than 0.05 there is no difference
50:49between sample and population. And kapag
50:51naman less than or greater less than or
50:55equal to 0. 05 it means that there is
50:59significant difference between sample
51:01and population normal distribution. So
51:04ano yung gagawin natin dito? To find out
51:06if there is a normal distribution,
51:09kailangan syempre there is no difference
51:11between them. So ibig sabihin to say it
51:14is normally distributed ang p value
51:17dapat natin will be greater than 0.05.
51:22Okay. And masasabi natin it is nonnmal
51:26distributed if it is less than or equal
51:29to 0.05. And there is a specific test or
51:34treatment ang ginagamit natin for test
51:35of normality. Wala doun sa mga nabanggit
51:37ko, this one is part of the statistician
51:40na siya na ang bahalang magbigay to find
51:42out if it is normally distributed or not
51:45before to proceed on the given
51:47treatment. Okay,
51:49that is for the test of normality. Now,
51:51let us proceed with the test of
51:53homogeneity. Kanina sa normality, bakit
51:55kailangan normally distributed siya?
51:58Simply, uh we need to find out if the
52:00scores that we gave or that we have na
52:03nakuha is tumatama on a given mean. Ibig
52:06sabihin almost nagkakasundo sila lahat
52:10na ganun yung result. Okay. Kapag gann
52:13uh ibig sabihin maganda 'yung data na
52:15nakuha natin. And in terms of test of
52:17homogeneity we are dealing with those
52:20groups. Sabi natin we are comparing
52:22different groups. Kapag sinabi nating um
52:25test independent, Test dependent, right?
52:28So on this part, we are going to compare
52:31those two groups na tinitingnan natin,
52:33okay? Before we proceed on the treatment
52:36and kailangan homogeneous sila in
52:38nature. Kailangan wala silang masyadong
52:41pagkakaiba aside doon sa dinefine mo na
52:44pagkakaiba. What does it mean? For
52:46example, male and female. Okay? So,
52:49dinefine natin siya in terms of male and
52:50female pero we can consider them
52:53homogeneous. Ibig sabihin dapat walang
52:55other aspect na magkaiba sila only for
52:58the gender, male and female. Yun yung
53:00titingan natin for the test of
53:02homogeneity. And again, anong gagamitin
53:05natin dito? We are going to use the P
53:08value. And if it is greater than 0.05,
53:11there is no difference. Less than or
53:13equal to 0.05 there is a difference. And
53:17since ang kailangan natin equal silang
53:19dalawa, there should be no difference at
53:21all. So therefore to consider it is
53:24homogeneous ang data natin, kailangan
53:26greater than 0.05.
53:30And kapag less than or less than or
53:33equal to 0.05 05 ang P value natin yung
53:36test in terms of homogeneity natin
53:40heterogeneous ang data na meron tayo.
53:42Kalatkalat yung data na meron tayo.
53:45Okay? Or kalatkalat yung give different
53:47group or hindi talaga sila ah related sa
53:50isa't isa or there are lots of um
53:54lots of differences between them no?
53:57Kapag ang kinuha natin will be less than
54:00or equal to 0.05. kapag ang result natin
54:03is less than or equal to 0.05.
54:06Okay? Let us some of things non? Ano
54:08iyung mga gagamitin nating treatment
54:10based on the test of normality and test
54:13of homogeneity? First, we need to look
54:16for the test of normality. Sabi natin,
54:18alamin natin if it is normally
54:20distributed or non-normal distribution
54:23ang meron tayo. Kapag ah greater than 5
54:26ang P value, it is normal. Kapag less
54:28than or equal to 5, nonnormal ang
54:31distribution natin. Okay? And kapag
54:33nonmal, magpo-proceed agad tayo doun sa
54:36mga nonparametric test na meron tayo
54:38doun sa ating summary na binigay. Okay?
54:41And for the normal, kapag normal ang
54:43distribution natin, proceed tayo agad
54:45kay test of homogeneity. Okay? Sa test
54:48of homogeneity, hinahanap natin if it is
54:51homogeneous or heterogeneous. Kapag
54:54homogeneous ang data natin, ang P value
54:56is greater than 5. Kapag heterogenous
54:59less than or equal to 5. So ngayon kapag
55:04homogeneous ang data natin, pwede na
55:06nating gamitin yung mga parametric test
55:08na meron tayo na diniscuss ko kanina.
55:11Pero kapag hindi siya homogeneous ang
55:14data natin, we have this one option by
55:17means of using brown foright. Okay? So
55:20ito yung pwede nating magamit for
55:22heterogenous. And again in terms of
55:25computing those parametric test,
55:27non-parametric and brown force height,
55:29hindi na natin muna siya idi-discuss for
55:32this uh for this part no' part of the
55:35SPSS na. saka na natin siya idi-discuss
55:38if there will be enough ano 'no uh if
55:42there is a chance na makapag-discuss
55:44ulit tayo dito. Okay? So now ah in terms
55:48of ano idea na meron tayo, sabi ko
55:52kanina ah if the test of normality and
55:56homogeneity did not satisfied, if hindi
55:59natin na-satisfy yun, pwede nating
56:01gamitin yyung mga nonparametric test.
56:03Okay. In terms of normality no? So
56:06instead instead of using one sample Test
56:09we can use the Wilcoson signed rank
56:11test. Okay? Instead of using Test
56:15dependent sample pwede rin nating
56:17gamitin si Wilcoson. Okay? And instead
56:20of using Test independent sample, kapag
56:23hindi normal ang data, gamitan natin ng
56:25manwhtney. Okay? And kapag naman
56:29analysis of variance at hindi normal ang
56:31data, gamitan natin ng crosscal valleys.
56:34Okay? So that is our ano no, our way or
56:39tips sa pagkuha ng given treatment.
56:43Okay? So proceed tayo. Let us have this
56:46a given example, no'. Researcher aims to
56:49determine the comparison of academic
56:51performance of STEM students in
56:53mathematics and science subject. So dito
56:56uh we need to identify what should be
56:58the correct research question we have
57:01here. Okay? Since we need to find out
57:03the comparison of the academic
57:05performance ng STEM students and science
57:08uh in terms of mathematics and science
57:10subject, of course we need to get this
57:13question. No. Is there a significant
57:15difference between the performance of
57:17the STEM students in mathematics and
57:20science subject? So ico-compare natin
57:23iyung dalawa. Now, what will be our
57:27treatment to be used in terms of the
57:29research objective and the research
57:31question? So here we are going to
57:34compare. Since score ang pagmumulan
57:37niya, it could be grades or it could be
57:39the score of the exam or the final exam.
57:42It means that it is parametric ang test
57:44natin. And of course, ilan 'yung
57:47pinaka-group na mayro'n tayo dito?
57:50Okay, we have one group which is stem
57:52students. Okay? Containing two things na
57:56ide-describe natin sa kanya. Two
57:57variables na kinukuha natin doon sa
58:00isang group which is the STEM group. No?
58:02So by that we are dealing with two
58:04samples pa rin pero dependent sample. So
58:08ano na yung mismong statistical
58:10treatment na pwede nating gamitin? So on
58:12this part, we can use test dependent
58:16sample. Kasi kanino ba nanggaling yung
58:18data? Sa kanya lang. Okay? Sa iisang
58:20group lang which is the STEM students.
58:23Okay?
58:25And by that we can have now the
58:27hypothesis no? So there is no
58:29significant difference between math and
58:31signs. Sabi natin kailangan laging
58:33negative ang null hypothesis. And there
58:35is significant difference between math
58:37and science scores. Okay, that would be
58:40our hypothesis on this part no and let
58:43us assume for example for this ano no
58:45for this analysation the result of the P
58:48value contains 0.023.
58:51Now we are going to find out if we are
58:53going to reject the null hypothesis or
58:56we're just going to uh fail to reject
58:59the given hypothesis. In other words,
59:02we're going to accept the null
59:03hypothesis. So ano yung boundary natin?
59:06Negative less than or equal to 0.05.
59:10Okay? Yung 0.05 to accept the
59:12alternative hypothesis. And since si
59:150.023
59:17is less than or equal to 0.05. If it is
59:20less than we are going to reject the
59:23null hypothesis it means that there is
59:26significant difference between math and
59:29science scores okay so kung ano yung
59:32nakuha what would be the implication of
59:34this ibig sabihin kung ano yung nakuha
59:36nating values from the scores ng math
59:40and science nagma-matter yung subject
59:42system ibig sabihin baka nga system mas
59:45magaling siya sa signs kung mas mataas
59:47yung scores niya doon or mas magaling
59:49siya sa math kung mas mataas yung score
59:50niya sa math. Okay? By that uh we use
59:53the t dependent sample to interpret the
59:56data. Okay, that is the first one. Okay,
59:59let us have the next example.
1:00:02Suppose that we are uh the researcher
1:00:04aims to determine if the average height
1:00:09of Filipino male in emus reach the
1:00:12standard average height of Filipino male
1:00:14which is 160
1:00:16cm. So how are we going to deal with
1:00:19this? So ang ating magiging uh research
1:00:22question, possible research question
1:00:24will be is there a significant
1:00:26difference between the average height of
1:00:29Filipino male in IMUS and standard
1:00:32average height of the Filipino male in
1:00:35the Philippines? We are going to compare
1:00:37the standard height in the Philippines
1:00:39and iyung naging standard height ngus.
1:00:42Okay, we are going to compare the two.
1:00:44So what will be our statistical
1:00:47treatment applied on this? First since
1:00:49we are going to compare the next one
1:00:52height ang pinag-uusapan it is
1:00:54parametric data. So ngayon tingnan natin
1:00:57how many samples do we have. So on this
1:01:00part we are just going to get one
1:01:01sample. Ako kukuhaan nila ng height. The
1:01:04other person kukuhaan nila ng height.
1:01:06They are going to get the average.
1:01:08Kanino nila ico-compare? Sa standard.
1:01:10And kapag sa standard nila kino-compare,
1:01:13sa standard value there is an expected
1:01:15value to be compared, we are going to
1:01:18use one sample Test. Okay? And let us
1:01:23now formulate the given hypothesis.
1:01:26So uh the hypothesis will be there is no
1:01:29significant difference between the
1:01:31average height and the expected value.
1:01:34And the alternative hypothesis will be
1:01:36there is significant difference.
1:01:38paulit-ulit lang talaga 'yung
1:01:39pinaka-process niya no' and let us
1:01:42assume for example that the p value is
1:01:440.086
1:01:46so what will be its ano no
1:01:49interpretation the first one since it is
1:01:52greater than 0.05 05 we failed to reject
1:01:56the given null hypothesis. Ibig sabihin
1:02:00there is no significant difference
1:02:02between the average height of Filipino
1:02:04male inus and expected value. Walang
1:02:09pagbabago. And by that what would be the
1:02:12implication? It means that nasa standard
1:02:15height pa rin ang imus. Hindi pa rin
1:02:17sila uh ibig sabihin almost nasa 163 cm
1:02:22pa rin ang most of the Imus resident.
1:02:26Okay. Filipino IMUS resident natin.
1:02:29Okay? So that would be the implication
1:02:31of the given interpretation. Tatandaan
1:02:34hindi natatapos sa interpretation ng
1:02:36data natin. We need to create conclusion
1:02:39based on the given interpretations that
1:02:41we have. Okay? That is the uh uh
1:02:45research. Ang pinakadulo natin
1:02:47conclusion dapat. So you need to analyze
1:02:50things about don sa data na nakuha niyo.
1:02:52Okay? Next. Research aims to determine
1:02:56the comparison between the math anxiety
1:03:00level of students with growth and fixed
1:03:02mindset. So we are going to determine
1:03:04the comparison between the growth
1:03:06mindset uh growth and fixed mindset in
1:03:09terms of the mats anxiety level. So what
1:03:12would be our research question? Is there
1:03:14a significant difference between the
1:03:16math anxiety level of the student of
1:03:20with growth mindset and the student with
1:03:22fixed mindset? So what will be our
1:03:26uh statistical treatment to be used? It
1:03:29is compare in comparing. And since
1:03:31growth mindset and fixed mindset to,
1:03:33there is some ah scaled ano no test na
1:03:36pwedeng gamitin dito na kung saan scores
1:03:39ang makukuha natin to determine if it
1:03:42has a given growth mindset or fixed
1:03:45mindset. By that we can say it is
1:03:47parametric pa din. Okay? And aside from
1:03:50that, how many samples do we have? So on
1:03:52this part iyung isang grupo natin hinati
1:03:54natin sa dalawa. the first one iyung
1:03:57merong fixed mindset and the other group
1:03:59is iyung merong growth mindset and we
1:04:02are going to compare their math anxiety
1:04:04level. So by that we are going to use
1:04:07ttest independent sample. So let us now
1:04:11proceed with formulating hypothesis. So
1:04:13for the uh for the null hypothesis there
1:04:16is no significant difference of the mat
1:04:18anxiety between the given growth mindset
1:04:20people and the fixed mindset people. And
1:04:22also in terms of alternative hypothesis
1:04:26there is a significant difference naman.
1:04:28Okay? So let us assume that the result
1:04:31is 0.035.
1:04:34So what will be our interpretation from
1:04:37this? So it is less than or uh less than
1:04:41or equal to 0.05.
1:04:43So therefore we are going to reject the
1:04:46null hypothesis. So we can say that
1:04:48there is significant difference between
1:04:51the math anxiety level of students with
1:04:53growth mindset and fixed mindset. So
1:04:56what will be our implication? So it
1:04:58means that
1:05:00our mind our mindset either growth
1:05:03mindset or fixed mindset really affects
1:05:06our mat anxiety. So nakakapagbigay siya
1:05:09ng matxiety ah or ah it really have a uh
1:05:15in terms of the data that we have no uh
1:05:17we try to find out if there is an
1:05:19effectung mismong mindset natin dun sa
1:05:21nagiging mat anxiety level natin. So we
1:05:24can say that kapag growth mindset ka,
1:05:26mas mababa iyung mat anxiety level mo
1:05:28compare kapag ikaw ay fixed mindset and
1:05:31so on and so forth. Okay? So this is
1:05:33just ano lang no? Assumed data lang tayo
1:05:36for us to have a computation. Okay.
1:05:40So let us have the next one association
1:05:43in terms uh let us now try to analyze uh
1:05:46relationships. Okay. So how are we going
1:05:48to deal with this? The first one of
1:05:50course we are going to use the P value.
1:05:53This time instead of looking for the no
1:05:55difference and with difference we are
1:05:57going to deal with no relationship and
1:06:00with relationship. 'Yung dalawang
1:06:02variable na tinitignan natin, may
1:06:04relasyon ba sila or wala? There is a
1:06:06connection or there is no connection at
1:06:09all. So syempre kapag greater than 0.05
1:06:12no relationship. Kapag less than or
1:06:15equal to 0.05
1:06:17with relationship.
1:06:19Okay. So ano yung how are we going to
1:06:21determine what type of correlation or
1:06:23what type of relations do they have? So
1:06:26we have three no negative correlation,
1:06:29no correlation at all and positive
1:06:32correlation. So what would be the
1:06:34difference between them? So kapag
1:06:35negative correlation
1:06:38it can be considered to be weak,
1:06:40moderate or strong negative correlation.
1:06:43How about in positive we can consider it
1:06:45to be weak, moderate and strong positive
1:06:47correlation. And there is a scale
1:06:49between them, no? So we can say that
1:06:52between kapag ang nakuha mong R values,
1:06:54kasi pagdating kay person R, ang
1:06:56ginagamit natin will be the R value.
1:06:59Kapag in between siya ng -0.1
1:07:02and 0.1, there's no correlation at all.
1:07:05Okay? Kapag naman in between ng 0.1 and
1:07:081, posi 1, of course there is a positive
1:07:11correlation. Depends if it is weak,
1:07:13moderate and strong. Kapag weak ang
1:07:15correlation, ibig sabihin hindi gaanong
1:07:17mataas 'yung connection nila sa isa't
1:07:19isa. Okay? Kumbaga ikaw sumakay ka sa
1:07:21jeep, okay? Ang connection niyo mababa
1:07:24in terms of doun sa driver saka ikaw
1:07:26kasi hindi naman kayo totally
1:07:27magkakilala. Sumay ka lang sa jeep niya.
1:07:29Okay? So just like that, no? Weak lang
1:07:31yung connection niyong dalawa. And kapag
1:07:34strong dito na yung papasok for example
1:07:36connection mo between parents. Okay? Mas
1:07:38mataas yung connection kumpara doun sa
1:07:40driver. Okay, that's just ano lang no um
1:07:46uh representation lang on how to how to
1:07:49give the idea of weak correlation and
1:07:51strong correlation. Okay? And in terms
1:07:54of negative naman negative weak
1:07:58in terms of negative naman kapag umabot
1:07:59tayo ng -0.1 1 and 1. In between them,
1:08:03negative correlation ang meron tayo. So,
1:08:06what does it mean in terms of positive
1:08:08correlation? So, kapag positive
1:08:10correlation, ibig sabihin yung isang
1:08:12variable, kapag tumaas yung isang
1:08:14variable, tumataas din yung pangalawang
1:08:16variable. Same goes kapag bumaba yung
1:08:19pangalawang yung unang variable,
1:08:21bumababa din yung ah pangalawang
1:08:23variable natin. There is a direct
1:08:25relationship between them. Okay, that is
1:08:28positive correlation. Kapag negative
1:08:30naman, uh, there is an alternate no'no
1:08:33inverse relationship. So kapag tumaas
1:08:35'yung unang variable natin, 'yung
1:08:37pangalawang variable bababa or
1:08:39naapektuhan siya pababa. Okay? Kapag
1:08:42naman mababa ung unang variable natin,
1:08:44tataas naman yung pangalawang variable.
1:08:46So by that there is a negative
1:08:48relationship between them or negative
1:08:51correlation between them. Ibig sabihin
1:08:54kung titingnan natin siya in a given
1:08:56data yung relationship nila, it could be
1:09:00mean na
1:09:03negative ang relationship na meron sila.
1:09:05Ibig sabihin naapektuhan siya pababa.
1:09:07Okay? There is chance. Okay? There is a
1:09:09chance na naapektuhan siya pababa. And
1:09:12there is a chance na naapektuhan din
1:09:13siya pataas kapag naman positive ang
1:09:15correlation. And kapag no correlation at
1:09:18all at all at all, it means that overall
1:09:21wala talaga silang connection sa isa't
1:09:23isa. There is no relationship at all.
1:09:25That is in the in between of -0.1
1:09:29and 0.1.
1:09:32Okay.
1:09:34So that is how are we going to deal with
1:09:36analyzing relationships between
1:09:39variables. So let us try po no. The
1:09:41research aims to determine the
1:09:44relationship between emotional quotient
1:09:47and social quotient. So dito tinitingnan
1:09:50natin paano ba kapag tumaas yung
1:09:52emotional quotient natin, ano ang
1:09:54nangyayari sa social quotient na meron
1:09:56tayo? So tinitignan natin yung
1:09:58relationship nilang dalawa. Okay? If
1:10:01there is any, no? We have two questions
1:10:03lagi pagdating kay association or
1:10:06relationship. Kapag pinag-uusapan ung
1:10:07relationship, first one, is there a
1:10:09linear relationship between emotional
1:10:12quotient and social quotient? Again,
1:10:14dito nga pala ang di-discuss lang muna
1:10:16natin is linear linear relationship muna
1:10:19o yung pinakamadaling method natin.
1:10:21Okay? And the other one, what type of
1:10:24relationship between emotional quotient
1:10:26and social quotient we have? Okay. So
1:10:29anong gagamitin natin dito? First one,
1:10:31we are trying to associate the data. So
1:10:33therefore we are going to use either
1:10:35pearon r kai square or spearman row and
1:10:39since we are dealing with emotional
1:10:40quotient and social quotient there is
1:10:42specific scores for that no so therefore
1:10:45it is a parametric study or parametric
1:10:48data rather so therefore we are going to
1:10:50use pearon r correlation okay so in
1:10:54using the pearson r correlation let us
1:10:56have now our hypothesis yung mabibigyan
1:10:59lang natin ng hypothesis will be yung
1:11:01ating question Number one, if there is a
1:11:05linear relationship between those two
1:11:07variables. So what will be our null and
1:11:10uh null and alternative hypothesis? So
1:11:13iyung null natin there is no significant
1:11:15relationship instead of difference. This
1:11:17time there is no significant
1:11:19relationship between emotional quotient
1:11:22and social quotient. Okay? And the
1:11:24alternative naman will be there is
1:11:26significant relationship. So let us
1:11:28assume that we have this values. The P
1:11:30value is 0.035. 035 and the R or the
1:11:34correlation value is 0.57. What does it
1:11:38conclude? First one, since it is less
1:11:41than 0.05 ang data natin, we need to
1:11:44reject the given null hypothesises. So,
1:11:46we can say that there is significant
1:11:48relationship. Okay, may connection iyung
1:11:51emotional quotient and social quotient.
1:11:54Okay. The next one is let us now try to
1:11:57analyze the given R value. Okay. Since
1:12:00it is in between of 0.1 and 1, therefore
1:12:04it is a positive correlation. So si 0.57
1:12:08hindi siya negative. And aside from
1:12:10that, it is uh in between of 0.5 and 0.8
1:12:16which is in terms of moderate. So we can
1:12:20say that this one is moderate positive
1:12:24correlation. Okay? So somehow there is a
1:12:27connection between
1:12:30emotional quotient and social quotient.
1:12:33Okay, that is how are we going to
1:12:35interpret a given relationship between
1:12:38datas. Okay, so let us now try to
1:12:42summarize things. No, so we try to find
1:12:46out the given treatment by means of
1:12:48answering what are you going to do with
1:12:50the data? What type of data do you have
1:12:52and how many samples do you have? And
1:12:54also we tried to find out the
1:12:56interpretation of the given P value and
1:12:59the interpretation of the given
1:13:01correlation. Usually we are trying to
1:13:03find or use the given P value in
1:13:05everything no? Somehow lagi natin siyang
1:13:07ginagamit.
1:13:09Okay? So that would be the summary of
1:13:11the all the discussions we have today
1:13:14no? And before end
1:13:18some important idea na kailangan alam
1:13:20natin as a researcher kasi ito yung
1:13:23madalas nilang nakakalimutan or dito
1:13:26sila nagkakamali kaya nauulit yung paper
1:13:28nila. So what are those? The first one
1:13:31is the bivariate pearon correlation only
1:13:35reveals association. Yung mga
1:13:37correlational study uh treatment natin,
1:13:40it just serve association of data. It
1:13:43doesn't mean that there is a causation
1:13:45between data. So what does it mean?
1:13:47Kapag gumamit tayo ng correlation hindi
1:13:49ibig sabihin yyung isang variable
1:13:52naaapektuhan niya yyung pangalawang
1:13:53variable. We are just getting the
1:13:55relationship between them. There is just
1:13:58a relationship pero hindi ibig sabihin
1:14:00naaapektuhan ka niya. Just like on my
1:14:02example no about sa ah for example ako
1:14:06and yung ah driver. Okay? There is a
1:14:09connection between them kapag sumakay
1:14:11ako sa jeep niya. But then kung hindi
1:14:13ako sasakay sa kanya ah sa kanya sa jeep
1:14:15niya what will happen is there is no
1:14:18connection at all. No but then we can
1:14:20say na hindi ako naapektuhan sa kanya.
1:14:22Okay. Hindi ako naapektuhan don sa
1:14:27pagsakay ko don sa jeep niya. Okay. So
1:14:29therefore, uh there is a connection
1:14:32between us doon pero hindi niya ako
1:14:35masya or hindi niya ako naapektuhan at
1:14:37all. So it ah kapag gumamit tayo ng
1:14:39correlation study, tatandaan hindi
1:14:42causation ang hinahanap natin. Yung mga
1:14:44study about effects, the effects of this
1:14:46and this and that, huwag niyong
1:14:48gagamitan ng correlational study. Parang
1:14:51awa niyo na. Okay? Kasi kayo rin ang
1:14:54alam niyo na mahihirapan sa dulo. So
1:14:56sino ba ang gina Paano ba? Saan ba
1:14:58gagamitin si Cation? Paano sir kapag
1:15:00gann 'yung ano natin study natin. Okay
1:15:02ulitin niyo na lang ulit. Joke lang. So
1:15:04anong gagawin natin? Simply ang
1:15:06gagamitin nating treatment will be
1:15:08comparative statistical analysis.
1:15:10Ano-ano iyung mga comparative? Yung mga
1:15:12diniscuss natin. Test dependent, Test
1:15:15independent, one sample Test. And
1:15:17kailangan para mas mataas iyung
1:15:19causation effect, kailangan laging may
1:15:22control variable. Okay? Experimental
1:15:25tayo pagdating dito. Okay? Remember that
1:15:28causation comparatives analysis tayo.
1:15:31Kapag naman ah association lang,
1:15:34correlation study tayo. Okay?
1:15:37Now, the greater the sample, the greater
1:15:39the chance that sample contains normal
1:15:42distribution. para mas makasigurado tayo
1:15:45na mas mataas 'yung normally uh kung
1:15:47normally distributed ba 'yung sample
1:15:49natin, kailangan mas mataas din 'yung
1:15:51ating ah number ng sample natin. Kasi
1:15:53kapag konti lang 'yung kinuha nating
1:15:55sample from the given population,
1:15:58particularly for example million yung
1:15:59population mo, kumuha ka lang ng 10
1:16:01hindi mo kayang makuha if it is normally
1:16:03distributed. Lalabas doon baka hindi
1:16:05siya normally distributed. Kung mas
1:16:07marami iyung sample na kukuhain mo on
1:16:10the given population, mas tumataas iyung
1:16:13normal ah yyung chance na normally
1:16:15distributed siya. Okay? Doon sa mismong
1:16:18data kasi kapag hindi maraming ka pang
1:16:20kailangan gawin kapag hindi siya
1:16:21normally distributed. Kaya umpisa pa
1:16:23lang bago ka mag-gather ng data,
1:16:25kailangan mas marami or enough na yung
1:16:28number ng respondents na meron ka.
1:16:31Next,
1:16:33the goal of inferential statistic is to
1:16:36discover some properties or general
1:16:38pattern about large group by studying a
1:16:40small group. Remember, ang tiniting lagi
1:16:43natin dito will be the sample. Okay?
1:16:45Sample kino-connect natin siya from the
1:16:48given population. Kung ano yung nating
1:16:50nakuha sa sample, ina-assume natin na
1:16:52ganon din pagdating sa population. That
1:16:55is inferential statistics. Okay.
1:17:01Next. Ito. Isa rin sa mga ano nagiging
1:17:04problem. The before after design in one
1:17:06group does not include a control group.
1:17:08Ito 'yung sinasabi ko. There should
1:17:10always be a control group kapag
1:17:11nagco-compare tayo. No, on this part,
1:17:14for example, ako nag-take ako ng exam.
1:17:16Kinuha ko yung post test and preest
1:17:18course ko. So ngayon titingnan ko kung
1:17:20merong difference sa akin ah dun sa
1:17:22pinaka-data natin, no. there will be
1:17:24biases kasi wala tayong control group.
1:17:26Hindi ibig sabihin na naapektuhan ako ng
1:17:28intervention or hindi. Okay? Kaya be
1:17:31careful on using before and after
1:17:32design. Yung mga ah design na kung saan
1:17:35merong post test and preest or trial one
1:17:38and trial 2. Those are some of the ideas
1:17:41na somehow nagiging bias tayo kapag
1:17:43kumukuha tayo ng data. So be careful on
1:17:46using those things. Okay? Nagiging
1:17:48dehado tayo pagdating sa defense.
1:17:51Okay? So that would be all about the
1:17:53discussion in terms of inferential
1:17:55statistics. No and now that you know
1:17:58things about the treatment and you know
1:18:00things about how to interpret the given
1:18:03uh treatment that we have or the given
1:18:06result you can now do your research. And
1:18:08if you fail, remember that if the first
1:18:11if at first you don't succeed, try to
1:18:14two more times so that your failure is
1:18:17statistically significant. Ulit-ulitin
1:18:20lang natin hanggang sa maging
1:18:22statistically significant yung magiging
1:18:24failure niyo. So that would be all.
1:18:27Thank you po.
1:18:27Okay, let us now move forward for our
1:18:30first question that is from Mr. Vince
1:18:32Chamoro. How do we solve for ANOVA? This
1:18:36is the first question, Sir Jerome, how
1:18:38do we solve for anova?
1:18:43Mike test. Naririnig?
1:18:46Okay. So on solving ANOVA no that that
1:18:50is ano no we can use different uh
1:18:54application just like what I have said.
1:18:56No, we can use the SPSS and we can also
1:18:59use Excel for solving that one, no? And
1:19:03as well as there is some formulas naman
1:19:05regarding that and hindi siya munang
1:19:07tinackle natin. is because more on we
1:19:10are dealing with treatments muna on how
1:19:12to choose a given treatments and how to
1:19:14interpret those but then if there will
1:19:16be some chance na ano no baka makuha na
1:19:18or we can ano no try to have a detailed
1:19:24about that one no on how to find how to
1:19:27solve a given anov but at least we know
1:19:29how to interpret it mas better na yan
1:19:32thank you p
1:19:36okay thank you for that Answer Jerome,
1:19:38let us now move forward to our second
1:19:40question. I think this one is very
1:19:42interesting question. The question is,
1:19:44are studies and action research entitled
1:19:47effectiveness of stress management
1:19:49program to the teachers of UCC. Is it
1:19:53correct to use measurement of central
1:19:55tendency min in order to determine the
1:19:58stress level of the respondents before
1:20:00and after the implementation of the
1:20:02program and to use an independent test
1:20:05to determine if there is a significant
1:20:07difference between the two mean scores
1:20:10of the stress levels of respondence. If
1:20:13that is wrong, what should we use
1:20:15instead?
1:20:17Okay. No, so in terms of that,
1:20:21first thing, no, so ang dami palang
1:20:23tanong non. So, the first one is about
1:20:26the given mean kapag gagamitin natin is
1:20:29the mean. Yes, of course it depends pa
1:20:31rin naman. It depends on the given
1:20:32instrument that you have, no? Be careful
1:20:35with the instrument. sometimes sabi nga
1:20:36ni sir G while ago in terms of having
1:20:40this scale instrument no iyung strongly
1:20:42agree agree and disagree so it can be
1:20:45fall under ordinal data so therefore we
1:20:47can use median instead of using the mode
1:20:50ah instead of using the mean rather but
1:20:52then if your data gives us a given score
1:20:56of the stress level you can use the mean
1:20:58na agad-agad so that would be the best
1:21:00dat uh best central tendency that we can
1:21:03use and by that ah since it is in a form
1:21:08of parametric no sabi natin and you are
1:21:10going to compare uh since we are dealing
1:21:13with effect sabi natin causation to ito
1:21:15yyung tinutukoy natin na causation no so
1:21:18we can say that we can use the
1:21:20independent t test on this no um be
1:21:23careful lang sa paggamit ng independent
1:21:25t test test since we are dealing with
1:21:27teachers no all of the teachers here in
1:21:30UCC or some of the teachers here in UCC
1:21:33you need to split them into two.
1:21:35Okay, that would be the hardest one, no?
1:21:38Ah, in terms of independent Test,
1:21:40kailangan mo silang i-split into two.
1:21:43The other one have the controlled group
1:21:45and the other one has a experimental
1:21:47group. Yung control group is hindi natin
1:21:49siya bibigyan ng intervention or hindi
1:21:51natin siya bibigyan nung pinaka-program
1:21:54na pino-propose niyo. And the other one,
1:21:56the other group is mag-a-undergo ng
1:21:58ganong program. And if there will be
1:22:00some changes between them, therefore uh
1:22:02there will be some result or idea na
1:22:06possible na talagang effective yung
1:22:08paggamit. Ah if there is no if there is
1:22:10difference. Pero kapag walang difference
1:22:12sila, possible na hindi talaga siya
1:22:15naapektuhan at all or hindi effective
1:22:17'yung mismong ating approach or yung
1:22:19program mismo. So tama naman po na
1:22:21independent Test yung gagamitin natin.
1:22:24So wala naman akong naliban sa mga
1:22:26questions. If it is wrong, there's
1:22:28nothing wrong with what you said. Thank
1:22:30you.
1:22:33All right. Thank you, Sir Jerome, for
1:22:36raising that clarification. Let us move
1:22:38forward to our third question. This is
1:22:40from student of the laasal ashanti
1:22:44Naomi. When should we use Test and can
1:22:47you give a sample study that use Test?
1:22:51Okay, there's a lot no ah maraming
1:22:53pwedeng pagamitan si Test and usually
1:22:56sabi ko kanina kapag Test ang
1:22:58pinag-uusapan natin lahat ng diniscuss
1:23:00ko kanina Test independent, Test
1:23:02dependent, one sample Test. Those are
1:23:05parts of the uh inferential statistics
1:23:09under comparison. So if you are
1:23:12comparing the data, if there is a
1:23:14significant difference between them, we
1:23:17are using
1:23:19ah we are using that one no iyung Test
1:23:22na tinatawag natin overall those Test na
1:23:24meron tayo. And be careful lang din
1:23:26again, no. There will be some cases
1:23:28kapag one sample lang. Kapag two groups
1:23:30ang kailangan mo pero independent pero
1:23:32two groups ka pero dependent sample or
1:23:34three or more groups, kailangan specific
1:23:37tayo doon, no. Hindi laging ginagamit si
1:23:39TTES. Ah usually kapag sumobra ng
1:23:42dalawa, yun yung pinaka-clue natin doon.
1:23:44Kapag sumobra ng dalawa, yung group na
1:23:47kino-compare natin, automatically we are
1:23:50going to use ANOVA. Okay? So hindi na
1:23:52tayo magte-test on that part. Okay. In
1:23:54terms of the study, yung kaninang
1:23:57example na binigay ni ano no, the first
1:23:59question that we have uh that is one of
1:24:02the example for the Test. And aside from
1:24:04that, just a simple for example if
1:24:06you're going to compare the height and
1:24:08the weight of the given person that is
1:24:11also forming Test. Then we can use Test
1:24:13for that. Uh the scores of two different
1:24:16person uh such as kanina uh different
1:24:20strand for example TVL ah ABM, STEM ah
1:24:25performing arts. Huwag magagalit ha kung
1:24:27hindi mabanggit. Okay? So Yums, no? And
1:24:30the others as well no. Kapag kinuha
1:24:32natin yung scores nila in a specific
1:24:34subject for example general mathematics
1:24:36and we compare them no'no? So kapag
1:24:39sobrang dami na ANOVA pero kapag dalawa
1:24:41lang doon yung hinihingi natin for
1:24:43example Yumes and TVL lang muna so we
1:24:45can use Test okay particularly
1:24:48independent Test ayan po. Thank you.
1:24:56And we have more questions to go Sir
1:24:57Jerome let's proceed to the next one
1:24:59from Miss Michelle Aguilar. When it
1:25:02comes to getting number of samples, do
1:25:04we have a standard number of
1:25:06respondents?
1:25:08Okay. So, in terms of the having the
1:25:10standard, no? So,
1:25:13kapag standard na pinag-uusapan kasi
1:25:15natin, there is no. depends on the given
1:25:17populations, no? Um, there is a formula
1:25:21that we can use, no, iyung coach formula
1:25:23natin, but then um somehow no,
1:25:27the least okay, number of respondents
1:25:31that we could get. Pero kagaya nga ng
1:25:32sinabi ko, kung kaya or possible na mas
1:25:35damihan pa natin, okay? Mas damihan pa
1:25:38natin yung respondents natin para mas
1:25:41maging normally distributed yung data
1:25:43natin, gawin natin kung kaya pa nating
1:25:45kumuha ng data especially kapag sobrang
1:25:48taas ng population. Nakadepende lagi
1:25:50kasi siya sa dami ng population eh. If
1:25:52thousands lang naman 'yung populations
1:25:54natin, it's uh kung sinabi ni costron na
1:25:56nasa 200 lang, kahit gawin mo siyang 300
1:26:00para mas sigurado ka. 'Yung binibigay
1:26:02lang ng formula natin 'no? 'Yung
1:26:04pinaka-least possible or wala ng bababa
1:26:06dapat doun na number of respondents or
1:26:09else baka magkaroon tayo ng problema in
1:26:11terms of normal distribution. But then
1:26:14pwede naman ang mangyario, you can check
1:26:16the normal uh if it is normally
1:26:18distributed enough or and kung hindi pa,
1:26:21pwede niyo ng gawin is dagdagan pa lalo
1:26:23'yung respondents or dagdagan mo pa lalo
1:26:25'yung respondents if ever. So 'yun po.
1:26:35Yes, we have more questions. Okay, let's
1:26:38move forward. Thank you, Sir Jerome.
1:26:39Another one is from KTEN Jersey Dela
1:26:43Cruz. Our variables are the two study
1:26:46times, day and night in the students
1:26:49examinations course. We are wondering if
1:26:51it's okay to use ANOVA while also using
1:26:55SPMAN. And if there's a more fitting
1:26:58method for our paper, what should it be?
1:27:02Okay no? Ah medyo ano yun? Wait lang.
1:27:05Sabi natin uh you're going to test if
1:27:08nakakaapekto yung day and night if I'm
1:27:10not mistaken no kaso hindi ko siya
1:27:12makakausap right now kasi this one
1:27:15should have a given conversation para
1:27:17mas maintindihan natin iyung study.
1:27:18Again, it will be ano no depends on how
1:27:21you interpret the given research ngayon
1:27:24kasi hindi natin hindi siya nabigyan or
1:27:26hindi mo sa akin binigay kung ano yung
1:27:28nasa problems mo sa statement of the
1:27:30problems. Kasi doon tayo dumedepende.
1:27:32Kanina mapansin niyo is I'm going to
1:27:35give the aim or the research objective.
1:27:38Then afterwards I'm going to give the
1:27:41question behind that research objective.
1:27:45Saka ako nag-proceed with the treatment
1:27:46kasi that would be the process.
1:27:48Kailangan nakadepende tayo ano ba yung
1:27:50kinukuha mo sa data. Pero kung ang
1:27:52tinitignan natin, if I'm not if I'm
1:27:54going to look on what you have said, no,
1:27:57ah nagagamit ka ng significant
1:28:00difference, co-connect ah kukuhain mo
1:28:01rin yung correlation nila. Ah medyo
1:28:04marami na marami na yung focus ng
1:28:06research mo. But then it is possible
1:28:07naman. So ah medyo marami-rami lang yung
1:28:10mga kailangan mo na kuhain.
1:28:12Marami-raming interpretation ang
1:28:14kailangan and marami marami-rami ding
1:28:16conclusion. though uh justifiable naman
1:28:20'yung dami non'n kasi you are going to
1:28:22compare if I'm go if I'm not mistaken
1:28:24based on what ano no what what she said
1:28:27po no na kung saan um the given uh day
1:28:33and night ico-compare natin yung test
1:28:35course nila and kung nakakaapekto rin ba
1:28:38yung ano time span kung pagpataas ba ng
1:28:41pataas iyung time natin no we can have
1:28:44that one no depend still it depends on
1:28:46the given problems You need to check
1:28:48again your problems. Okay? And if your
1:28:51problems requires relationship, you need
1:28:53to use correlational. And if you if your
1:28:56problems requires uh comparison between
1:29:00the day and night, so you need to use
1:29:03the
1:29:05uh the other one aside from correlation
1:29:08which is in terms of comparison naman.
1:29:10'Yan po. Thank you.
1:29:18Alri, next one is from Verhel Rotoni.
1:29:22What is the preferred formula in getting
1:29:25the relationship between the two
1:29:27variables?
1:29:30Formula. Ito yung ano no ah
1:29:33misinformation ng mga bata in terms of
1:29:35research. Usually lagi ito ang hinihingi
1:29:37nila. Sir, ano po ba yung formula na
1:29:39dapat nilalagay natin sa research? And
1:29:41if I'm if I'm the one to ask that no a
1:29:45researcher as well, I am not about to
1:29:47put the formula itself. What do I put on
1:29:49the given research is saan siya
1:29:51gagamitin yung pinaka-treatment na yon?
1:29:54Bakit yun yung pinili niyo? Ano yung
1:29:56connection niya sa research? You're just
1:29:57going to explain those things. That's
1:29:59why hindi talaga siya kailangan
1:30:00formulated or hindi natin kailangan
1:30:03ilagay siya kasi hindi na talaga siya
1:30:04necessarily at all, no? Because we have
1:30:07a lots of uh applications that we use in
1:30:11terms of finding the those different
1:30:14treatments or in terms of using those
1:30:16different treatments. there is
1:30:19applications na, huwag na natin
1:30:20masyadong pahirapan iyung sarili natin
1:30:22on that part. Okay? We are not taking
1:30:24anym statistics at all. If we are taking
1:30:26any statistics subjects, it is better na
1:30:31kailangan talaga natin to identify those
1:30:33formulas. But then if we are dealing
1:30:34with researches, ang pinakaimportante
1:30:36doon is alam mo kung saan mo gagamitin
1:30:39yyung treatment and alam mo kung paano
1:30:41mo siya i-interpret. That's all po.
1:30:47Thank you, Sir Jerem. And last question
1:30:49for this um session from Justin Gomez.
1:30:53Can a study be both descriptive and
1:30:56inferential? Is it possible to use both
1:30:59descriptive and inferential statistics
1:31:02in the study?
1:31:03Okay. So kagaya ng sinabi ni Sir Gab no
1:31:06nung umaga ah hindi nawawala
1:31:10hindi nawawala ang descriptive kay
1:31:12inferential. Kagaya nung example ko ng
1:31:14pinakaumpisa is magi-start ka lagi
1:31:17talaga sa descriptive kasi siya 'yung
1:31:18nagbibigay ng data mo eh. For example,
1:31:22if you're going to correlate, kung ang
1:31:24tinitignan mo lang is more on
1:31:26inferential and you want to correlate
1:31:27your scores in math and signs, sinabi mo
1:31:30tumataas yyung scores ni math, bumababa
1:31:33yyung scores ni sign. For just for
1:31:34example, no, kung yun lang yung
1:31:36tinitignan mo, hindi siya enough. Bakit?
1:31:38Wala tayong descriptive data. Okay? Si
1:31:41descriptive data binibigay niya yung
1:31:43mean. For example, tinitingnan natin
1:31:45ilan yung naging score ng mga bata or
1:31:47what is the average score ng mga bata sa
1:31:49math. What is the average score ng mga
1:31:51bata sa signs? And by that and as you
1:31:55always as you check the given formulas
1:31:58kung kung titingnan natin yung formulas
1:32:00no, laging kasama si mean or other
1:32:02central tendency sa pag-compute ng given
1:32:05treatment, no? So kaya kailangan
1:32:07kukuhain mo talaga yung descriptive
1:32:09data. And of course, syempre kung kinuha
1:32:11mo na yung descriptive data, you need to
1:32:13give your analysations or you need to
1:32:15give your interpretation about the data.
1:32:17Simply co-compare mo lang naman mas
1:32:20mataas ba yung average score sa science,
1:32:22mas mataas ba yung average score sa sa
1:32:24math and so on and so forth. So yun po.
1:32:27Thank you.
1:32:30All
1:32:30right. Thank you, sir Jerome. I think we
1:32:32have covered everything like all the
1:32:35queries that we have for this session.
1:32:37So I'll be calling Sir Dustin for us to
1:32:40proceed with the program property. Thank
1:32:42you sir.
1:32:44[Musika]
1:32:553 2 1
1:32:59Enlightening. We are truly grateful for
1:33:01your invalable contribution for this
1:33:03webinar, Sir Ramos. As such we would
1:33:05like to present our token appreciation
1:33:09the certificate.
1:33:15Please allow me to read the content.
1:33:18This certificate is awarded to Jerome D.
1:33:21Ramos for imparting his valuable
1:33:24insights in inferential statistics
1:33:26during this webinar. quantitative data
1:33:29analysis understanding relevance of
1:33:31research techniques given this 28 day of
1:33:34January 2022 at Unida Christian Colleges
1:33:38IMU City signed by Joselyn Dimaala
1:33:41School Principal and Bishop Edgardo
1:33:44Marquez School Administrator
1:33:51[Musika]
1:33:56as we are taking pictures. I would like
1:33:59to inform the audience that please do
1:34:01not leave the webinar yet. We are about
1:34:04to release the evaluation form and
1:34:06release the webinar certificates.
1:34:11Now to properly wrap things up,
1:34:17we would like to invite Sir John Arvin
1:34:20Glow, research department specialist to
1:34:22deliver his closing remarks.
1:34:25[Musika]
1:34:28Good afternoon researchers. It is a
1:34:30pleasure to be with you all today. I
1:34:33commend the presentations and active
1:34:35discussions of our speakers, Mr. Pranka
1:34:39and Mr. Ramos. Therefore, I can conclude
1:34:42that the purpose of the webinar has been
1:34:44completely accomplished.
1:34:48As they say, statistics is a scientific
1:34:51investigation which collects data that
1:34:54turns into investigation and information
1:34:58into insight.
1:34:59This was further strengthened by Marcus
1:35:02Aurelius and I quote,
1:35:06"Nothing has such power to broaden the
1:35:09mind as the ability to investigate
1:35:12systematically and truly all that comes
1:35:15under die, observation and life.
1:35:18I hope that what you have learned
1:35:20through the webinar will help you a lot
1:35:22especially in conducting your research
1:35:24studies.
1:35:25Finally on behalf of the research
1:35:28department I would like toess our
1:35:31gratitude to all the speakers comm and
1:35:35of course our participants for being
1:35:40their schedule
1:35:42I would like toose myally
1:35:45the end of the wein session in uncty we
1:35:51are staying healthy and God bless
1:35:54everyone.
1:36:09Can we please have Miss Christine MZ,
1:36:11faculty from the research department to
1:36:14lead us with the closing prayer.
1:36:16Let's pray. Heavenly Father, we thank
1:36:18you for the success of this webinar. We
1:36:21know that you have blessed us with this
1:36:22success and we are very grateful for the
1:36:26knowledge and wisdom that you have
1:36:28provided to our um speakers that they
1:36:31were able to share with us valuable
1:36:33insights and information that we can
1:36:35really use in our doings. And also we
1:36:39thank you for the time and opportunity
1:36:41to really help not only our students but
1:36:43so as our teachers, faculties and other
1:36:47individuals that are here with us today
1:36:49virtually. uh we ask that you still
1:36:51guide us as we go and that we were able
1:36:54to apply this knowledge in our future
1:36:57endeavors. Grant us we do this fully
1:36:59aware that um our learnings are not only
1:37:02for ourselves but for the service of
1:37:04other people as well. Lord we ask for
1:37:08your guidance and um may you help us
1:37:11realize that our plans and actions are
1:37:14not only for ourselves but for your
1:37:16greater glory. We thank you for
1:37:18everything we pray in Jesus name.

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